Patent Publication Number: US-10779476-B2

Title: Crop management method and apparatus with autonomous vehicles

Description:
RELATED APPLICATION 
     This application is a Continuation-In-Part application of U.S. patent application Ser. No. 16/128,309, entitled “Vine Growing Management Method and Apparatus With Autonomous Vehicles”, filed on Sep. 11, 2018, and claims priority to U.S. application Ser. No. 16/128,309, which Specification is hereby fully incorporated by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates to the field of agriculture. More particularly, the present disclosure relates to method and apparatus for managing growing of crops, e.g., grain, fruit, vegetable, vines of a plurality of varietals, with the assistance of autonomous vehicles. 
     BACKGROUND 
     The background description provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section. 
     Agriculture is a major industry in the United States, which is a net exporter of food. As of the 2007 census of agriculture, there were 2.2 million farms, covering an area of 922 million acres (3,730,000 km 2 ), an average of 418 acres (169 hectares) per farm. Major crops include corn, soybeans, wheat, cotton, tomatoes, potatoes, grapes, oranges, rice, apples, sorghum, lettuce, sugar beets, and so forth. 
     Agriculture, food, and related industries contributed $1.053 trillion to U.S. gross domestic product (GDP) in 2017, a 5.4-percent share. The output of America&#39;s farms contributed $132.8 billion of this sum—about 1 percent of GDP. The overall contribution of the agriculture sector to GDP is larger than this because sectors related to agriculture—forestry, fishing, and related activities; food, beverages, and tobacco products; textiles, apparel, and leather products; food and beverage stores; and food service, eating and drinking places—rely on agricultural inputs in order to contribute added value to the economy. 
     However, over the years, farming has become an increasingly difficult business. Today, it is estimated that the American farmer receives just 16-cents for every dollar spent on food by the consumer. That is down 50 percent from 1980 when the farmers were receiving 31-cents for every dollar spent. Margins, especially on smaller farms, are too thin to have room in their operating budgets to purchase new technology and equipment, invest in experimental agricultural practices or adapt to a new environmental and economic climate, and yet continuous innovation is needed to increase the yields. 
     For the fruit and vegetable segment, with the increasing interest among Americans in healthy living, there has been a steady increase in demand for fresh fruits and vegetables, including organic fruits and vegetables. The U.S. fruit and vegetable market was valued at USD 104.7 billion in 2016. Vegetables and fruits are presently reigning as the U.S. top snacking items. Like farming in general, owning and operating an orchard or vegetable farm is a tough business. Huge amount of investment in the U.S. is expected in terms of technology to improve the yield and quality of the products, and their efficient transport. 
     For the wine industry, consumption in America has steadily increased in the last two decades, growing from about 500 million gallons in the year 1996 to about 949 million gallons in 2016 1 . The value of the total U.S. Wine Market for the year 2017 is estimated to be $62.7 billion, of which, $41.8 billion are domestically produced 2 . Currently, for the year 2018, the number of wineries in U.S. is estimated to be about 9,654 3 . The total vine growing acres in the U.S. was estimated to exceed 1,000,000 acres, as far back as 2012 4    1  Source: Wine Institute, DOC, BW166/Gomberg, Fredrikson &amp; Associates estimates. Preliminary History revised. 2  Source: Wines &amp; Vines, 2018, BW166, 2018. 3  Source: Statisa—The Statistics Portal. 4  The world&#39;s grape growing (vineyard) surface area 2000-2012 by Per Karlsson, Jun. 6, 2013, Winemaking &amp; Viticulture. 
     Owning and operating a vineyard is a tough business. “To take on the challenge of running a winery, you need to be determined, fearless, and passionate about your craft—although owning a vineyard seems romantic, the wine-making business is a tough one.” 5  In addition to the upfront financial investments required for the land and the infrastructure (like building, bottling and cellar equipment, trucks, and so forth), there are multitude of potential problems that could arise with growing vines. Examples of these problems may include, but are not limited to, over or under irrigation, diseases (such as mildews and black rots), or pests (such as berry moth, Japanese beetles, and rose chafers). Further, these problems may vary from one varietal to another. And a vineyard typically grows vines of multiple varietals. It is not uncommon for a vineyard to span over 100 acres, with over 1000 vines planted per acre. And multiple varietals are planted in different sections of the vineyard.  5  The Economics of Running a Winery, Aug. 20, 2018, Caroline Goldstein. 
     For the craft beer industry, consumption in America has also steadily increased in the recent years, 2018 saw 7,346 operating U.S. craft breweries in 2018—4,521 microbreweries, 2,594 brewpubs, 231 regional breweries. Craft brewers produced 25.9 million barrels of beer. Retail dollar value for craft beer sold in 2018 was $27.6 billion. Resultantly, there has been significant increase in interest in increasing the efficient growing and production of hops. 
     Similarly, with the enactment of the 2018 Farm Bill on Dec. 20, 2018, removing hemp from schedule I of the Controlled Substances Act, making hemp no longer a controlled substance, and with increasing number States legalizing the medical and recreational use of marijuana, likewise, there has been significant in increase in interest in increasing the efficient growing and production of  cannabis.    
     Thus, methods and apparatuses that can improve the management of growing crops of various types are desired. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings. To facilitate this description, like reference numerals designate like structural elements. Embodiments are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings. 
         FIG. 1  illustrates an overview of a system for managing growing crops of the present disclosure, in accordance with various embodiments. 
         FIGS. 2A-2C  illustrate taking of aerial images and terrestrial images of crops in the example vineyard of  FIG. 1 , with the assistance of autonomous vehicles, according to various embodiments. 
         FIG. 3  illustrates a process for managing growing crops with the assistance of autonomous vehicles, and components of the crop growing management system of  FIG. 1 , according to various embodiments. 
         FIGS. 4A-4   b  illustrate an example visual report to assist in managing growing crops, and an example user interface for interactively viewing the visual report, according to various embodiments. 
         FIG. 5  illustrates an example process for machine processing the aerial images taken with the assistance of autonomous vehicles, according to various embodiments. 
         FIG. 6  illustrates an example process for machine analyzing the aerial and/or terrestrial images, according to various embodiments. 
         FIG. 7  illustrates an example process for machine generating the visual report to assist in managing growing vines of a number of varietals, according to various embodiments. 
         FIG. 8  illustrates an example process for machine facilitating interactive viewing of the visual report, according to various embodiments. 
         FIG. 9  illustrates an example neural network suitable for use with present disclosure to analyze aerial and/or terrestrial images for crop growing anomalies, according to various embodiments; 
         FIG. 10  illustrates an example software component view of the crop growing management system of  FIG. 1 , according to various embodiments. 
         FIG. 11  illustrates an example hardware component view of the hardware platform for the crop growing management system of  FIG. 1 , according to various embodiments. 
         FIG. 12  illustrates a storage medium having instructions for practicing methods described with references to  FIGS. 1-9 , according to various embodiments. 
         FIGS. 13 a -13 d    illustrate a number of example seasonal NDVI profiles for a number of example crops grown in a number of sections of a number of example crop growing farms. 
         FIG. 14  illustrates a number of example mean seasonal NDVI profiles for a number of example crops grown in a number of sites of an example geographical region. 
     
    
    
     DETAILED DESCRIPTION 
     To address challenges discussed in the background section, apparatuses, methods and storage medium associated with managing growing crops, such as vines in a vineyard, are disclosed herein. In some embodiments, a method for managing growing crops includes operating one or more unmanned aerial vehicles (UAV) to fly over a plurality of sections of a crop growing farm, such as a vineyard. The UAVs are fitted with a plurality of cameras equipped to generate images in a plurality of spectrums. The plurality of sections of the crop growing farm may grow crops of various types, e.g., a vegetable farm may grow various vegetables, an orchard may grow various fruits, a vineyard may grow vines of a plurality of varietals, and so forth. The method further includes taking a plurality of aerial images of the sections of the crop growing farm in the plurality of spectrums, using the plurality of cameras, while the UAVs are flying over the plurality of sections of the crop growing farm; storing the plurality of aerial images of a plurality of spectrums of the crops of various types being grown, e.g., the vines of the plurality of varietals being grown, in a computer readable storage medium (CRSM), and executing an analyzer on a computing system to machine analyze the plurality of aerial images for anomalies associated with growing the crops of various types, e.g., the vines of the plurality of varietals. The machine analysis takes into consideration topological information of the crop growing farm, e.g., the vineyard, as well as current planting information of the crop growing farm, e.g., the vineyard. 
     In some embodiments, the method includes operating one or more terrestrial robots to traverse the plurality of sections of the crop growing farm, such as a vineyard. The one or more terrestrial robots are fitted with one or more cameras equipped to generate images in visual spectrum. The method further includes taking a plurality of visual spectrum terrestrial images of the crops of various types, e.g., vines of a plurality of varietals, being grown in the plurality of sections of the crop growing farm, e.g., a vineyard, using the one or more cameras fitted on the one or more terrestrial robots, while the one or more terrestrial robots are traversing the plurality of sections of the crop growing farm, e.g., a vineyard; storing the plurality of visual spectrum terrestrial images of the vines of the plurality of varietals being grown, in the CRSM; and executing the analyzer on the computing system to machine analyze the plurality of visual spectrum terrestrial images for additional anomalies associated with growing the crops of various types, e.g., vines of a plurality of varietals, in the plurality of sections of the crop growing farm, e.g., a vineyard. The machine analysis of the visual spectrum terrestrial images takes into consideration phenology information of the various types of crops being grown, at different growing stages. 
     These and are other aspects of the methods and apparatuses for managing crop growing, in particular, managing growing vine in a vineyard will be further described with references to the Figures. In the following detailed description, reference is made to the accompanying drawings which form a part hereof wherein like numerals designate like parts throughout, and in which is shown by way of illustration embodiments that may be practiced. It is to be understood that other embodiments may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. Therefore, the following detailed description is not to be taken in a limiting sense, and the scope of embodiments is defined by the appended claims and their equivalents. 
     Various operations may be described as multiple discrete actions or operations in turn, in a manner that is most helpful in understanding the claimed subject matter. However, the order of description should not be construed as to imply that these operations are necessarily order dependent. In particular, these operations may not be performed in the order of presentation. Operations described may be performed in a different order than the described embodiment. Various additional operations may be performed and/or described operations may be omitted in additional embodiments. 
     For the purposes of the present disclosure, the phrase “A and/or B” means (A), (B), or (A and B). For the purposes of the present disclosure, the phrase “A, B, and/or C” means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B and C). The description may use the phrases “in an embodiment,” or “In some embodiments,” which may each refer to one or more of the same or different embodiments. Furthermore, the terms “comprising,” “including,” “having,” and the like, as used with respect to embodiments of the present disclosure, are synonymous. 
     As used herein, the term “module” may refer to, be part of, or include an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and/or memory (shared, dedicated, or group) that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. 
     Referring now to  FIG. 1 , wherein an overview of a system for managing growing crops of the present disclosure, in accordance with various embodiments, is illustrated. For ease of understanding, system  100  will be described with reference to managing growing vine of a plurality of varietals in a vineyard, however, the disclosure is not so limited. System  100  may be employed to manage growing crops of various types in various crop growing farms. Examples of crops growing that may be managed include, but are not limited to, growing of Hops, Kiwis, Apples, Berries, Cherries, Citrus Fruit, Avocado, Pears, Plums, Hemp,  Cannabis , and so forth. Examples of crop growing farms include, but are not limited, vegetable farms, orchards, vineyards, and so forth. In the case of growing vine, examples of varietals may include, but are not limited to, cabernet sauvignon, pinot noir, chardonnay, pinot gris, and so forth. In the case of growing  cannabis , example of varietals may include  cannabis  indica or  Cannabis sativa  species. 
     As shown, system  100  for managing growing crops in a crop growing farm  102 , e.g., growing vines in a vineyard, may include one or more UAVs  104 , and crop growing management system  110  having crop growing management software  120 . In some embodiments, system  100  may further include one or more terrestrial robots  108 . Collectively, one or more UAVs  104  are fitted with a plurality of cameras to capture aerial images in a plurality of spectrums. The UAVs  104  are operated to fly over crop growing farm, e.g., a vineyard,  102  and capture aerial images of various sections of crop growing farm, e.g., a vineyard,  102  in the plurality of spectrums. Different crop types, e.g., fruits or vegetables of different types, fruits of different types,  cannabis  or vines of different varietals, may be grown in different sections of crop growing farm  102 . The plurality of spectrums may include, but are not limited to, visual, red, near infrared, near red, and so forth. 
     Crop growing management software  120  is configured to be executed on one or more computer processors of crop growing management system  110 , to machine process the aerial images taken, and machine analyze the aerial images for anomalies associated with growing crops of various types, e.g., over or under irrigation, and generate a visual report with indications of the anomalies. The machine analysis and reporting takes into consideration topological information of the crop growing farm, e.g., natural or man made geographical features, like a pond, a building and so forth, current planting information, e.g., the crop types or varietals being grown, and where in crop growing farm  102 . 
     For embodiments where system  100  also includes one or more terrestrial robots  106 , each of the one or more terrestrial robots  106  is equipped with one or more cameras to capture terrestrial images in visual spectrum, in one or more directions. The one or more directions may include a left (up/straight ahead/down) outward looking direction, a right (up/straight ahead/down) outward looking direction, a forward (up/straight ahead/down) outward looking direction, and/or a backward (up/straight ahead/down) outward looking direction. The one or more terrestrial robots  106  are operated to traverse crop growing farm  102  and capture terrestrial images of the crops of various types or varietals being grown in the various sections of crop growing farm  102 , in the visual spectrum. The images of the crops may show various aspects of the crops, e.g., for vines, the images may show various aspects of the vines, e.g., its grapes, its leaves, its roots, and so forth. 
     Crop growing management software  120  is further configured to be executed on the one or more computer processors of crop growing management system  110 , to machine process the terrestrial images taken, and machine analyze the terrestrial images of the crops for further anomalies associated with growing crops of a particular type or varietal, e.g., plant diseases and/or pest infestations, and generate a visual report with indications of the anomalies. The machine analysis and reporting additional takes into consideration phenology information of the crop types or varietals, at different growing stages. 
     In some embodiments, a plurality of UAVs  104  are employed. Each UAV  104  is either successively or concurrently fitted with three (3) cameras for capturing aerial images in 3 spectrums, visual spectrum, red or near infrared spectrum, and near red spectrum. In some embodiments, the 3 cameras include a red/green/blue (RGB) camera configured to capture and generate images in visual spectrum, a Normalized Difference Vegetation Index (NDVI) camera configured to capture and generate images in red or near infrared spectrum, and a Normalized Difference Red Edge (NDRE) camera equipped to capture and generate images in near red spectrum. In some embodiments, one or more UAVs  104  may be fitted with one or more infrared thermal cameras to capture aerial thermal images of the various sections of vineyard  102 . The plurality of UAVs  104  are operated to systematically fly over all sections of vineyard  102 , or selectively fly over selected sections of vineyard  102 , capturing and generating aerial images of the all or selected sections of vineyard  102 . In some embodiments, the cameras has resolution and zoom in power to allow each pixel of each aerial image to cover approximately 3-4 cm 2  of a sectional area, with the UAVs  104  operated at 400 ft or below. In alternate embodiments, as camera resolution further improves, each pixel of each aerial image may cover an area as small as 1 cm 2 . 
     In some embodiments, a swarm of lightweight UAVs with less Federal Aviation Administration (FAA) operation restrictions, such as Dragonfly Drone with less height operational restrictions, are employed. A Dragonfly Drone is a drone weighing typically less than 1 lb, which is available in multiple form factors but less than 9″ in diameter. The Dragonfly Drone may be incorporated with extremely high resolution near infrared (NIR), short wave infrared (SWIR), thermal, and/or RGB cameras, amongst a large variety of others. The Dragonfly drones are used together in coordinated swarms mapping together to rapidly take imagery and collect data from the air over large crop growing farms. The small form factor eliminates the current need to acquire an FAA license to fly the drones. The Dragonfly drones may also contain a magnetic content for magnetic charging, and can return to a WIFI enabled base station for recharging, uploading of imagery, and downloading of flight plan. 
     The concurrent employment of the near red spectrum images of the NDRE cameras provides certain information that complement the limitations of the red or infrared images of the NDVI cameras. The index of NDVI images is derived from reflectance values in red and near-infrared bands of the electromagnetic spectrum. Index values ranging from −1 to 1 indicate the instantaneous rate of photosynthesis of the crop of interest. NDVI is commonly thought of as an index of biomass, but a normal NDVI curve will decline towards the end of the growing season even when the amount of biomass is at peak levels. Therefore, NDVI can only be considered an index of photosynthetic rate, not the amount of foliage. Also, NDVI is not sensitive to crops with high leaf area index (LAI) and tends to saturate at high LAI values. 
     On the other hand, NDRE uses the red-edge portion of the spectrum. Since red-edge light is not absorbed as strongly as red-light during photosynthesis, it can penetrate deeper into the crop canopy and thereby solves the issue of NDVI saturating at high LAI values. NDRE is also more sensitive to medium-high chlorophyll levels and can be used to map variability in fertilizer requirements or foliar nitrogen demand. Leaf chlorophyll and nitrogen levels do not necessarily correlate with soil nitrogen availability and should be ground-truthed with soil or tissue samples. 
     In some embodiments, a plurality of terrestrial robots  106  are employed. Each terrestrial robot  106  is fitted with at least two (2) cameras to capture and generate terrestrial images of the crops of different types or varietals, in visual spectrum, in at least 2 directions along the routes traversed by the terrestrial robots  106 . Similarly, the plurality of terrestrial robots  106  are operated to systematically traverse the entire crop growing farm  102  or selectively traverse selected sections of crop growing farm  102 , capturing and generating terrestrial images of all crops of all types or varietals or selected crops of selected types or varietals grown in all or selected sections of crop growing farm  102 . 
     In some embodiments, each UAV  104 /robot  106  includes computer readable storage medium (CRSM)  124 / 126  to temporarily store the aerial/terrestrial images captured/generated. In some embodiments, the CRSM are removable, such as a universal serial bus (USB) drive, allowing the captured/generated aerial/terrestrial images to be removably transferred to CRSM of crop growing management system  110 , via a compatible input/output (I/O) port on crop growing management system  110 . In some embodiments, each UAV  104 /robot  106  may include an I/O port (not shown), e.g., a USB port, to allow the stored aerial/terrestrial images to be accessed and transferred to crop growing management system  110 . In still other embodiments, each UAV  104 /robot  106  may include a communication interface, e.g., WiFi or Cellular interface, to allow the stored aerial/terrestrial images to be wirelessly transferred to crop growing management system  110 , via e.g., access point/base station  118 . Access point/base station  118  may be communicatively coupled with crop growing management system  110  via one or more private or public networks  114 , including e.g., the Internet. 
     Except for the cameras fitted to UAVs  104 , and the manner UAVs  104  and robots  106  are employed, UAVs  104  and robots  106  may otherwise be any one of a number of such vehicles/devices known in the art. For example, except for the cameras fitted to UAVs  104 , UAVs  104  may be a fixed wing UAV, a tricopter, a qudcopter, a hexcopter or a lightweight UAV. In particular, as discussed earlier, UAVs  104  may be lightweight UAVs that may operate above 400 ft, Similarly, terrestrial robots  106  may be wheeled, threaded or screw propelled. 
     Still referring to  FIG. 1 , in some embodiments, system  100  may further include various sensors  108  dispositioned at various locations in crop growing farm  102  to collect and generate various local environmental data. Example of sensors  108  may include, but are not limited to, temperature sensors for sensing local temperature, humidity sensors for sensing local humidity level, moisture sensors for sensing moisture level in the soil, and so forth. In various embodiments, sensors  108  may be equipped to provide the collected sensor data to crop growing management system  110  wirelessly, via access points/base station  118 . In some embodiments, some of these sensors may be dispositioned in UAVs  104  and/or terrestrial robots  106  to collect and generate various local environmental data as UAVs  104  and/or terrestrial robots  106  fly over or traverse crop growing farm  102 . For these embodiments where sensors  108  are employed, crop growing management software  120  further takes these local environmental data into consideration, when machine processing aerial images (and optionally, terrestrial images) to machine detect anomalies associated with growing crops of various types or varietals. In some embodiments, sensors  108  may include imaging devices or cameras mounted at various fixtures of the crop growing farm, e.g., utility poles, or nettings covering the crops being grown, to capture overhead images of the corresponding sections of the crop growing farm (to complement or supplement the aerial images captured with the UAVs). Examples of netting coverings include netting typically used in farms to reduce wind, sun and bird effects on growing plants. The netting may be incorporated with extremely high resolution near infrared (NIR), short wave infrared (SWIR), thermal, and/or RGB cameras, amongst a large variety of others. Multiple cameras can be attached to the underside of nets, providing both real time and video imagery of plant growth. 
     In some embodiments, system  100  may further include remote servers  112  having repositories of environmental data applicable to crop growing farm  102 , e.g., weather or environmental data services, with temperature, precipitation, air pollution data for the geographical region/area where crop growing farm  102  is located. Crop growing management system  110  may be communicatively coupled with remote servers  112  via one or more private or public networks  114 , including e.g., the Internet. For these embodiments, crop growing management software  120  further takes these regional environmental data into consideration, when machine processing and analyzing aerial images (and optionally, overhead and/or terrestrial images) to machine detect anomalies associated with growing crops of various types or varietals. 
     In some embodiments, crop growing management software  120  is further configured to support interactive viewing of the visual report by a user, via a user computing device  116 . User computing device  116  may be coupled with crop growing management system  110  via one or more public and/or private networks  114 , and/or access point/base station  118 . In some embodiments, crop growing management software  120  may facilitate interactive viewing of the visual report, via web services. For these embodiments, user computing device  116  may access and interact with the visual report via a browser on user computing device  116 . In some embodiments, crop growing management software  120  may provide an agent e.g., an app, to be installed on user computing device  116  to access and interact with the visual report. Except for its use to access and interact with the visual report, user computing device  116  may be any one of a number of computing devices known in the art. Examples of such computing devices may include, but are not limited to, desktop computers, mobile phones, laptop computers, computing tablets, and so forth. 
     Similarly, except for their use to facilitate provision of aerial/overhead/terrestrial images and/or environment data, as well as accessing the visual reports, access point/base stations  118  and network(s)  114  may be any one of a number access points/base stations and/or networks known in the art. Networks  114  may include one or more private and/or public networks, such as the Internet, local area or wide area, having any number of gateways, routers, switches, and so forth. 
     Before further describing elements of system  100 , it should be noted while for ease of understanding, UAVs  104 , terrestrial robots  106 , and crop growing management system  110  have been described so far, and will continue to be mainly described as assisting in management of growing crops of a plurality of types or varietals in a crop growing farm, the present disclosure is not so limited. System  100  may be practiced with UAVs  104 , terrestrial robots  106 , and crop growing management system  110  configured to service multiple crop growing farms growing crops of multiple types or varietals. 
     Referring now to  FIGS. 2A-2C , wherein taking of aerial/overhead images of a crop growing farm, an example vineyard, and terrestrial images of crops being grown, vines growing in the example vineyard with the assistance of autonomous vehicles, according to various embodiments, are illustrated. Shown in  FIG. 2A  is a composite aerial overhead image  202  of a crop growing farm, an example vineyard,  102 , generated by combining or stitching together individual aerial overhead images  204  taken by one or more UAVs  104 , while the one or more UAVs  104  fly over the crop growing farm, example vineyard,  102 . As described earlier, in some embodiments, the one or more UAVs  104  are operated to systematically fly over all sections of the crop growing farm, example vineyard,  102  at a selected altitude. In various embodiments, each pixel of each aerial/overhead image covers an area of approximately 3-4 cm 2  or smaller. Thus, the number of aerial/overhead images  204  shown in  FIG. 2A  as being combined together to cover the crop growing farm, example vineyard,  102  are significantly less than the number of aerial/overhead images  204  combined/stitched together in real life. The number is reduced for ease of illustration and understanding, and thus is not to be read as limiting on the present disclosure. 
     The different levels of grayness in  FIG. 2A  correspond to different colors in real life in the composite NDVI or NDRE images combined/stitched together based on the individual NDVI/NDRE images taken, which in turn correspond to different conditions of the soil and/or different conditions of the crops (depending in part, on the crop types or varietals being grown). 
     Shown in  FIGS. 2B-2C  are example terrestrial images  220  and  230  of couple of the example vines of couple varietals being grown in couple of the sections in example vineyard  102 , captured by the camera(s) of terrestrial robot(s)  106 , while terrestrial robot(s)  106  is (are) operated to traverse various sections of example vineyard  102 . As illustrated by the example picture  220  in  FIG. 2B , disease (in this case, downy mildew), may be machine detected, using the phenology information provide, for various growing stages of the vine varietal. As illustrated by the example picture  230  in  FIG. 2C , infestation (in this case, sweetpotato weevil), may be machine detected, using the phenology information provide, for various growing stages of the vine varietal.  FIGS. 2A and 2B  are non-limiting examples. As those skilled in various crop growing arts would appreciate, there are many possible vine diseases and/or infestations that need to be managed for the various crops of various types/varietals. 
     Referring now to  FIG. 3 , wherein an example process for managing growing crops with the assistance of autonomous vehicles, and components of an example crop growing management system of  FIG. 1 , according to various embodiments, are illustrated. As shown, for the illustrated embodiments, example crop growing management system  350  includes crop growing management software  320  and CRSM  320 . Crop growing management system  350  may correspond to crop growing management system  110  of  FIG. 1 , and crop growing management software  320  may correspond to crop growing management software of  120  of  FIG. 1 . 
     Crop growing management software  320  includes image processor  322 , analyzer  324 , reporter  326  and interactive report reader  328 . Image processor  322  is configured to process and combine/stitch together individual aerial/overhead images taken in various spectrums to form a plurality of composite aerial/overhead images  332  of the crop growing farm, e.g., a vineyard, in the various spectrums. Analyzer  324  is configured to machine process and analyze aerial/overhead images  302  and/or terrestrial images  304  to identify anomalies with growing crops of various types, e.g., vine of the various varietals in the various sections of the crop growing farm, e.g., the vineyard. In some embodiments, analyzer  324  is configured to apply artificial intelligence, e.g., neural networks, to identify anomalies with growing crops of various types, e.g., vine of the various varietals, in the various sections of the crop growing farm, e.g., the vineyard. Reporter  326  is configured to machine generate one or more visual reports  336  of the crop growing farm, e.g., the vineyard, identifying/highlighting the anomalies associated with growing crops detected. In some embodiments, visual reports  336  may be two dimensional (2D) reports. In other embodiments, visual reports  336  may be three dimensional (3D) reports or halograms. Interactive report reader  328  is configured to facilitate a user in interactively viewing the visual report. 
     CRSM  330  is configured to store individual aerial/overhead images  302  of various sections of the crop growing farm, e.g., a vineyard, captured in various spectrums, as well as individual terrestrial images  304  of various crops of various types, e.g., vines of various varietal, captured in the visual spectrum. CRSM  330  is also configured to store topological information  306  of the crop growing farm, such as pond, creeks, streams and so forth, as well as current planting information  308 , i.e. crop types/varietals planted, and where. CRSM  330  may also be configured to store phenology information  310  of the crop types/varietals, at different growing stages, and environmental data  312  collected by local sensors and/or received from remote servers. CRSM  330  may also be configured to store composite aerial/overhead images  332  generated from individual aerial/overhead images  302 , analysis results  334  and reports  336 . 
     In various embodiments, phenology information  310  of the crop types/varietals may include various profiles of the various crops types/varietals over a growing season for different growing sections of a crop growing farm.  FIGS. 13 a -13 d    illustrate various example profiles of various example crops types/varietals over example growing seasons for different growing sections of a number of example crop growing farms. Specifically,  FIG. 13 a    illustrates example NDVI profiles  1300  for various example apples (of the same or different types/varietals) over an example growing season from end of March to end of October for various sections of an example apple orchard. Each line represents an example NDVI profile for the example apples grown in a section of the apple orchard. The vertical axis depicts the mean NDVI values, while the horizontal axis depicts the dates in the growing season. The alphanumeric identifiers of the profiles, shown in the bottom of the Figure, identify the sections of the example apple orchard. 
       FIG. 13 b    illustrates example NDVI profiles  1310  for various example blueberries (of the same or different types/varietals) over an example growing season from end of March to end of October for various sections of an example blueberry orchard. Each line represents an example NDVI profile for the example blueberries grown in a section of the blueberry orchard. The vertical axis depicts the mean NDVI values, while the horizontal axis depicts the dates in the growing season. The alphanumeric identifiers of the profiles, shown in the bottom of the Figure, identify the sections of the example blueberry orchard. 
       FIGS. 13 c  and 13 d    respectively illustrates example NDVI profiles  1320  and  1330  for various example low productivity and high productivity hops (of the same or different types/varietals) over an example growing season from end of March to end of October for various sections of couple of example hop growing farms. Each line represents an example mean NDVI profile for the example hops grown in a section of an example hop farm. The vertical axis depicts the mean NDVI values, while the horizontal axis depicts the dates in the growing season. The alphanumeric identifiers of the profiles, shown in the bottom of the Figures, identify the sections of the example hop farms. 
       FIG. 14  illustrates a number of example mean seasonal NDVI profiles  1400  over an example growing season from mid-October to mid-March for a number of example crops grown in a number of sites of an example geographical region. The vertical axis depicts the mean NDVI values, while the horizontal axis depicts the dates in the growing season. The example crops include pears, kiwis, cherries, avocados and mandarin oranges. 
     In other embodiments, the profiles may span different growing seasons, as well as other profiles, such as but not limited to NDRE profiles, may be used instead or additionally. 
     In various embodiments, except for their usage CSRM  330  may be any one of CRSM known in the art including, but are not limited to, non-volatile or persistent memory, magnetic or solid state disk drives, compact-disk read-only memory (CD-ROM), magnetic tape drives, and so forth. 
     Still referring to  FIG. 3 , process  300  for managing growing of crops, e.g., vines, include operations performed at stages A-E. Starting at stage A, the UAVs fitted with a plurality of cameras equipped to capture/generate aerial images  302  in a plurality of spectrums are operated, in succession or concurrently, to fly over all or selected sections of the vineyard. And individual aerial images  302  of the various sections of the vineyard are taken as the UAVs fly over the sections at a selected altitude. As described, in some embodiments, the aerial images are taken at high resolution covering a small area of 3-4 cm 2  per pixel or smaller. On capture/generation, individual aerial images  302  of the various sections of the vineyard are stored into CRSM  330 . Optionally, overhead images of selected sections of the crop growing farm may be taken with imaging devices/cameras mounted on nettings and/or fixtures in the sections. 
     At stage A, the one or more terrestrial robots fitted with one or more cameras equipped to capture/generate terrestrial images  304  in visual spectrum may also be optionally operated, in succession or concurrently, to traverse all or selected sections of the crop growing farm, e.g., a vineyard. And individual terrestrial images  304  of various crops of various types or varietals being grown in the various sections of the crop growing farm are taken as the terrestrial robots traverse over the sections. On capture/generation, individual terrestrial images  304  of various crops of various types or varietals being grown in the various sections of the crop growing farm are stored into CRSM  330 . 
     From stage A, process  300  may proceed to stages B and C in parallel. At stage B, image processor  322  may be executed on a computer system to machine process and combine/stitch together individual aerial/overhead images taken in various spectrums (take by the UAVs and/or stationary mounted cameras) to form a plurality of composite aerial/overhead images  332  of the crop growing farm, in the various spectrums. On generation, composite aerial/overhead images  332  of the crop growing farm, in the various spectrums, are stored into CRSM  330 . 
     At stage C, analyzer  324  may be executed on the computer system to machine process individual aerial/overhead images  302  and/or individual terrestrial images  304  to machine analyze and detect anomalies associated with growing crops of the various types or varietals, taking into consideration topological information of the crop growing farm, and current planting information of the crop growing farm. Anomalies may include, but are not limited, whether a section is under irrigated or over irrigated. By taking into consideration of the topological information, the analyzer may avoid false identification e.g., a pond or a creek as over irrigated, or a section growing crops of particular types, e.g., vines of a particular varietal, being stressed as under irrigated. In embodiments where terrestrial images  304  are also being machine processed and analyzed to detect anomalies associated with growing crops of the various types or varietals, the analysis may take into consideration phenology information of the various crops, at different growing stages. Anomalies may include, but are not limited, various types of plant diseases and/or pest infestations. 
     In some embodiments, machine analysis of aerial/overhead images  302  as well as terrestrial images  304 , to detect anomalies associated with growing crops of various types or varietals may further take into consideration local environmental data  312  collected by local sensors disposed at various locations throughout the vineyard, and/or regional/areal environmental data  312  provided by one or more remote environmental data services, applicable to the crop growing farm. 
     From stages B and C, process  300  may proceed to stage D. At stage D, reporter  326  may be executed on a computer system to machine generate one or more visual reports  336  of the crop growing farm, with indications of the anomalies detected. The visual reports  336  are generated using composite aerial/overhead images  332 , and based at least in part on the results of the analysis  334 . As described earlier, visual reports  336  may be 2D, 3D or hologram. 
     Next, at stage E, on generation of visual reports  336 , interactive report reader  328  may be executed on a computing system to machine facilitate interactive viewing of visual reports  336 , by a user. 
     Still referring back to  FIG. 3 , in some embodiments, each of image processor  322 , analyzer  324 , reporter  326  and interactive report reader  328  may be implemented in hardware, software or combination thereof. Example hardware implementations may include by are not limited to application specific integrated circuit (ASIC) or programmable circuits (such as Field Programmable Gate Arrays (FPGA)) programmed with the operational logic. Software implementations may include implementations in instructions of instruction set architectures (ISA) supported by the target processors, or any one of a number of high level programming languages that can be compiled into instruction of the ISA of the target processors. In some embodiments, especially those embodiments where analyzer includes at least one neural network, at least a portion of analyzer  324  may be implemented in an accelerator. One example software architecture and an example hardware computing platform will be further described later with references to  FIGS. 10 and 11 . 
     Referring now to  FIGS. 4A-4   b , wherein an example visual report to assist in managing crop growing, and an example user interface for interactively viewing the visual report, according to various embodiments, are illustrated. As shown in  FIG. 4A  and described earlier, example visual report  400  includes a composite image  402  of the crop growing farm, e.g., a vineyard, formed by combining or stitching together the individual aerial images taken of the various sections of the crop growing farm, while the UAVs flew over the sections of the crop growing farm (and optionally, individual overhead images taken of the various sections of the crop growing farm by various stationary mounted imaging devices/cameras mounted on fixtures in the various sections). For the illustrated embodiments, the various sections of the crop growing farm may be annotated with various information  404  useful to the user, including but are not limited to, section identification information, crop types/varietals being grown, anomalies detected, and so forth. Example composite image  402  of an example vineyard, is formed by combining or stitching together the individual aerial/overhead NDRE images taken of the various sections of the example vineyard. 
     In various embodiments, visual report  400  is in colors. The different gray scale levels in the drawing correspond to different colors depicting various conditions as captured by the aerial/overhead images taken in a particular spectrum, e.g., NDVI, NDRE and so forth. 
     For the illustrated embodiments, visual report  400  may further include various legend  406  and auxiliary information  408  to assist a user in comprehending the information provided. For example, legend  406  may provide the quantitative scale of a condition metric, such as soil moisture level, corresponding to the different colors. Example auxiliary information  408  may include, but are not limited to, time the aerial/overhead images are taken, air temperature at the time, wind speed at the time, wind direction at the time, and other observed conditions at the time. 
       FIG. 4B  illustrates an example user interface for facilitating a user in interactively viewing the visual reports with a user/client computing device. As shown, for the embodiments, user interface  450  includes a number of tabs  452   a - 452   c , one each for viewing a particular visual report of aerial/overhead images taken in a particular spectrum. Each tab, e.g., tab  452   a , includes main display area  454  for displaying the visual report  462  annotated with highlights  464  of anomalies detected. Additional, each tab may further include section  456  for displaying various information, e.g., file information, and section  458  for displaying various command icons. In response to a selection of a command, or a selection of an anomaly highlighted, a pop up window  466  may be displayed to provide further information and/or facilitate further interaction. 
     Referring now to  FIG. 5 , wherein an example process for machine processing the aerial/overhead images, according to various embodiments, is illustrated. As shown, for the embodiments, example process  500  for machine processing/combining the aerial/overhead images to generate the composite aerial/overhead image include operations perform at blocks  502 - 512 . The operations may be performed by e.g., image processor  322  of  FIG. 3 . 
     Example process  500  starts at block  502 . At block  502 , a corner aerial/overhead image may be retrieved. For example, it may be the upper left corner image, the upper right corner image, the lower right corner image or the lower left corner image. Next at block  504 , the next image in the next column of the same row (or next row, same column) may be retrieved and combined/stitched with the previously processed images, depending on whether the aerial/overhead images are being combined on a column first basis (or a row first basis). 
     At block  506 , a determination is made if the last column has been reached, if the aerial/overhead images are being combed or stitched in a column first basis (or the last row has been reached, if the aerial/overhead images are being combed or stitched in a row first basis). If the last column of the row (or last row of the column) has not been reached, process  500  returns to block  504 , and continues therefrom as earlier described. If the last column of the row (or last row of the column) has been reached, process  500  proceeds to block  508 . 
     At block  508 , the next image in the first column of the next row, if the aerial/overhead images are being combed or stitched in a column first basis (or first row, next column, if the aerial/overhead images are being combed or stitched in a row first basis) may be retrieved and combined/stitched with the previously processed images. 
     At block  510 , a determination is made if the last row has been reached, if the aerial/overhead images are being combed or stitched in a column first basis (or the last column has been reached, if the aerial/overhead images are being combed or stitched in a row first basis). If the last row of the column (or last column of the row) has not been reached, process  500  proceeds to block  512 . 
     At block  512 , the next image in the next row, first column is retrieved, if the aerial/overhead images are being combed or stitched in a column first basis (or next column, first row, if the aerial/overhead images are being combed or stitched in a row first basis), and combined/stitched with the previously processed images. Thereafter, process  500  continues at block  504 , as earlier described. 
     Eventually, it is determined at block  510  that the last row of the column (or last column of the row) has been reached. At such time, process  500  proceeds to block  512 . 
     While the combining/stitching process to generate the composite aerial/overhead image has been described with an example process that starts at one of the 4 corners of a substantially rectangular sectional partition of the crop growing farm, it should be noted that the present disclosure is not so limited. The crop growing farm may be in any shape, and may be partitioned into sections in non-rectangular manner. The combining/stitching process may start with any aerial/overhead image, and radiates out to combine and stitch the next aerial/overhead image in any number of directions successively. 
     Referring now to  FIG. 6 , wherein an example process for machine analyzing the images, according to various embodiments, is illustrated. As shown, for the embodiments, example process for machine analyzing the images include operations at blocks  602 - 612 . The operations at blocks  602 - 612  may be machine performed by e.g., analyzer  324  of  FIG. 3 . 
     Process  600  starts at block  602 . At block  602 , an individual aerial/overhead image (and optionally, corresponding terrestrial images of the section) is (are) retrieved. Next, at block  604 , topological information of the crop growing farm and current planting information for the section are retrieved. From block  604 , process  600  may proceed to one of blocks  606 ,  608  or  610  depends on whether corresponding terrestrial images are also analyzed, and/or whether local/remote environmental data are considered. 
     If corresponding terrestrial images are also analyzed, process  600  proceeds to block  606 . At block  606 , phenology information of the crop types/varietals being grown, at different stages, are retrieved for the analysis. If environmental data are also being taken into consideration, process  600  also proceeds to block  608 . At block  608 , the local/remote environmental data are retrieved. 
     From block  604 ,  606  or  608 , process  600  eventually proceeds to block  610 . At block  610 , the aerial/overhead image, and optionally, corresponding terrestrial images, are analyzed for anomalies associated with growing vine of the various varietals. As described earlier, the analysis takes into consideration the topological information of the crop growing farm, and current planting information. The analysis may also optionally take into consideration phenology information, at different growing stages, and/or local/remote environmental data. 
     If anomalies are not detected, process  600  ends, otherwise the anomalies are noted, before ending process  600 . Process  600  may be repeated for each section or selected sections of the crop growing farm. 
     Referring now to  FIG. 7 , wherein an example process for machine generating a visual report, according to various embodiments, is illustrated. As shown, for the embodiments, processor  700  includes various operations performed at blocks  702 - 710 . The operations at blocks  702 - 710  may be performed by e.g., reporter  326  of  FIG. 3 . 
     Example process  700  starts at block  702 . At block  702 , one or more composite aerial/overhead images are outputted. Next at block  704 , an area of the crop growing farm, e.g., a vineyard, is selected, and at block  706 , the selected area is examined to determine whether anomalies associated with growing crops of various types, such as vine of various varietals, were detected. If a result of the determination indicates that anomalies associated with growing crops of various types or varietals were not detected, process  700  proceeds to block  710 , else process  700  proceeds to block  708 , before proceeding to block  710 . At block  708 , the visual report is annotated to highlight the anomaly detected. 
     At block  710 , a determination is made on whether there are additional areas of the crop growing farm to be analyzed. If a result of the determination indicates there are additional areas of the crop growing farm to be analyzed, process  700  returns to block  704 , and proceeds therefrom as earlier described. Otherwise, process  700  ends. 
     Referring now  FIG. 8 , wherein an example process for machine facilitating interactive viewing of a visual report, according to various embodiments, is illustrated. As shown, for the illustrated embodiments, process  800  for facilitating interactive viewing of the visual report comprises operations at blocks  802 - 806 . In some embodiments, operations at blocks  802 - 806  may be performed by e.g., interactive report reader  328  of  FIG. 3 . 
     Process  800  may start at block  802 . At block  802 , a user request may be received. The user request may be associated with displaying a new visual report, providing further information on a detected anomaly, providing remedial action suggestions for a detected anomaly, and so forth. Next at block  804 , the user request may be processed. At block  806 , the processing results, e.g., the requested visual report, further explanation of an anomaly of interest, a remedial action suggestion, and so forth, may be outputted/displayed for the user. The process results may optionally further include facilities for further interaction by the user. 
     Referring now to  FIG. 9 , wherein an example neural network, in accordance with various embodiments, is illustrated. As shown, example neural network  900  may be suitable for use e.g., by analyzer  324  of  FIG. 3 , in determining whether the images suggest anomalies associated with growing vines of the varietals. Example neural network  900  is a multilayer feedforward neural network (FNN) comprising an input layer  912 , one or more hidden layers  914  and an output layer  916 . Input layer  912  receives data of input variables (x)  902 . Hidden layer(s)  914  processes the inputs, and eventually, output layer  916  outputs the determinations or assessments (y)  904 . In one example implementation the input variables (x i )  902  of the neural network are set as a vector containing the relevant variable data, while the output determination or assessment (y)  904  of the neural network are also as a vector. 
     Multilayer feedforward neural network (FNN) may be expressed through the following equations: 
     
       
         
           
             
               
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     where ho i  and y i  are the hidden layer variables and the final outputs, respectively. f( ) is typically a non-linear function, such as the sigmoid function or rectified linear (ReLu) function that mimics the neurons of the human brain. R is the number of inputs. N is the size of the hidden layer, or the number of neurons. S is the number of the outputs. 
     The goal of the FNN is to minimize an error function E between the network outputs and the desired targets, by adapting the network variables iw, hw, hb, and ob, via training, as follows: 
     
       
         
           
             
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     where y kp  and t kp  are the predicted and the target values of pth output unit for sample k, respectively, and m is the number of samples. 
     In some embodiments, analyzer  324  of  FIG. 3  may include a pre-trained neural network  900  to determine whether an aerial/overhead and/or a terrestrial image suggests one or more anomalies associated with growing vines of the varietals. The input variables (x)  902  may include an aerial/overhead image of a section captured in a particular spectrum, one or more corresponding terrestrial images, topological data, current planting data, phenology data, and environmental data. The output variables (y i )  904  may include values indicating whether anomalies are detected and/or anomaly types. The network variables of the hidden layer(s) for the neural network of analyzer  324  for determining whether an aerial/overhead image of a section captured in a particular spectrum and/or corresponding terrestrial images suggest anomalies with the vines being grown, are determined by the training data. 
     In the example neural network of  FIG. 9 , for simplicity of illustration, there is only one hidden layer in the neural network. In some other embodiments, there can be many hidden layers. Furthermore, the neural network can be in some other types of topology, such as Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and so forth. 
     Referring now to  FIG. 10 , wherein a software component view of the crop growing management (CGM) system, according to various embodiments, is illustrated. As shown, for the embodiments, V\CGM system  1000 , which could be CGM system  110 , includes hardware  1002  and software  1010 . Software  1010  includes hypervisor  1012  hosting a number of virtual machines (VMs)  1022 - 1028 . Hypervisor  1012  is configured to host execution of VMs  1022 - 1028 . The VMs  1022 - 1028  include a service VM  1022  and a number of user VMs  1024 - 1028 . Service machine  1022  includes a service OS hosting execution of a number of system services and utilities. User VMs  1024 - 1028  may include a first user VM  1024  having a first user OS hosting execution of image processing  1034 , a second user VM  1026  having a second user OS hosting execution of analysis and report generation  1036 , and a third user VM  1028  having a third user OS hosting execution of interactive report viewing, and so forth. 
     Except for the crop growing management technology of the present disclosure incorporated, elements  1012 - 1038  of software  1010  may be any one of a number of these elements known in the art. For example, hypervisor  1012  may be any one of a number of hypervisors known in the art, such as KVM, an open source hypervisor, Xen, available from Citrix Inc, of Fort Lauderdale, Fla., or VMware, available from VMware Inc of Palo Alto, Calif., and so forth. Similarly, service OS of service VM  1022  and user OS of user VMs  1024 - 1028  may be any one of a number of OS known in the art, such as Linux, available e.g., from Red Hat Enterprise of Raliegh, N.C., or Android, available from Google of Mountain View, Calif. 
     In alternate embodiments, where CGM  1000  may be configured to service multiple crop growing farms, each user VM  1024 ,  1026  and  1028  may be configured to respectively handle image processing, analysis, report generation, and interactive report viewing of one crop growing farm, to provide data isolation between crop growing farms. 
     Referring now to  FIG. 11 , wherein an example computing platform that may be suitable for use to practice the present disclosure, according to various embodiments, is illustrated. As shown, computing platform  1100 , which may be hardware  1002  of  FIG. 10 , may include one or more system-on-chips (SoCs)  1102 , ROM  1103  and system memory  1104 . Each SoCs  1102  may include one or more processor cores (CPUs), one or more graphics processor units (GPUs), one or more accelerators, such as computer vision (CV) and/or deep learning (DL) accelerators. ROM  1103  may include basic input/output system services (BIOS)  1105 . CPUs, GPUs, and CV/DL accelerators may be any one of a number of these elements known in the art. Similarly, ROM  1103  and BIOS  1105  may be any one of a number of ROM and BIOS known in the art, and system memory  1104  may be any one of a number of volatile storage known in the art. 
     Additionally, computing platform  1100  may include persistent storage devices  1106 . Example of persistent storage devices  1106  may include, but are not limited to, flash drives, hard drives, compact disc read-only memory (CD-ROM) and so forth. Further, computing platform  1100  may include input/output (I/O) device interface to couple I/O devices  1108  (such as display, keyboard, cursor control and so forth) to system  1100 , and communication interfaces  1110  (such as network interface cards, modems and so forth). Communication and I/O devices  1108  may include any number of communication and I/O devices known in the art. I/O devices may include in particular sensors  1120 , which may be some of the sensors  108  of  FIG. 1 . Examples of communication devices may include, but are not limited to, networking interfaces for Bluetooth®, Near Field Communication (NFC), WiFi, Cellular communication (such as LTE 4G/5G) and so forth. The elements may be coupled to each other via system bus  1112 , which may represent one or more buses. In the case of multiple buses, they may be bridged by one or more bus bridges (not shown). 
     Each of these elements may perform its conventional functions known in the art. In particular, ROM  1103  may include BIOS  1105  having a boot loader. System memory  1104  and mass storage devices  1106  may be employed to store a working copy and a permanent copy of the programming instructions implementing the operations associated with hypervisor  112 , service/user OS of service/user VM  1022 - 1028 , and components of the CGM technology (such as image processor  322 , analyzer  324 , reporter  326  and interactive report reader  328 , and so forth), collectively referred to as computational logic  922 . The various elements may be implemented by assembler instructions supported by processor core(s) of SoCs  1102  or high-level languages, such as, for example, C, that can be compiled into such instructions. 
     As will be appreciated by one skilled in the art, the present disclosure may be embodied as methods or computer program products. Accordingly, the present disclosure, in addition to being embodied in hardware as earlier described, may take the form of an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to as a “circuit,” “module” or “system.” Furthermore, the present disclosure may take the form of a computer program product embodied in any tangible or non-transitory medium of expression having computer-usable program code embodied in the medium.  FIG. 12  illustrates an example computer-readable non-transitory storage medium that may be suitable for use to store instructions that cause an apparatus, in response to execution of the instructions by the apparatus, to practice selected aspects of the present disclosure. As shown, non-transitory computer-readable storage medium  1202  may include a number of programming instructions  1204 . Programming instructions  1204  may be configured to enable a device, e.g., computing platform  1100 , in response to execution of the programming instructions, to implement (aspects of) hypervisor  112 , service/user OS of service/user VM  122 - 128 , and components of CGM technology (such as mage processor  322 , analyzer  324 , reporter  326  and interactive report reader  328 , and so forth) In alternate embodiments, programming instructions  1204  may be disposed on multiple computer-readable non-transitory storage media  1202  instead. In still other embodiments, programming instructions  1204  may be disposed on computer-readable transitory storage media  1202 , such as, signals. 
     Any combination of one or more computer usable or computer readable medium(s) may be utilized. The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a transmission media such as those supporting the Internet or an intranet, or a magnetic storage device. Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory. In the context of this document, a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-usable medium may include a propagated data signal with the computer-usable program code embodied therewith, either in baseband or as part of a carrier wave. The computer usable program code may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc. 
     Computer program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer-readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a,” “an” and “the” are intended to include plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specific the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operation, elements, components, and/or groups thereof. 
     Embodiments may be implemented as a computer process, a computing system or as an article of manufacture such as a computer program product of computer readable media. The computer program product may be a computer storage medium readable by a computer system and encoding a computer program instructions for executing a computer process. 
     The corresponding structures, material, acts, and equivalents of all means or steps plus function elements in the claims below are intended to include any structure, material or act for performing the function in combination with other claimed elements are specifically claimed. The description of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill without departing from the scope and spirit of the disclosure. The embodiment was chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for embodiments with various modifications as are suited to the particular use contemplated. 
     It will be apparent to those skilled in the art that various modifications and variations can be made in the disclosed embodiments of the disclosed device and associated methods without departing from the spirit or scope of the disclosure. Thus, it is intended that the present disclosure covers the modifications and variations of the embodiments disclosed above provided that the modifications and variations come within the scope of any claims and their equivalents.