CROP YIELD PREDICTION SYSTEM

A yield prediction system including an information gathering unit that retrieves a plurality of images of a field over a time period, an information analysis unit that divides each image into a plurality of tiles. a pixel analysis unit that gathers at least one agronomic rule to each tile and a simulation unit that determines the yield represented by each pixel in each image based on the agronomic rules and the analysis of each tile.

BACKGROUND OF THE INVENTION

Crop yield forecasting is a central task in precision agriculture because of its impact on food security, economics, and scientific development. Numerous stakeholders are impacted: farmers rely on accurate predictions to make informed management decisions and take appropriate actions; commercial suppliers seek to understand how new seed varieties will perform in different areas; governments and international organizations depend on early and accurate forecasts to anticipate disruptions in food security or import/exports.

Current methods of crop yield forecasting involve reliance on manual methods and prior yields which are not accurate. Computer based yield predictions have been attempted with very little success. Therefore, a need exists for system that will accurately predict the crop yield of a particular field.

SUMMARY OF THE INVENTION

One embodiment of the present disclosure includes a yield prediction system including an information gathering unit that retrieves a plurality of images of a field over a time period, an information analysis unit that divides each image into a plurality of tiles. a pixel analysis unit that gathers at least one agronomic rule to each tile and a simulation unit that determines the yield represented by each pixel in each image based on the agronomic rules and the analysis of each tile.

In another embodiment, each tile is a four channel image having red, blue, green and NIR reflectance.

In another embodiment, a mean square error, mean absolute error and mean absolute precent error are calculated for each tile.

In another embodiment, only the areas of each tile that are managed are used in the analysis.

In another embodiment, each tile is scaled to bring a value of a pixel in the tile to between 0-2.

In another embodiment, an encoder/decoder analyzes the pixel density for each image.

In another embodiment, the encoder/decoder analyzes shades of each pixel to determine a stress level of all areas of the field ranging from no stress to high stress.

In another embodiment, erosion and blurring are applied to each tile to remove noise from the image by the pixel analysis unit.

In another embodiment, using the stress levels of each area and the pixel and the yield density of each pixel, the encoder/decoder calculates the predicted yield of each area of the field based on the image data only.

In another embodiment, wherein each area in the image is classified based on the severity levels.

Another embodiment of the present disclosure includes a method of predicting a yield of a field including the steps of retrieving a plurality of images of a field over a time period via an information gathering unit, dividing each image into a plurality of tiles via an information analysis unit;gathering at least one agronomic rule to each tile via a pixel analysis unit and determining the yield represented by each pixel in each image based on the at least one agronomic rules and the analysis of each tile via a simulation unit.

In another embodiment, each tile is a four channel image having red, blue, green and NIR reflectance.

In another embodiment, a mean square error, mean absolute error and mean absolute precent error are calculated for each tile.

In another embodiment, only the areas of each tile that are managed are used in the analysis.

In another embodiment, each tile is scaled to bring a value of a pixel in the tile to between 0-2.

In another embodiment, an encoder/decoder analyzes the pixel density for each image.

In another embodiment, the encoder/decoder analyzes shades of each pixel to determine a stress level of all areas of the field ranging from no stress to high stress.

In another embodiment, erosion and blurring are applied to each tile to remove noise from the image by the pixel analysis unit.

In another embodiment, using the stress levels of each area and the pixel and the yield density of each pixel, the encoder/decoder calculates the predicted yield of each area of the field based on the image data only.

In another embodiment, each area in the image is classified based on the severity levels.

DETAILED DESCRIPTION OF THE INVENTION

Referring now to the drawings which depict different embodiments consistent with the present invention, wherever possible, the same reference numbers will be used throughout the drawings and the following description to refer to the same or like parts.

Yield forecasting has been a central task in computational agriculture because of its impact on agricultural management from the individual farmer to the government level. The yield predication system100of the present disclosure utilizes high-resolution aerial imagery and output from high-precision harvesters to predict in-field harvest values for farms in the US. By analyzing yield on a pixel level in an image of a field, farmers are provided a detailed analysis of which areas of the farm may be performing poorly so the appropriate management decisions can be made in addition to providing an improved prediction of total yield.

FIG.1depicts one embodiment of a yield prediction system100consistent with the present invention. The yield prediction system100includes a yield prediction unit102, a communication device 1104, a communication device 2106each communicatively connected via a network108. The yield prediction unit102further includes an information gathering unit110, an information analysis unit112, a pixel analysis unit114and a simulation unit116.

The information gathering unit110and information analysis unit112may be embodied by one or more servers. Alternatively, each of the pixel analysis unit114and simulation unit116may be implemented using any combination of hardware and software, whether as incorporated in a single device or as a functionally distributed across multiple platforms and devices.

In one embodiment, the network108is a cellular network, a TCP/IP network, or any other suitable network topology. In another embodiment, the yield prediction unit100may be servers, workstations, network appliances or any other suitable data storage devices. In another embodiment, the communication devices104and106may be any combination of cellular phones, telephones, personal data assistants, or any other suitable communication devices. In one embodiment, the network102may be any private or public communication network known to one skilled in the art such as a local area network (“LAN”), wide area network (“WAN”), peer-to-peer network, cellular network or any suitable network, using standard communication protocols. The network108may include hardwired as well as wireless branches.

FIG.2depicts one embodiment of a yield prediction unit102. The yield prediction unit102includes a network I/O device204, a processor202, a display206and a secondary storage208running image storage unit210and a memory212running a graphical user interface214. In one embodiment, the processor202may be a central processing unit (“CPU”), an application specific integrated circuit (“ASIC”), a microprocessor or any other suitable processing device. The memory212may include a hard disk, random access memory, cache, removable media drive, mass storage or configuration suitable as storage for data, instructions, and information. In one embodiment, the memory208and processor202may be integrated. The memory may use any type of volatile or non-volatile storage techniques and mediums. The network I/O line204device may be a network interface card, a cellular interface card, a plain old telephone service (“POTS”) interface card, an ASCII interface card, or any other suitable network interface device.

FIG.3depicts one embodiment of a communication device104/106consistent with the present invention. The communication device104/1106includes a processor302, a network I/O Unit304, an image capture unit306, a secondary storage unit308including an image storage device310, and memory312running a graphical user interface314. In one embodiment, the processor302may be a central processing unit (“CPU”), a application specific integrated circuit (“ASIC”), a microprocessor or any other suitable processing device. The memory312may include a hard disk, random access memory, cache, removable media drive, mass storage or configuration suitable as storage for data, instructions, and information. In one embodiment, the memory312and processor302may be integrated. The memory may use any type of volatile or non-volatile storage techniques and mediums. The network I/O device304may be a network interface card, a plain old telephone service (“POTS”) interface card, an ASCII interface card, or any other suitable network interface device.

In one embodiment, the network108may be any private or public communication network known to one skilled in the art such as a Local Area Network (“LAN”), Wide Area Network (“WAN”), Peer-to-Peer Network, Cellular network or any suitable network, using standard communication protocols. The network108may include hardwired as well as wireless branches.

FIG.4Adepicts a schematic representation of a process to predict the crop yield of a field. In step402, images of one or more fields are gathered over time by the information gathering unit110. In one embodiment, the images are gathered from low flying aircraft over one or more growing seasons. In step404, the information gathering unit110receives information from farm equipment operating on each field. The equipment information may include the geographic location of the equipment over time, the equipment velocity at a given time, and a vector map of the equipment as it moves through the field which is used to determine seed density at any position in the field. In step406, the information analysis unit112gathers agronomic rules to be applied to each of the tiles. In one embodiment, the agronomic rules are rules gathered from professional agronomists relating the crop type and field type similar to the crop type and field type in the images. In step408, each field image is separated into a plurality of equal sized tile. In one embodiment, the tiles are 512×512 pixels by the pixel analysis unit114. Each tile is downsized using cubic resampling to produce images having 20 cm/pixel resolution. The tiles are split into groups for training, validation and testing. Tiles containing less ten percent of data are discarded.

In step410, the agronomic rules are applied to each tile. The normalized vegetation index (NDVI) and green normalized vegetation index (GDVI) are determined across each tile by the pixel analysis unit114. Each tile is then analyzed by the pixel analysis unit114by applying erosion, blurring, threshold and connected components to identify anomalous regions in each tile. Each field is then represented as an s=4 non-mutually exclusive binary mask F corresponding to agronomic rules previously gathered as high stress, low biomass, low vigor and low growth associated with the field. Each image of the field is evaluated to create a feature map defined by the following equation:

Where Fsis the number of times in the first p aircraft flights in which the sth feature is present. For each tile, the features are calculated based on the mean, standard deviation, mean absolute deviation, standard deviation and 5th, 25th, 50th, 75th, 95thpercentiles of common agronomic indices such as NDVI, NDWI, SAVI, EVI and GRNDV. In one embodiment, the mean, standard deviation and skew of the red, green, blue and NIR histograms are calculated for each tile by the pixel analysis unit114. In another embodiment, the mean, standard deviation and skew are calculate for the seeding rate distribution for each tile using the equipment information. In step412, total yield of each tile is determined for each tile using the previously determined features of each tile. The information analysis unit determines the mean squared error between the actual and predicted yield using the following formula:

Where Mij is the mask corresponding to the same area whose elements are 1 if the area is managed and 0 otherwise and Ytotal is the single value yield calculated by the model.

FIG.4Bdepicts a schematic representation of a process to estimate crop yield of a field. In step420, images of one or more fields are gathered over time by the information gathering unit110. In one embodiment, the images are gathered from low flying aircraft over one or more growing seasons. In step422, the information gathering unit110receives information from farm equipment operating on each field. The equipment information may include the geographic location of the equipment over time, the equipment velocity at a given time, and a vector map of the equipment as it moves through the field which is used to determine seed density at any position in the field.

In step424, the field application data is generated based on image data and equipment data without using any agronomic data. In step426, the image is separated into tiles. In one embodiment, the tiles are each 512 by 512 pixels. In another embodiment, each tile is a 4 channel image having red, blue, green and NIR reflectance channels taken from a flight Ip. Each tile is scaled to bring the values of the pixels to the 0-2 range. In step428, the pixels in each tile are analyzed by the pixel analysis unit114to determine a pixel level yield in each tile. For each image Xij a yield density of Yij in units/pixel is calculated. In step430, the total yield is calculated by calculating the yield of each tile using the yield density. The total predicted yield is calculated using the following formula:

Where Mij is the mask corresponding to the area whose elements are 1 if the area is managed and 0 if the area is not managed. Field level metrics are calculated by performing an aggregate over all tiles using the equation:

Where the tile areas used are only managed portions of the tile area. In one embodiment, mean square error, mean absolute error and mean absolute precent error are calculated. In step432, the average yield of the field is determined. The average field value is calculated using the following formula:

Where the tile area corresponds only to areas In the tiles which are planted. Mean square error, mean absolute error and mean absolute percent error are calculated using the totals.

FIG.4Cdepicts a schematic representation of a process of determining crop yield using an encoder/decoder to determine filed crop yield with image data only. In step440, image data is gathered from the information gathering unit110. In step442, the encoder/decoder analyzes the pixel density for each image. Using pixel density only, the encoder/decoder determines areas of the field producing more or less based on the level of stress displayed in each pixel value. The encoder/decoder analyzes the shades of each pixel to determine the stress level of all areas of the field ranging from no stress to high stress. The pixel by pixel analysis also identifies the yield density of each pixel to determine the total yield of the field in the image with very high accuracy using only image data. By identifying the stress levels of each area and the pixel and the yield density of each pixel, the encoder/decoder can accurately determine the predicted yield of each area of the field based on the image data only.

FIG.5depicts a schematic representation of a process of generating validation data for an image. In step502, a tile is selected from an image of a field by the information gathering unit110. In step504, anomaly detection is performed on the tile by the information analysis unit112. In one embodiment, the anomaly detection is performed based on NDVI of the image. In step506, erosion and blurring are applied to the image to remove noise from the image by the pixel analysis unit114. In one embodiment, erosion and blurring are performed on NDVI and GNDVI versions of the images. In step508, each region in the tile is thresholded at three levels to create three severity levels by the pixel analysis unit114. The severity levels along with the green and red differentials in each region are used to describe the tile. In step510, each area in the image is classified based on the severity levels by the pixel analysis unit114. In one embodiment, the areas are classified as high stress, low biomass, low vigor and low growth. In step510, validation data is generated for each region in the image by the simulation unit116. In one embodiment, the validation data is generated using Lasso, Random Forest and LightGBM algorithms using different security and classification levels in addition to raw RGBN data. In another embodiment, the longitude and latitude of each image region is included in the algorithm.

The validation data along with the training data can be used to improve the performance of the image analysis thereby improving the yield prediction for a field based on an image. As one having ordinary skill in the art would appreciate, using the processes described herein, the ability to accurately predict the yield from a farm field based on image analysis is greatly increased.

While various embodiments of the present invention have been described, it will be apparent to those of skill in the art that many more embodiments and implementations are possible that are within the scope of this invention. Accordingly, the present invention is not to be restricted except in light of the attached claims and their equivalents.