Method and system for crop loss estimation

Crop loss estimation allows a user to monitor and estimate damage to the crops due to various natural events/factors. State of the art systems used for the crop loss estimation have the disadvantage that they do not convey to the users extent of damage. In addition to this, the existing methods do not take into account the recovery factor of the crops due to multiple factors and end up in overestimating the loss. The disclosure herein generally relates to crop monitoring, and, more particularly, to a method and system for crop loss estimation. In this method, crop loss is assessed based on real-time weather parameters and remote sensing data collected and processed, and crops are classified as being in one of a repairable damage class and a permanent damage class. The system also quantifies the crop loss, which allows the user to understand magnitude of the crop loss.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY

This application is an US National Stage Filing and claims priority from International Application No. PCT/IB2021/052360, filed on Mar. 22, 2021, which application claims priority from Indian Provisional Patent Application No. 202021013244 filed on Mar. 26, 2020. The entire contents of the aforementioned applications are incorporated herein by reference.

TECHNICAL FIELD

The disclosure herein generally relates to crop monitoring, and, more particularly, to a method and system for crop loss estimation.

BACKGROUND

There are different factors that affect growth or health of crops. A few examples of such factors are weather conditions, soil characteristics, region, water availability and so on. Some crops grow in hot weather conditions, some grow in cold weather conditions, some in monsoon, and so on. Even though crops are planted taking such characteristics and requirements into consideration, sudden change in climatic conditions, occurrence of any localized calamity/natural event or any such factors can still adversely affect health of the crops.

Many crop loss assessment/health monitoring systems exist in the market, and they use different approaches to monitor the crop loss/health. Image processing based crop loss estimation is an example, in which image of the crops, taken at two different time instances are compared using appropriate image processing mechanisms, to understand changes happened over a period of time. One disadvantage of the state of the art systems is that they do not convey to the users extent of damage. In addition to this, existing methods do not take into account the recovery factor of the crops due to multiple factors and end up in overestimating the loss. Also, even if there is a chance that the users can actually save the crop from complete damage, the user wouldn't know, which in turn affects the yield/results.

SUMMARY

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a processor implemented method for crop loss estimation is provided. The crop loss estimation is performed for each Region on Interest (ROI), which is a specific geographical area being considered. Initially, real-time information on one or more weather parameters, and one or more remote sensing indicators are collected via one or more hardware processors. Further, by processing the real-time information on the one or more weather parameters and the one or more remote sensing indicators, one or more critical time windows in at least one later time instance is determined wherein in each of the one or more critical time windows one or more crops in the ROI suffers a crop loss. Further, the crop loss in each of the one or more critical time windows is classified as one of a repairable damage and a permanent damage, via the one or more hardware processors. Further, the crop loss in each of the time windows is quantified. To quantify the crop loss, initially remote sensing time series data of at least one image of at least one hotspot in the time window for which crop loss is to be quantified is collected. Further, a time series estimator for each pixel in the at least one image is estimated, wherein the time-series estimator for a pixel is estimated using comparison of a pre-defined time-series of the pixel with a current time series of the pixel. Further, a temporal estimator for each pixel in the at least one image is estimated, wherein the temporal estimator for a pixel is estimated based on a long-term temporal average of a crop at a target pixel with a temporal data of the crop at the current pixel. Further, a spatial estimator for each pixel in the at least one image is estimated, wherein the spatial estimator for a pixel is estimated based on condition of the pixel in comparison with one or more other pixels in the image of the hotspot. The total crop loss at a hotspot is quantified as equal to weighted average of the time-series estimator, the temporal estimator, and the spatial estimator.

In another aspect, a system for crop loss estimation is provided. The system includes one or more hardware processors, one or more communication interfaces, and one or more memory storing a plurality of instructions. The plurality of instructions when executed cause the one or more hardware processors to perform the crop loss estimation for each Region on Interest (ROI). The system initially collects real-time information on one or more weather parameters and one or more remote sensing indicators. Further, by processing the real-time information on the one or more weather parameters and the one or more remote sensing indicators, one or more critical time windows in at least one later time instance is determined wherein in each of the one or more critical time windows one or more crops in the ROI suffers a crop loss. Further, the crop loss in each of the one or more critical time windows is classified as one of a repairable damage and a permanent damage, via the one or more hardware processors. Further, the crop loss in each of the time windows is quantified. To quantify the crop loss, initially remote sensing time series data of at least one image of at least one hotspot in the time window for which crop loss is to be quantified is collected. Further, a time series estimator for each pixel in the at least one image is estimated, wherein the time-series estimator for a pixel is estimated using comparison of a pre-defined time-series of the pixel with a current time series of the pixel. Further, a temporal estimator for each pixel in the at least one image is estimated, wherein the temporal estimator for a pixel is estimated based on a long-term temporal average of a crop at a target pixel with a temporal data of the crop at the current pixel. Further, a spatial estimator for each pixel in the at least one image is estimated, wherein the spatial estimator for a pixel is estimated based on condition of the pixel in comparison with one or more other pixels in the image of the hotspot. The total crop loss at a hotspot is quantified as equal to weighted average of the time-series estimator, the temporal estimator, and the spatial estimator.

In yet another aspect, a non-transitory computer readable medium for crop loss estimation is provided. The crop loss estimation is performed for each Region on Interest (ROI), which is a specific geographical area being considered. Initially, real-time information on one or more weather parameters, and one or more remote sensing indicators are collected via one or more hardware processors, by the non-transitory computer readable medium. Further, by processing the real-time information on the one or more weather parameters and the one or more remote sensing indicators, one or more critical time windows in at least one later time instance is determined wherein in each of the one or more critical time windows one or more crops in the ROI suffers a crop loss. Further, the crop loss in each of the one or more critical time windows is classified as one of a repairable damage and a permanent damage, via the one or more hardware processors. Further, the crop loss in each of the time windows is quantified. To quantify the crop loss, initially remote sensing time series data of at least one image of at least one hotspot in the time window for which crop loss is to be quantified is collected. Further, a time series estimator for each pixel in the at least one image is estimated, wherein the time-series estimator for a pixel is estimated using comparison of a pre-defined time-series of the pixel with a current time series of the pixel. Further, a temporal estimator for each pixel in the at least one image is estimated, wherein the temporal estimator for a pixel is estimated based on a long-term temporal average of a crop at a target pixel with a temporal data of the crop at the current pixel. Further, a spatial estimator for each pixel in the at least one image is estimated, wherein the spatial estimator for a pixel is estimated based on condition of the pixel in comparison with one or more other pixels in the image of the hotspot. The total crop loss at a hotspot is quantified as equal to weighted average of the time-series estimator, the temporal estimator, and the spatial estimator.

DETAILED DESCRIPTION OF EMBODIMENTS

FIG.1illustrates an exemplary system for crop loss estimation, according to some embodiments of the present disclosure. The system100may be implemented in a computing device. Examples of the computing device include, but are not limited to, mainframe computers, workstations, personal computers, desktop computers, minicomputers, servers, multiprocessor systems, laptops, a cellular communicating device, such as a personal digital assistant, a smart phone, and a mobile phone; and the like. The system100, implemented using the computing device, includes one or more hardware processor(s)102, IO interface(s)104, and a memory106coupled to the processor102. The processor102can be a single processing unit or a number of units. The hardware processor102, the memory106, and the IO interface104may be coupled by a system bus such as a system bus112or a similar mechanism. The processor102may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor102is configured to fetch and execute computer-readable instructions and data stored in the memory106.

The IO interfaces104may include a variety of software and hardware interfaces, for example, interface for peripheral device(s), such as a keyboard, a mouse, an external memory, and a printer. Further, the IO interfaces104may enable the computing device to communicate with other computing devices, such as a personal computer, a laptop, and like.

The modules108may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular abstract data types. The modules108may include programs or computer-readable instructions or coded instructions that supplement applications or functions performed by the system100. The modules108may also be used as, signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the modules108can be used by hardware, by computer-readable instructions executed by the one or more hardware processors102, or by a combination thereof. In an embodiment, the modules108can include various sub-modules and other module(s)116. The other module(s)116may include programs or coded instructions that supplement applications and functions of the computing device.

The data110, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the module(s)108. The data110includes, for example, information on weather data and remote sensing data collected over a period of time, information on classification of crop loss, Weather and Remote Sensing indexes, quantified crop loss, and so on, and other data. The other data includes data generated as a result of the execution of one or more modules in the other module(s).

FIG.2is a flow diagram depicting steps involved in the process of crop loss estimation, using the system ofFIG.1, according to some embodiments of the present disclosure. In order to perform monitoring of crops in a particular geographic area/region, certain Region of Interest (ROI)s may be selected by a user, using a user interface provided by the I/O interface(s)104. Each ROI may include a part of a larger geographical area being monitored by the system100for crop loss estimation. For convenience, the crop loss estimation process is explained considering a single ROI.

Initially, the system100collects (202) real-time weather information and remote sensing data from the ROI. The system100may use any appropriate sensors for collecting the real-time weather data. For example, one or more suitable temperature sensors may be used to collect information on atmospheric temperature in the ROI. Similar way, based on other weather parameters required to be collected for the crop loss estimation purpose, using appropriate sensors. To ease the process of covering large geographical areas, the system100may rely on the remote sensing data, that may be collected by any suitable means such as but not limited to satellites, drones, and so on.

The system100processes the collected weather data and the remote sensing data to determine (204) one or more critical time windows, wherein in each of the critical time windows, one or more weather parameters are identified to be outside a normal range of the weather parameter. Steps involved in the process of determining one or more of the time windows as the critical time windows are depicted inFIG.3, and the steps are elaborated in description ofFIG.3.

The system100further processes the real-time data collected for each of the critical time windows, estimates crop loss in each of the critical time windows, and then classifies (206) the crop loss in each of the time windows as one of a repairable damage and a permanent damage. Steps involved in the process of classifying the crop loss in each of the time windows as one of the repairable damage and the permanent damage are depicted inFIG.4, and the process is explained in description ofFIG.4.

The system100further quantifies the crop loss in each of the critical time windows. A quantified data of the crop loss, alone or in combination with the classification generated at step206, can indicate to the user an extent of the crop loss, and this in turn can allow the user to take appropriate measures to save the crops or discard the crops. Steps involved in the process of quantifying the crop loss are depicted inFIG.5, and the steps are elaborated in description ofFIG.5.

In various embodiments, one or more steps in method200may be performed in the same order as depicted inFIG.2or in any alternate order that is technically feasible. In another embodiment, one or more steps in method200may be omitted.

FIG.3illustrates a flow diagram depicting steps involved in the process of determining one or more time windows as critical time windows in terms of crop loss, using the system ofFIG.1, in accordance with some embodiments of the present disclosure. At step302, the system100assigns weightage to each of the weather parameters and the remote sensing data collected in real-time. The weightages for each of the weather parameters and the remote sensing data may be pre-defined based on one or more factors/parameters such as but not limited to type of crops, type of an event (if detected), location information (for example, whether the ROI is in a Agro-Ecological Region/Zone (AER/AEZ) or not), and so on. For example, specific values of weightages for ROI in an AER are pre-defined. While processing the real-time data collected, the system100may check whether the ROI from which the data has been collected is in an AER, and if yes, then the corresponding weightages are assigned. Similarly for other factors individually, or for any suitable combination of the one or more factors, the weightages may be defined with the system100, and the system in turn assigns the weightages.

Further at step304, the system100calculates value of a Weather and Remote Sensing (WeR) index based on the weightages assigned to the weather parameters and the remote sensing data. The system100calculates the WeR index continuously for the real-time data collected at different instances over a period of time. The WeR index calculated over a period of time is used by the system100to calculate (206) an accumulated WeR index.

At step308, the system100compares the accumulated WeR index with a threshold of WeR index. If the accumulated WeR index exceeds the threshold of WeR index for any time window, that particular time window is determined as a critical time window.

In various embodiments, one or more steps in method300may be performed in the same order as depicted inFIG.3or in any alternate order that is technically feasible. In another embodiment, one or more steps in method300may be omitted.

FIG.4is a flow diagram depicting steps involved in the process of classifying crops as belonging to one of a permanent damage class and a repairable damage class, using the system ofFIG.1, according to some embodiments of the present disclosure. At step402, the system100uses a combination of data such as but not limited to crop/growth stage, weather patterns, agro-ecological zones, and type of events, to determine one or more hotspots in each of the critical time windows. Hotspots are the regions/pixels where there is a chance of crop loss, repairable or permanent damage. The system100then determines at step404, a pre-event remote sensing index (A) for each pixel in an image of each of the hotspots. The system100further calculates a long term average (B) and a short term average (C). The system100then classifies a pixel as belonging to a repairable damage class if (A-C)>threshold, and A-B<threshold. Similarly, the system100then classifies a pixel as belonging to a permanent damage class if (A-C)>threshold, and (A-B)>threshold. Value of the threshold may be configured with the system100, statically or dynamically.

If a pixel or a group of pixels that represent a crop in the image belong to the repairable damage class, then the system100classifies health loss/damage of that particular crop as a repairable damage. The ‘health loss’ of the crop amounts to the crop loss. If a pixel or a group of pixels that represent a crop in the image belong to the permanent damage class, then the system100classifies health loss/damage of that particular crop as a permanent damage.

In various embodiments, one or more steps in method400may be performed in the same order as depicted inFIG.4or in any alternate order that is technically feasible. In another embodiment, one or more steps in method400may be omitted.

FIG.5is a flow diagram depicting steps involved in the process of quantifying crop loss in a time window, using the system ofFIG.1, in accordance with some embodiments of the present disclosure.

In order to quantify the crop loss in a critical time window, the system100collects (502) remote sensing time series data of at least one image of at least one hotspot in the critical time window. The system100then estimates a time series estimator for each pixel in the at least one image, wherein the time-series estimator for a pixel is estimated using comparison of a pre-defined time-series of the pixel with a current time series of the pixel. The pre-defined time-series of the pixel may be a historical data collected and maintained by the system100in a database in the memory106. The system100then estimates (504) a temporal estimator for each pixel in the at least one image, wherein the temporal estimator for a pixel is estimated based on a long-term temporal average of a crop at a target pixel with a temporal data of the crop at the current pixel. The system100further estimates a spatial estimator for each pixel in the at least one image, wherein the spatial estimator for a pixel is estimated based on condition of the pixel in comparison with one or more other pixels in the image of the hotspot. Further, the system100quantifies the total crop loss at a hotspot as equal to weighted average of the time-series estimator, the temporal estimator, and the spatial estimator.

The embodiments of present disclosure herein addresses unresolved problem of crop loss estimation. The embodiment, thus provides a method and system for classifying damage to health of the crops as one of a permanent damage and a repairable damage. Moreover, the embodiments herein further provides a mechanism to quantify health loss of crops i.e. crop loss.

For a given ROI, crops are represented as C1, Agro-Ecological Zone are represented as AE1, and an even that occurred is considered as Drought (D). Weather parameters considered are Temperature (T) with weight Tw and Rainfall (P) with weight Pw, and the remote sensing indicators=NDVI (Nd) with weight Nw and Land Surface Temperature (LST) with weight LSTw.

Daily WeR Index is calculated as:
(DWeR_It1=(T×Tw)+(P×Pw)+(Nd×Nw)+(LST×LSTw)  (1)

From the daily WeR indexes collected over a period of time, the accumulated WeR Index is calculated as:
AWeR_Itn=DWeR_It1+DWeR_It2+ . . . +DWeR_Itn(2)Predefined threshold for defined C1 and AE1 is AWIsCheck if AWeR_Itn=>AWeRIsthen Crop C1 under stress at that pixelelseno stress and continue the AWI estimation

The process is repeated for all pixels to determine pixels under stress, which are termed as localized hot spots (hot spots).

2. Classifying the Pixels into Repairable and Permanent Damage

Assume a remote sensing variable as NDVI under consideration.Fixed time series average of NDVI before the event as Pre_NDVIPre_NDVI of each pixel is calculated by finding maximum NDVI from selected series (more than three images)
Pre_NDVI=Maximum(NDVIt1,NDVIt2,NDVIt3, . . . NDVItN)  (3)Short term average of NDVI after the event as Short_NDVIShort_NDVI of each pixel is calculated by finding maximum NDVI from selected short series (typically two or three images/scenes) after event.
Short_NDVI=Maximum(NDVIte1,NDVIte2,NDVIte3)  (4)Long term average of NDVI after the event as Long_NDVILong_NDVI of each pixel is calculated by finding maximum NDVI from selected long series after event (more than three images).
Long_NDVI=Maximum(NDVIte1,NDVIte2,NDVIte3, . . . NDVIteN)   (5)
NDVI=(NIR−Red)/(NIR+Red)  (6)whereNDVI—Normalized Difference Vegetation IndexNIR—Reflectance in Near Infra-red bandRed—Reflectance in Red bandpredefined threshold (which is a function of crop, stage, ago-climatic zone) is Ptif(Pre_NDVI−Short_NDVI)>PtAND (Pre_NDVI−Long_NDVI)>Ptassign it as permanent damageelseassign it as repairable damage
3. Estimation of Crop Loss Severity for Repairable Damage as Well as Permanent Damage AreasNow, get satellite data for those localized hot spotsEstimators,
Time-series estimator(Ets)=f(Sts,Ats)  (7)Sts—Standard time series of a C1, for AE1Ats—time-series of a pixel under stress
Temporal Estimator(Et)=f(TAt,Pt)  (8)TAt—Long term average of a affected pixelPt—value at affected pixel during affected time-period
Spatial Estimator(Es)=f(As,Cs)  (9)As—Average condition of all affected pixelsCs—condition of affected pixels under study
Crop Loss Severity Index(CLSI)=(Wts×Ets)+(Wt×Et)+(Ws×Es)  (10)here Wts, Wt and Ws are the weights for time-series, temporal and spatial estimators

The scope loss severity index represents the quantified value that represents total crop loss in the time window considered.