Abstract: Agriculture is impacted due to various parameters that results into heavy losses to the crops. Existing methods rely on manual inventories from fields and statistical results are prone to errors thus classification of crops may not be accurate. Present disclosure provides systems and methods for detection and prediction of disguised and potential non-performing assets (NPAs) in agriculture. Disguised NPAs detection involves, obtaining satellite data from sources and natural calamity, detecting of crop for past years, crop growth, estimating yields, market prices for individual years, AC zones, etc. wherein a generate performance score is generated for each field for classifying crop as disguised NPA.

Abstract: The disclosure generally relates to methods and systems for crop damage assessment using semantic reasoning. Conventional techniques using only specific data either individually or in a combination may result in bias and may not accurately estimate the crop damage, due to diversity in each of the natural calamities. The present disclosure solves the technical problems in the art using domain ontologies and a semantic reasoning over the spatio-temporal data for the automatic assessment of the crop damage due to the natural calamities. The present disclosure establishes automated crop loss assessment using trigger-based analysis of plurality of sources like satellite-based earth observations, weather observations, social media posts and news articles, for obtaining a spatio-temporal data. Then the spatio-temporal data is reasoned over the domain knowledge graph, using the semantic reasoning technique, for the crop damage assessment.

Abstract: Monitoring of animals in a herd has various challenges. A method and a system for monitoring and measuring the behavior of a plurality of animals in a herd chamber is provided. The disclosure provides a method to develop a model to identify the animal interactions between a given set of herds of two or more animals. The system uses image, RFID sensors, animal phenotyping information such as species, breed, age and reproduction cycle information to generate an animal profile. This profile is used to quantify the interaction into an aggression score to the animals in the given herd. The aggression score over a given duration can help herd manager to put the animals with similar aggression score together. This will also help analyze the temporal aspects of the aggression score for a given animal and take preventive measures.

Abstract: Providing the right mechanism to apply blockchain technology with flexible or dynamic credit disbursement while also addressing valuation of the agricultural assets that are dynamic in nature is required. A method and a system for estimating flexible credit eligibility and disbursement schedule using NFTs of agricultural assets is disclosed. A detailed blockchain based approach is provided for evaluating credit collateral of the NFTs representing the agricultural assets, also interchangeably referred to as physical farm assets or agri-assets. Valuation of the credit eligibility is based on flexibility provided to the farmer on deciding the farm assets to be used, current status of the farmer selected agricultural assets and credit equivalent value of the each of NFTed asset selected by the farmer for the season/year. The dynamic disbursement schedule is based on the personalized or customized parameters associated with the current status of farm and farm assets linked to the credit collateral.

Abstract: The disclosure herein relates to identification of agro-phenological zones. Further the disclosed method and system also shares techniques for updating of the identified/existing agro-phenological zones. In a diverse geographical domain (like India), in order to maximize the crop production (avoid crop failures) from the available resources and prevailing diverse climatic conditions it necessary to use the resources and technology to infer the best agriculture approach on an individual location. The invention enables identification of the agro-phenological zones based on satellite image, weather data, soil data and cloud free historical satellite data using several techniques that includes machine learning, time series analysis, heuristic time series analysis technique and clustering. Further the invention also discloses techniques to update the identified/existing agro-phenological zones using historic data of agro-phenological zones of satellite image.

Abstract: The disclosure relates generally to methods and systems for precise estimation of carbon emission due to tillage operations. Conventional techniques that estimate the carbon emission due to the tillage operation are not efficient and effective as only the tillage operation is considered. The present disclosure combines the type of implement used for tillage and the depth of tillage for precise estimation of carbon emission. In the present method, the geo-tagged fields where the tillage operation is performed are identified based on a satellite image data. Next, an implement type used for the tillage operation is detected. Further a spatial tillage depth having tillage depths are estimated. Lastly precise estimation of carbon released due to the tillage operation is calculated based on the soil organic carbon released due to tillage operation and the carbon emission due to fuel consumed by the type of implement used and the spatial tillage depth.

Abstract: Conventional approaches lack in traceability of various factors that led to changes in farms states. Further, conventional approaches for managing farms, and associated data obtained from various sources do not fully allow for employing farm's practices to other similar conditions as they may have adverse effects over other identical/similar crops during cultivations. Present disclosure present systems and methods that codify/pre-process and store farm parameters, associated versions and farm interactions in a systematic manner, along with associated knowledge, inferences, that bring changes to a farm's state, which can be referred as farm versions. The inferences, and relationships between farm configurations, versions and the like are used by the system to generate farm portability score for target farm and identify farm state portability. Once the current farm state is identified, the system further creates an optimized routing mechanism for the target farm to attend an optimal state.

Abstract: Precise estimation of duration of cover crop is a challenge considering multiple factors contributing to the same and complexity involved in capturing them in the estimation process. A method and system for estimation of cover crop duration and generating Integrated Cover Crop Index (ICCI) is disclosed. Firstly, the maincrop is identified and associated time series data is eliminated to avoid false positives. Detection of type of cover crop and its exact duration is derived by integrated use of satellite remote sensing data, sensor data, field observations and phenology based indicators. Duration of cover crop is estimated considering the impact of snow cover, dormant period etc., by integrated use of remote sensing and sensor data along with local domain crop knowledge of the region. The ICCI provides quantitative measure for cover crop effort and can be used for incentivizing farmers following sustainable cropping practices.

Abstract: 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.

Abstract: Plant health estimation is required to be performed so as to detect any health issues in early stages, so as to take counter measures. Existing systems for the plant health estimation perform the health estimation by considering data obtained from satellite images of the plants being monitored. However this alone may not be much effective as the satellite images fail to provide information on many parameters which have direct or indirect impact on health of the plants. Disclosed herein are a method and a system for plant health estimation, wherein the system performs health estimation at a macro level and a micro level. The macro level health estimation is done using satellite images of the plants as inputs, whereas the micro level health estimation is done by collecting and processing sensor data with respect to various parameters that affect health of a plant.

Abstract: Machine Learning models to be created for crop mapping for any region, require huge volumes of ground truth data requiring manual effort in generating region specific training dataset. Method and system for providing generalized approach for crop mapping across regions with varying characteristics is disclosed. The method provides automatic generation of a labelled pixel dataset representing cropping pattern of a Region of Interest (ROI) for building a ML crop mapping model for the ROI. The generated labelled pixel dataset captures regional dependency and localized phenological indicators for the ROI. ML crop mapping model is updated using a database, regularly updated for the set of crops and the plurality of features associated with each of the set of crops and corresponding the set of crops.

Abstract: This disclosure relates generally to method and system to calculate net carbon sequestration for agriculture using remote sensing data. Climate change is one of the factor in sustainable development on the earth and has sparked numerous initiatives to reduce earth's carbon footprint. The disclosed method processes remote sensing data comprising one or more input images indicating one or more characteristics of at least one agriculture crop of a geographical region. The method calculates a carbon footprint value of at least one agriculture crop by obtaining a plurality of carbon values associated with the geographical region. A net carbon flux of least one agriculture crop is calculated based on the carbon footprint value, a data maturity index, and a difficulty level. The method enables growers and carbon credit purchasers to correctly determine the carbon footprint of a geographical region to accurately predict the reduction of greenhouse gas emissions reducing environmental degradation.

Abstract: A processor implemented method of detecting a concentration of plurality of chemical residue in an agricultural produce is provided. The method include (a) receiving, by a hyper spectral device, a data set associated with one or more reflectance measurements of the agricultural produce; (b) determining, data associated with a plurality of bands; (c) dynamically reiterating, the steps (a) and (b) at predetermined time interval to obtain a trained dataset; (d) determining, relevant wavelengths among the selected trained data sets based on a feature selection technique to form an array of emitters; (e) calibrating, by the identified array of emitters, to emit light on the detecting region of one or more sample of the agricultural produce to obtain data associated with reflectance and transmittance; and (f) calculating, a calibration index with a de-multiplication flag to detect presence or absence of the plurality of chemical residue in the agricultural produce.

Abstract: This disclosure relates generally to root zone moisture estimation for vegetation cover using remote sensing. Conventionally, it is challenging to estimate root zone soil moisture using only satellite data. Moreover, estimation of soil moisture under vegetation cover based on bare surface soil moisture and vegetation parameters is not available. The disclosed method and system facilitate estimation of an ensemble of soil moisture under vegetation cover and root zone soil moisture using process based soil water balance for spatial estimation of root zone soil moisture. The system estimates bare surface soil moisture for different soil types/textures using the baseline bare surface model and soil properties derived from satellite data and in-situ sensors. The method further provides temporal spatially distributed soil moisture inputs to an intelligent irrigation management/information system which is very important to reduce and regulate water consumption.

Abstract: Stubble burning is a serious problem resulting in pollution attributable to smog, loss of nutrients in the top soil, and risk of fires spreading out of control. Existing methodologies have attempted to predict burning areas, but have failed to do so because of inefficient sensing mechanism. Present disclosure proposes a system and method to compute burning index score pertaining to crops by detecting harvest season and predicting probable areas of burning by combining current year's crop area map along with harvesting period and historical hot spot information. Computation of the burning index score is accomplished based on inputs received from at least one of satellite imaging, multi-spectral drone based sensing devices and crowdsourcing information. This will help to prioritize the area for taking corrective measures such as training of farmers, availability of resources, optimizing the resources schedule, etc.

Abstract: Plant health estimation is required to be performed so as to detect any health issues in early stages, so as to take counter measures. Existing systems for the plant health estimation perform the health estimation by considering data obtained from satellite images of the plants being monitored. However this alone may not be much effective as the satellite images fail to provide information on many parameters which have direct or indirect impact on health of the plants. Disclosed herein are a method and a system for plant health estimation, wherein the system performs health estimation at a macro level and a micro level. The macro level health estimation is done using satellite images of the plants as inputs, whereas the micro level health estimation is done by collecting and processing sensor data with respect to various parameters that affect health of a plant.

Abstract: This disclosure relates to methods and systems for high resolution and scalable crop yield forecasting by first developing a first crop yield forecasting model to generate coarse resolution yield maps and further dynamically selecting a set of pixels from the coarse resolution yield maps. The coarse resolution yield maps, satellite, weather and soil related data are fed as input to a second crop yield forecasting to generate high resolution crop yield forecasting maps. Further, domain knowledge about crop growth stages, economically important crop growth stages and weather based triggers are identified to quantify extent of change in crop yield. This helps in crop yield forecasting during real time adverse weather conditions. Finally, an adjusted crop yield model is obtained after adjusting losses incurred due to the real time adverse weather conditions to obtain accurate high resolution crop yield forecasting maps. The method of present disclosure is inexpensive, light-weight, and scalable.

Abstract: Coarse resolution satellite data has limited information content of land captured in the images and small area field boundary detection is technically challenging. Embodiments of the present disclosure provide a method and system for detection of field boundaries using coarse resolution satellite data and GIS. Multi-layer crop segmentation approach is used over RoI, wherein agricultural land related segments are derived from a soil layer, a temporal stack for a current season and a temporal change layer from the coarse resolution satellite data of the RoI. Segmentation based on different aspects captures various details at each segmentation layer. The segments are then combined, which aggregates all information related to the land to accurately detect the field boundaries in the RoI. The field boundaries are further refined using GIS topology operations. Baseline cadastral maps are updated using information from maps generated using refined field boundaries identified in real time for each season.

Abstract: This disclosure relates generally to automated methods and systems for assessing moral hazard and adverse selection in agricultural insurance that assist an insurer, re-insurer by generating recommendations for adoption of crop protocols using the dynamic Crop Protocol (DFI) engine of insurance companies during the crop cycle (CP). A moral score is computed based on an adoption index, a crop health index and a weather index. The moral hazard score is further used to dynamically determine a moral hazard level that indicates a correlation between one or more parameters derived from the acquired data and classified as low, moderate, and high based on the correlation. The method of present disclosure aids to under-write the insurance and associated risk based policy effectively and efficiently for determining eligible claim payout to minimize manual error and mitigate faulty loss assessment.

Abstract: Agriculture is impacted due to various parameters that results into heavy losses to the crops. Existing methods rely on manual inventories from fields and statistical results are prone to errors thus classification of crops may not be accurate. Present disclosure provides systems and methods for detection and prediction of disguised and potential non-performing assets (NPAs) in agriculture. Disguised NPAs detection involves, obtaining satellite data from sources and natural calamity, detecting of crop for past years, crop growth, estimating yields, market prices for individual years, AC zones, etc. wherein a generate performance score is generated for each field for classifying crop as disguised NPA.