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: This disclosure relates generally to assessing soil carbon sequestration. One of the driving factors for climate change and global warming is emission of greenhouse gas emissions. Of the possible ways to reduce climate change and global warming adoption, sustainable agricultural practices will enable efficient soil carbon sequestration thereby reducing the greenhouse gases as well as increasing the crop yield. The disclosure is a method and a system for real time assessing soil carbon sequestration of farm based on remote sensing. The soil carbon sequestration of farm is assessed by continuously monitoring the farm at real time based on remote sensing using a plurality of satellite data and a plurality of farming data using several techniques. The several techniques utilized for assessing soil carbon sequestration includes machine learning, a carbon sequestration estimation technique, estimating a crop health index and an adoption index and computing a set of carbon sequestration parameters.

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 agricultural lending decision making process is complex due to various issues. Most of the financial institutions relied on subjective analysis to assess the credit risk of borrowers. A method and system for assessing credit worthiness of a farmer and risks associated with a farm have been provided. The credit worthiness of the farmer and risk associated with farm is assessed by aggregating data which significantly influence the farmer credit repayment ability. The system is configured to calculate farm credit risk score based on the farm capability, farmer socioeconomic traits and entrepreneurial quality which helps to assess credit worthiness of farmer accurately. The credit worthiness of the farmer is analyzed using satellite data, field data from the farm and risks associated with them. The farm credit risk score is calculated in real time basis to every farm loan sanctioned by the lending institutions to safeguard their loss and non-performing loans.

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.