Abstract: The disclosure relates generally to methods and systems for ground water prediction using integration of satellite and auxiliary observations. Conventional techniques in the art for the ground water level prediction are not accurate and efficient for predicting the ground water level. The present disclosure combines time-series archived remote sensing data with a wide array of past auxiliary datasets specific to a given region for precise prediction of future ground water condition, to predict the ground water situation i.e. rising or declining for a given region. These encompass diverse information such as weather conditions, soil properties, agricultural data, population statistics, water resources information, industrial details, drought records, seismic data, and surface deformation patterns.

Abstract: Climate change has become a matter of concern due to increase in green-house gases (GHG) emissions. Agriculture is major contributor in GHG emissions. The present disclosure provides a system and method for estimation of dynamic adoption index and transition time for shifting to regenerative agriculture. The present disclosure utilizes knowledge graph-driven machine learning for GHG emissions forecasting from an agricultural land using a historical information associated with the agricultural land derived from remote sensing and on-field sensors. Further, a dynamic adoption index is estimated with knowledge-driven machine learning and data integration using data on forecasted GHG emissions, recommended agricultural practices and followed agricultural practices on the farm. Furthermore, a dynamic transition time required for shifting to regenerative agriculture is estimated with machine learning and context-sensitive modeling.

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: 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: 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: 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 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: 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: 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 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: 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: 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.