公开(公告)号：US12033368B2

公开(公告)日：2024-07-09

申请号：US17585108

申请日：2022-01-26

Applicant: Tata Consultancy Services Limited

Inventor： Jayantrao Mohite ,  Suryakant Ashok Sawant ,  Ankur Pandit ,  Srinivasu Pappula

IPC: G06V10/48 ,  G06F18/214 ,  G06N20/00 ,  G06V10/74 ,  G06V10/774 ,  G06V20/10 ,  G06V20/13

CPC classification number: G06V10/48 ,  G06N20/00 ,  G06V10/761 ,  G06V10/774

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.

公开(公告)号：US12159456B2

公开(公告)日：2024-12-03

申请号：US17451664

申请日：2021-10-21

Applicant: Tata Consultancy Services Limited

Inventor： Jayantrao Mohite ,  Suryakant Ashok Sawant ,  Ankur Pandit ,  Srinivasu Pappula ,  Mariappan Sakkan

IPC: G06V20/10 ,  G06T7/70

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.

公开(公告)号：US11963488B2

公开(公告)日：2024-04-23

申请号：US17646731

申请日：2022-01-03

Applicant: Tata Consultancy Services Limited

Inventor： Ankur Pandit ,  Jayantrao Mohite ,  Suryakant Ashok Sawant ,  Srinivasu Pappula

IPC: A01G25/16 ,  G01N33/24 ,  G01S19/14 ,  G01S19/25 ,  G01S19/26

CPC classification number: A01G25/167 ,  G01N33/246 ,  G01S19/14 ,  G01S19/256 ,  G01S19/26

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.

公开(公告)号：US12182751B2

公开(公告)日：2024-12-31

申请号：US17813371

申请日：2022-07-19

Applicant: Tata Consultancy Services Limited

Inventor： Jayantrao Mohite ,  Srinivasu Pappula ,  Suryakant Ashok Sawant ,  Vaibhav Sadashiv Lonkar ,  Mariappan Sakkan ,  Ankur Pandit

IPC: G06Q10/0639 ,  A01B79/00 ,  G06F16/29 ,  G06Q50/02 ,  G06T7/11 ,  G06V10/75 ,  G06V20/00 ,  G06V20/10 ,  G06V20/13

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. Predicting potential NPAs includes obtaining current season satellite data, data received from the fields via sensors, determining crop protocol being followed, forecast weather, estimating cumulative growth, feedback received from fields, projected yield, estimating crop losses, and then performance score is generated for predicting crops as potential NPA.