Authors :
Ashutosh Pawar; Upasana Singh; Priyanka Shamraj; Bhargav Sonawane
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/z52m7mnt
Scribd :
https://tinyurl.com/3ydvj45a
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1981
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Crop area estimation is a critical aspect of
agricultural monitoring and management, providing
essential information for decision-making in the
agricultural sector. Study was carried out at Semantic
Technologies and Agritech services Pvt. Ltd., GIS and
Remote sensing team, Pune during Kharif-2023. All
methodology given by YESTECH manual under Pradhan
Mantri Fasal Bima Yojana (PFMBY) was followed. Latur
district facing more weather-based yield losses during last
few of years. In this case study we tried to estimate yield
of soybean crop for agriculture-based stake holders,
insurance companies, Government polices at Revenue
circle level (RC). Multimodal approach is beneficial over
single model yield estimation approach as it takes
ensemble yield for perfect forecasting of crop yield.
Accuracy was in the range as given in YESTECH manual
at RC level. Thus, overall results show that use of such
model for yield estimation is one of the best approach to
take the decisions for insurance based stake holders in
rainfed regions where more negative consequences on
soybean productivity under different climate change
scenario was observed.
Keywords :
Remote Sensing, GIS, NPP, Machine Learning, DSSAT-4.8, Soybean, Latur, Yield Simulation, Revenue Circle, Soybean productivity.
Crop area estimation is a critical aspect of
agricultural monitoring and management, providing
essential information for decision-making in the
agricultural sector. Study was carried out at Semantic
Technologies and Agritech services Pvt. Ltd., GIS and
Remote sensing team, Pune during Kharif-2023. All
methodology given by YESTECH manual under Pradhan
Mantri Fasal Bima Yojana (PFMBY) was followed. Latur
district facing more weather-based yield losses during last
few of years. In this case study we tried to estimate yield
of soybean crop for agriculture-based stake holders,
insurance companies, Government polices at Revenue
circle level (RC). Multimodal approach is beneficial over
single model yield estimation approach as it takes
ensemble yield for perfect forecasting of crop yield.
Accuracy was in the range as given in YESTECH manual
at RC level. Thus, overall results show that use of such
model for yield estimation is one of the best approach to
take the decisions for insurance based stake holders in
rainfed regions where more negative consequences on
soybean productivity under different climate change
scenario was observed.
Keywords :
Remote Sensing, GIS, NPP, Machine Learning, DSSAT-4.8, Soybean, Latur, Yield Simulation, Revenue Circle, Soybean productivity.
