International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
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Macroscale Cotton Yield Estimation in Beed District (Kharif-2023) using Multi-Model Ensembles Upasana Singh, Bhargav Sonawane, Priyanka Shamraj and Ashutosh Pawar Ashutosh Pawar: Head, GIS and RS, Semantic Technologies and Agritech services Pvt. Ltd., Pune. Upasana Singh: Sr. GIS and RS executive, Semantic Technologies and Agritech services Pvt. Ltd., Pune. Priyanka Shamraj: Agrometeorologist, Semantic Technologies and Agritech services Pvt. Ltd., Pune. and Bhargav Sonawane: Jr. RS executive Semantic Technologies and Agritech services Pvt. Ltd., Pune.
Department of GIS and Remote Sensing, Semantic Technologies and Agritech Services, Pvt. Ltd. Pune. -------------------------------------------------------------------------***-----------------------------------------------------------------------Abstract This study, conducted by the Semantic Technologies and Agritech Services, Pvt. Ltd., Pune, GIS and Remote Sensing Team in Pune during the Kharif-2023 season, focuses on estimating Cotton crop yield in Beed District. Following the methodology outlined in the YESTECH manual under the Pradhan Mantri Fasal Bima Yojana (PMFBY), the research addresses significant weather-induced yield losses in the region. The study targets Revenue Circle (RC) level assessment using a multimodal approach, incorporating various models for precise yield forecasting. The achieved accuracy, measured with Root Mean Square Error (RMSE) below ±30% at the RC level, demonstrates the effectiveness of the ensemble approach. The findings highlight the utility of such models in decision-making for agricultural stakeholders, insurance companies, and government policies, especially in rainfed regions facing cotton productivity challenges under diverse climate change scenarios. Keywords: Remote Sensing, GIS, Net Primary Productivity (NPP), Machine Learning, (DSSAT-4.8), Cotton, Beed, Yield Simulation, Revenue Circle, Cotton Productivity.
Introduction: In today's dynamic agricultural landscape, unpredictable weather patterns, such as erratic rainfall, rising temperatures, and extreme events, pose significant threats to crop growth and yield stability. Consequently, farmers increasingly turn to drought-resistant crops and intensive irrigation, exacerbating soil degradation and amplifying economic vulnerability due to unstable production. Average cotton productivity in Maharashtra, is likely falls between 187 kg/ha and 443 kg/ha. The average cotton productivity in Beed district is 7.15 quintals per hectare (715 kg/ha). Agriculture serves as the cornerstone of global economies, sustaining livelihoods for billions while presenting critical challenges in accurately predicting crop yields. Traditional methods relying on historical data and manual observations often struggle to address the dynamic nature of modern agricultural challenges. However, the integration of advanced technologies such as software applications, remote sensing, GIS, and AI/ML algorithms has revolutionized crop yield estimation, offering unprecedented accuracy and insight. Accurate crop yield estimation holds immense significance across sectors in the contemporary landscape. Firstly, in the insurance realm, precise estimates facilitate fair risk assessment, enabling insurers to develop tailored products that alleviate financial burdens on farmers during crop failures. Secondly, in economic forecasting, reliable predictions inform commodity markets, trade agreements, and pricing mechanisms, promoting stability and ensuring food security. Thirdly, governments leverage accurate estimates to formulate effective policies, including subsidy allocation, resource distribution, and strategic interventions during adverse conditions or pest outbreaks, fostering sustainable practices and rural development. Additionally, anticipating potential shortfalls supports proactive food distribution, enhancing access, and averting scarcity. Lastly, for farmers, precise estimates enable informed decisions on crop selection, resource allocation, and market participation, enhancing productivity and livelihoods. The adoption of advanced methods for crop yield estimation signals a transformative step towards building agricultural resilience. By harnessing the synergy between software applications, remote sensing, GIS, and AI/ML technologies, stakeholders empower informed decision-making, paving the way for sustainable agricultural practices and economic prosperity. This report emphasizes the significance of employing advanced methods for estimating crop yield and its implications across diverse domains.
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