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Институт Проблем Машиноведения РАН ( ИПМаш РАН ) Институт Проблем Машиноведения РАН ( ИПМаш РАН )

Institute for Problems in Mechanical Engineering
of the Russian Academy of Sciences

Institute for Problems in Mechanical Engineering of the Russian Academy of Sciences

Machine learning for crop yield forecasting

Autors:
Bolotbek Biibosunov , Baratbek Sabitov , Saltanat Biibosunova , Zhamin Sheishenov , Sharshenbek Zhusupkeldiev , Zhyldyz Mamadalieva ,
Pages:
174-181
Annotation:

Amid the persistent rise in global population, there has been a heightened focus on food security by academia, governmental initiatives, and international endeavors. Food security serves as a critical pillar in the national security framework, contributing to a nation’s sovereignty and self-sufficiency in food supply. To fulfill global requirements for essential food items, there is an imperative need to enhance agricultural efficiency across countries. Concurrently, agricultural practices must align with contemporary quality standards and meet consumer needs, drawing upon an integrated approach to crop cultivation technologies and yield classifications. Methodologies and tools for yield augmentation, grounded in scientific advancements in predictive modeling, are of paramount importance. Investigating the plethora of variables that contribute to optimal crop development, which in turn influences yield, poses significant challenges. Comprehensive inquiries that incorporate cutting-edge scientific and technological methodologies are essential for creating precise yield forecasts. The evolving landscape of yield modeling and prediction has emerged as a technologically sophisticated domain. Advanced methods such as machine learning and deep learning offer robust platforms for addressing crop yield forecasting, particularly when coupled with extensive datasets on environmental variables. A growing body of literature suggests the promising role of computational technologies and machine learning paradigms, inclusive of various forms of remote sensing data, in fine-tuning yield models. Yield prediction models are often characterized by intricate nonlinear equations influenced by a range of factors: seed quality and diversity, soil attributes, climatic variables, fertilizer usage, and other agronomic practices. The impacts of these variables on crop yield are varied, with some exerting greater influence than others. Additionally, crop yield is susceptible to adverse environmental and climatic conditions. While there exists a rich corpus of research on yield forecasting, addressing this issue remains an exigent priority in the agricultural sector.

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