INTERNATIONAL JOURNAL FOR INNOVATIVE RESEARCH IN MULTIDISCIPLINARY FIELD

Abstract: Agriculture faces increasing pressure to meet global food demands while coping with limited resources and environmental challenges. Machine Learning (ML) and data-driven techniques to enable smarter, more sustainable farming practices. By integrating data on soil conditions, environmental factors, weather, and historical crop performance, the study develops predictive models to forecast crop yield and optimize resource usage such as water, fertilizers, and nutrients. The research aims at  integrating  these Agriculture must dramatically increase productivity to feed a growing global population with minimal land expansion. This challenge is compounded by climate change, soil degradation, and water scarcity. Traditional forecasting methods (e.g. statistical models, time-series) struggle to model complex interactions among weather, soil, genetics, and management. In contrast, ML techniques like Random Forest, SVM, and neural networks excel at handling nonlinearity and high-dimensional data. Recent advances in sensor networks, satellite and drone imagery, and IoT enable the collection of rich, real-time data. climate change, weather variability, soil quality, genetics of seeds, and crop management practices. Crop yield variation is brought by intricate GxExM interactions of genetics, environment and farm management practices involving soil type, weather conditions, seed genetics and farmer farm management decision [1]. Knowledge of technologies with the Random Forest classifier to maximize crop productivity, optimize resource allocation, enhance crop health monitoring, automate farming operations, predict weather effects, ensure sustainable practices, and predict crop pricing. The research tackles issues such as climate change, resource shortages, and food insecurity by utilizing sophisticated machine learning, specifically random forests, to contribute towards enhanced global farming practices through intelligent data-driven approaches.