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Abstract: In the ever-evolving realm of agriculture, the quest for optimal crop yield is a constant challenge, intertwined with the intricate dance of environmental variables and agricultural practices. This research embarks on a journey to revolutionize the approach to fertilizer utilization, a critical factor in the cultivation equation. Leveraging the power of machine learning, specifically an advanced iteration of the random forest algorithm, the study endeavours to usher in a new era of precision agriculture. By delving into the nuanced interplay of time-series data encapsulating rainfall patterns and crop fertility, the research not only predicts nutrient requirements but also refines the predictive model using cutting-edge ensemble learning methods—XGBoost, AdaBoost, and LightGBM. The culmination of these efforts results in a sophisticated yet accessible tool, housed within a Flask Python framework, providing farmers with personalized nutrient recommendations upon inputting crop and location data. This transformative model stands as a beacon of sustainable agriculture, offering a potent means to optimize fertilization, reduce environmental impact, and bolster soil fertility. As the agricultural landscape embraces technological innovation, this research emerges as a pioneering force, empowering farmers with data-driven insights to navigate the complex tapestry of modern farming practices
Kshitij Koyande (Tue,) studied this question.