INTELLIGENT CROP RECOMMENDATION AND FERTILIZER PREDICITION

MACHINE LEARNING, COMPUTER ENGINEERING

The crop recommendation module employs machine learning algorithms to analyze soil type, climate conditions, and past crop performance, providing personalized suggestions for suitable crops in a given region. By considering local environmental characteristics, the system enhances the accuracy of its recommendations. This enables the system to forecast the ideal type and quantity of fertilizer needed for a specific crop and field. The user interface is designed for ease of use, ensuring accessibility for farmers with varying levels of technological proficiency. Farmers can input preferences, receive crop recommendations, and access detailed fertilizer prescriptions through a mobile or web-based application. Our Smart Agriculture solution aims to foster sustainable farming practices by promoting efficient resource use, reducing environmental impact, and enhancing overall farm productivity. This research proposes an intelligent system that uses a decision tree algorithm to anticipate fertilizer and recommend crops. To suggest appropriate crops for cultivation, the suggested approach examines several variables, including crop attributes, soil type, and climate. Furthermore, it forecasts the ideal kind and number of fertilizers needed for every suggested crop, supporting sustainable farming methods. The system's decision tree algorithm makes forecasts that are both accurate and efficient, which improves crop productivity and resource use. The field of crop recommendation and fertilizer prediction research aids in sustainable farming, global food security, rural development, and the agriculture sector's overall resilience to changing challenges by providing farmers with intelligent systems that incorporate data-driven insights.

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