AI-Powered Fertilizer Optimization for Sustainable Agriculture: A Soil and Season-based Approach

Traditional agricultural methods involve the unsustainable application of chemicals to the soil, resulting in soil depletion, imbalanced nutrients, and environmental degradation, and farmers have no affordable and instantaneous monitoring of soils to determine their health or otherwise. This research suggests an artificial intelligence-based framework to enhance the efficiency of fertilizers through computer vision, machine learning, and natural language processing (NLP) in sustainable farming. The system assists in the classification of soil images through a Convolutional Neural Network (CNN) of soil types (e.g., alluvial, black, red) and converts the narrated or typed soil parameters (e.g., NPK, pH, moisture) with the help of a rule-based model. It would then give localized fertilizer recommendations and crop production estimates based on a Random Forest Regression model, specific to the seasonal and regional factors. The most prominent results comprise an accuracy of 92.4% in soil identification, transcription of 94 % of speech into text, and prediction of crop yields with an error level of 5 % points. It will be multilingual (offering support in Hindi, Punjabi, and English) and voice-enabled to reach, including rural farmers. The system eliminates the overuse of fertilizer and lowers costs, and reduces effects on the environment by having precision agriculture. The experimental validation demonstrates its practicality, that is, it provides a scalable solution to enhancing the efficiency and productivity of Indian agriculture.