Deep neural network-driven Nitrogen fertilizer recommendation: A machine learning-based method for paddy soil and crop analysis using leaf imaging

This paper discusses a novel Machine Learning (ML) algorithm that leverages leaf images from rice crops to accurately determine soil nitrogen levels, aiming to optimize fertilizer usage. Utilizing the OpenCV package for image enhancement under controlled lighting, the model employs linear regression to establish a quantifiable correlation between leaf color and soil nitrogen content, achieving a prediction accuracy of specific accuracy percentage or metric. Unlike traditional methods, which are often costly and time-consuming without considering dynamic agricultural factors like crop variety and soil quality, our approach proposes a real-time, cost-effective solution. This research not only demonstrates the potential to increase agricultural sustainability and yield through precise fertilizer application but also paves the way for future research encompassing a broader spectrum of crops and soil properties. The proposed system provides farmers with an intuitive digital platform for nitrogen level assessment, facilitating targeted fertilizer application. By integrating camera-assisted soil health evaluation, the program promotes environmentally sustainable farming practices. Our findings indicate that ML-guided nitrogen fertilization can significantly enhance resource utilization efficiency, supporting the increasing global food demand. The implementation of this ML algorithm has shown to improve fertilizer application recommendations by with an accuracy of 80 percentage and low R2, RMSE and MAE Values, thereby reducing environmental impact and supporting sustainable agricultural development.