Smart Paddy identification and soil suitability prediction using Deep Learning

Abstract

Agriculture plays a vital role in the economy and food security of many countries, with paddy remaining one of the most important staple crops cultivated worldwide. Accurate crop detection and appropriate soil recommendation are essential for improving yield and reducing losses. Traditional farming practices rely heavily on farmer experience and manual observation, often leading to inefficient resource usage and reduced productivity. Artificial Intelligence provides advanced solutions for smart agriculture by enabling automated analysis of crop and soil conditions. This project focuses on paddy crop detection and soil suitability prediction using deep learning techniques. Image processing and machine-learning models are employed to detect paddy crop conditions, while soil parameters are analyzed to assess fertility and compatibility. Data collected from sensors and crop images is processed by AI models to generate accurate predictions, supporting early detection of crop issues and helping farmers make informed decisions. The system recommends suitable soil treatments and fertilizers, reduces manual effort through automation, and improves both crop yield and soil health. The proposed solution is scalable, cost-effective, and promotes sustainable farming practices, demonstrating the effectiveness of AI-based approaches in agriculture.