Smart Crop Advisory System

Agriculture faces persistent challenges such as delayed crop disease identification, improper fertilizer usage, unsuitable crop selection, and unreliable yield estimation, which collectively reduce productivity and increase financial risk for farmers. To address these issues, this paper presents a Smart Farming Assistant, an intelligent decision-support system that integrates Machine Learning and Deep Learning techniques to enhance precision agriculture. The proposed system performs automated crop disease detection using Convolutional Neural Networks by analyzing leaf images, enabling early and accurate diagnosis. It also provides fertilizer recommendations based on essential soil parameters such as nitrogen, phosphorus, potassium, pH, and moisture levels, ensuring optimal nutrient management. In addition, the system suggests the most suitable crop for given soil and environmental conditions using classification models, while crop yield is predicted through regression-based machine learning algorithms. The complete solution is deployed as a user- friendly web application that allows farmers to access real-time agricultural insights with minimal technical knowledge. Experimental evaluation demonstrates that the system delivers reliable predictions and actionable recommendations, supporting sustainable farming practices, reducing crop losses, and improving overall agricultural productivity. This work highlights the potential of AI-driven technologies to transform traditional farming into a smart and data-driven agricultural ecosystem.