SmartCrop-Coffee: A Predictive Agriculture Framework

Abstract: Coffee farming faces challenges such as crop diseases, improper fertilizer usage, climate variability, and limited access to expert guidance. Traditional practices rely on manual observation, leading to delayed disease detection and uncertain yield outcomes. This paper presents SmartCrop-Coffee, an AI-based decision support system for precision coffee farming. The framework integrates deep learning and machine learning to enable coffee leaf disease detection, fertilizer recommendation, coffee variety selection, and yield prediction. A Convolutional Neural Network (CNN) classifies leaf diseases, while Random Forest models support fertilizer, yield, and variety prediction using soil and crop data. Real-time weather data further enhances decision accuracy, supporting sustainable and data-driven coffee agriculture.

Keywords: Precision Agriculture, Coffee Leaf Disease Detection, Machine Learning, CNN, Yield Prediction.