AI-Based Smart Crop Disease Detection System Using Machine Learning

Abstract

Agriculture plays a vital role in global food security, yet crop diseases significantly reduce productivity and quality. Traditional disease detection methods rely on manual inspection, which is time-consuming and prone to errors. Previous research focused on traditional image processing and standalone machine learning models, which were limited in handling complex disease patterns and large datasets. This paper proposes an AI-based smart crop disease detection system using machine learning and deep learning techniques. The system utilizes image processing and Convolutional Neural Networks (CNNs) to automatically identify crop diseases from leaf images. The proposed framework enables real-time detection, improves accuracy, and supports farmers in decision-making. Experimental results demonstrate high accuracy and efficiency, making the system suitable for precision agriculture.