Fruit Disease Detection System Using Deep Learning

Abstract Timely identification of fruit diseases is essential for enhancing crop yields and reducing financial setbacks in agriculture. This research introduces an advanced detection system based on deep learning, particularly Convolutional Neural Networks (CNNs), combined with OpenCV and associated libraries. The proposed model is built to detect and classify a variety of fruit diseases by learning visual patterns from high-quality fruit images. The dataset includes both healthy and infected fruits, capturing diverse scenarios. To prepare the data, preprocessing methods like augmentation, resizing, and normalization are implemented via OpenCV to improve dataset quality and variety. A CNN architecture is designed to extract deep visual features and achieve accurate classification. Techniques like transfer learning help optimize the model's accuracy while keeping resource usage efficient. Experimental evaluations show excellent performance in terms of precision, recall, and accuracy, indicating strong applicability in real-world settings. This system significantly outperforms traditional manual approaches, providing scalable and swift decision-making tools for agricultural professionals. It lays the groundwork for integrating intelligent systems into precision agriculture to foster more efficient and eco-friendly farming. Key Words: Fruit Disease Detection, Convolutional Neural Networks, Deep Learning In Agriculture, OpenCV.