LEAF DISEASE PREDICTION

Abstract

Plant leaf diseases remain a major challenge in modern agriculture, often leading to severe yield losses and threatening food security. Traditional disease detection methods rely heavily on manual inspection by experts, which is time-consuming, labor-intensive, and prone to human error. To address these limitations, this study proposes a deep learning–based framework for automated leaf disease prediction using convolutional neural networks (CNNs). The research employs a curated dataset of healthy and diseased leaf images, encompassing multiple crop species and disease categories. Preprocessing steps such as image resizing, normalization, and augmentation are applied to enhance data diversity and reduce over fitting. The CNN architecture is designed to capture hierarchical features, including texture, color variations, and lesion patterns, enabling robust classification across diverse conditions. Experimental evaluation demonstrates that the proposed model achieves high accuracy, precision, and recall compared to traditional machine learning approaches. The system’s scalability allows integration into mobile and edge devices, making real-time disease monitoring feasible for farmers in resource-constrained environments. Beyond classification, the framework provides interpretability through visualization of feature maps, offering insights into the model’s decision-making process. This work contributes to precision agriculture by presenting a reliable, efficient, and cost-effective solution for early disease detection. By empowering farmers with timely information, the model supports proactive interventions, reduces dependency on chemical treatments, and promotes sustainable crop management practices. The findings highlight the transformative potential of artificial intelligence in agriculture, bridging the gap between cutting-edge technology and practical farming applications.