Leaf Disease Prediction Using Deep Learning

Abstract

Leaf diseases in plants pose significant challenges to global agriculture, impacting crop yield, quality, and food security. In response, this study proposes an innovative approach combining Convolutional Neural Networks (CNNs) for leaf disease prediction with recommendations for preventative methods and fertilizer application. A diverse dataset comprising images of healthy and diseased leaves is collected and preprocessed for CNN-based analysis. A custom CNN architecture is designed and trained on the dataset to accurately predict leaf diseases. Furthermore, preventative measures, including cultural practices, biological controls, and chemical treatments, are suggested based on disease characteristics and environmental factors. Additionally, tailored fertilizer recommendations aimed at strengthening plant immunity and resilience against diseases are provided. Experimental evaluations demonstrate the effectiveness of the CNN model in predicting leaf diseases, while the integrated preventative measures and fertilizer strategies aim to reduce disease incidence and severity. This holistic approach contributes to sustainable agriculture by empowering farmers with proactive disease management tools and promoting resilient crop production systems.