Detecting Plant Leaf Diseases Through Image Processing and CNN with Preventive Measures

This paper outlines a thorough strategy for identifying and categorizing plant leaf diseases by utilizing advanced image processing methods alongside Convolutional Neural Networks (CNNs). The proposed system captures high-resolution images of plant leaves and preprocesses them using image processing methods to enhance features and remove noise. We employ a deep learning-based CNN model trained on a large dataset of plant leaf images, covering a variety of diseases such as powdery mildew, leaf spot, and blight. The CNN architecture is designed to extract relevant features for disease classification, achieving high accuracy and reliability. Upon detection of a specific disease, the system provides preventive measures and treatment recommendations. This includes suggestions for organic and chemical-based interventions, cultural practices to minimize disease spread Additionally, the system offers insights into practices such as crop rotation, irrigation scheduling, and soil nutrient management, ensuring a holistic approach to plant disease prevention. Through extensive experimentation and validation, the research demonstrates that the proposed system not only excels in accurately detecting and categorizing diseases but also provides actionable recommendations for agricultural practices to mitigate disease risks and improve overall crop health.