Plant Disease Detection Project Guide

Agricultural productivity is greatly affected by plant diseases, which can cause severe losses if not detected early. Traditional disease identification methods are manual, time-consuming, and require expert intervention. By applying deep learning to plant leaf images, we can automate disease diagnosis at scale, enabling faster interventions and reducing crop damage. This project empowers smart farming and supports global food security initiatives with cutting-edge technology.

Convolutional Neural Networks (CNNs) are powerful tools for image-based classification problems. By training a CNN model on labeled leaf images of healthy and diseased plants, we can accurately identify diseases like powdery mildew, bacterial spots, and leaf blight. Transfer learning using pre-trained models like MobileNet or DenseNet further improves accuracy and reduces the need for extensive datasets. Data augmentation techniques make the model robust to different lighting conditions and leaf orientations.

The system collects plant leaf images, preprocesses them, and uses a trained CNN model to classify images into healthy or specific disease categories. Data augmentation techniques like random rotations, zooms, and flips improve model generalization across diverse field conditions. Farmers can simply upload a photo of a plant leaf to a web app and get instant feedback on plant health, along with disease classification and suggested remedies.

• Collect labeled datasets like PlantVillage containing healthy and diseased leaf images across multiple plant species.
• Preprocess images: resize, normalize, and apply data augmentation techniques like rotation, shift, and zoom.
• Train a CNN model or fine-tune a pre-trained architecture like MobileNet, ResNet, or EfficientNet.
• Evaluate model performance using metrics like accuracy, precision, recall, and confusion matrices.
• Deploy the model into a mobile or web application for farmers to diagnose plant health easily and quickly.

• PlantVillage Dataset
• TensorFlow Image Classification Guide
• Keras Transfer Learning Tutorials