Crop Disease Detection from Leaf Images Using Convolutional Neural Networks
Keywords:
Plant disease detection, convolutional neural networks, transfer learning, precision agriculture, PlantVillage, MobileNetV2, EfficientNet, Grad-CAM.Abstract
Every year, between 10–40% of the world's crops are lost to plant diseases, and locating an expert diagnostician is often time-consuming for the smallholder farmer. This paper proposes a Convolutional Neural Network (CNN) based disease identification model, using a single picture of the leaves taken by the smartphone camera. Four pretrained architectures, VGG16, ResNet-50, MobileNetV2 and EfficientNet-B0, are fine-tuned on the public PlantVillage dataset (54,305 images, 14 crops, 38 classes) and evaluated for generalization on real field images from PlantDoc. EfficientNet-B0 model, having only 5.3 M parameters, can attain 99.62% test accuracy on the PlantVillage dataset, and in the end, MobileNetV2 can be deployed in 38ms per image in an analysis with a mid-level Android phone. The results are 41.5% on field images, showing a big gap between the lab and field, and mixed domain fine-tuning with background augmentation gets back to 28.3%, achieving 69.8% accuracy on field images. The shape of the leaf and background are not modelled; Grad-CAM visualizations show that the models pay attention to lesion regions. The results show the capability of lightweight CNNs to provide an accurate, explainable, offline diagnosis of disease suitable for deployment in low-connectivity farming zones.
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