Wheat Disease Recognition through One-shot Learning using Fields Images

Accurate disease recognition from wheat plants is essential to mitigate the effects and to stop the spread of the diseases. State-of-the-art algorithms have developed through deep learning which recognizes the disease from field images. Although such methods produce highly accurate results, they require tons of labeled images, and work only on the classes which are involved in the training phase. Thus, to get the recognition against the different classes, the model is required to retrain. This article has proposed a wheat disease recognition network based on one-shot learning which not only needs a small number of images for training, but also can accommodate new categories because it can be trained even on few images of a new type. So, farmers can retrain the network just by giving a few images of plants containing the concerned disease, and start testing immediately. We have used the MobileNetv3 network as a feature extractor which is extremely fast and accurate classification. This network is fine-tuned on the PlantVillage dataset, while the last two dense layers are fine-tuned on the plant images of 11 wheat disease, those images are taken from the CGIAR Crop Disease dataset and Google images. The whole One-shot network is trained on 440 images having 40 images of each class. Siamese networks are used for producing the encodings of input images, then the absolute difference is calculated between encodings, and similarity scores are determined through the Sigmoid unit. It assigns 1 score to similar images, while 0 to dissimilar images. Our Mobilenetv3 model has achieved around 98% training and 96% validation, while the whole one-shot network has achieved more than 92% accuracy, 84% precision, and 85 recall. Our proposed system requires just a few images of a new type for training, instead of retraining the network as in standard classification networks.