Deep learning methods for the plant disease detection platform

Abstract

We introduce the Plant Disease Detection Platform (PDDP) that allows users to send photos of sick plant leaves or textual descriptions of their appearance to obtain information about an infection that hit the vegetation and treatment tips. The backend of the platform in terms of deep learning includes image classification and text similarity models. The image classification model has two parts: feature extractor and classifier. The feature extractor was trained using the triplet loss function along with transfer learning when the weights of the network are initialized from the MobileNetV2 pretrained on the ImageNet dataset. The classifier is a simple multilayer perceptron. The test on 100 random plant images from the Internet shows 98% of the classification accuracy. We did the post-training static quantization in order to reduce the overall model size and increase the inference performance. The final model has a size of 7 Mb and works 5 times faster than the initial model without significant loss of accuracy. The text similarity model is a BERT-based transformer for obtaining vector representation of input texts for further similarity calculation between user requests and disease descriptions on the PDDP.