Plant Disease Classification Based on ConvLSTM U-Net with Fully Connected Convolutional Layers

Abstract

Plants are susceptible to a variety of illnesses throughout their growth stages. One of the trickiest issues in agriculture is the early diagnosis of plant diseases. The entire output may be negatively impacted by infections if they are not discovered early on, which would lower farmers' profitability. Numerous researchers have proposed numerous cutting-edge solutions based on Deep Learning and Machine Learning techniques to address this issue. However, the majority of these systems either has poor classification accuracy rates or utilizes millions of training parameters. In this research, a novel model using ConvLSTM U Net-based automatic detection of plant disease is proposed. To the best of our knowledge, no state-of-the-art systems described in the literature have a hybrid system based on CAE and CNN to automatically identify plant diseases. The proposed model employed in this study is to identify the presence of Bacterial Spot disease in medicinal plants using the image of their leaves, but it may be extended to identifying any plant disease. The work conducted for this research employ a dataset that is readily accessible to get images of medicinal plant leaves. In comparison to previous methods described in the literature, the proposed ConLSTM U-Net model requires for less training parameters. As a consequence of this, the amount of time necessary to train the model for automatic plant disease detection and the amount of time required to diagnose the disease in plants using the trained model are both significantly decreased.