Soybean Disease Detection and Segmentation Based on Mask-RCNN Algorithm

Abstract

Anthracnose, frogeye leaf spot (FLS), rhizoctonia aerial blight (RAB), soybean mosaic virus (SMV), and yellow mosaic virus (YMV) of soybean are major common soybean leaf diseases that seriously affect soybean yield in India. However, the existing system needs a real-time detection method for soybean leaf diseases, which will help to take appropriate action for disease cure with minimum losses. This study studied a real-time detector for soybean leaf diseases based on deep convolutional neural networks. The 3,127 RGB images of disease-free leaves, anthracnose, FLS, RAB, SMV, and YMV-affected leaves of soybean were collected from the agriculture fields. The Mask R-CNN detection algorithm was used for the detection of soybean leaf diseases by introducing the Res Net 50 module. The pre-processed images (512×512 pixels) were used as input in Mask R-CNN. The model was trained at a number of epochs, training step per epoch training & validation, and learning rate were 80, 500, 50, 8, and 0.001, respectively. The detection accuracy was calculated at three levels of minimum detection confidence i.e. 0.80, 0.85, and 0.90. The results indicate that the maximum detection accuracy i.e. greater than 85% at 0.90 level of minimum detection confidence. This research indicates that the real-time detector based on deep learning provides a feasible solution for diagnosing soybean leaf diseases and provides guidance for the detection of other plant diseases. In addition to that the application of pesticide in the early stage reduce the use of pesticide resulting in less environmental pollution.