Application of Region-Based Convolutional Neural Network for Prompt Segmentation between Infected Cucumber Leaves and Healthy Cucumber Leaves

Abstract

Plant diseased leaf image segmentation plays an important role in the plant disease detection through leaf symptoms, and early separation of infected and healthy plant leaves from each other can prevent horticulture loss. To achieve this goal, Region-Based Convolutional Neural Network (R-CNN) for prompt segmentation between infected cucumber leaves and healthy cucumber leaves was proposed and applied. A whole color cucumber leaf image is inputted into the convolutional neural network of the Mask R-CNN model, thereafter, the extracted features in their map are passed to the region proposal network for region proposals, the proposed regions of interest in their unaligned form are aligned before passing them to the fully connected layers in a fixed size feature map for the following actions: (1) bounding boxing, classification, and masking. The experimental results obtained in this work are on a par with the results obtained in literature, which demonstrates the effectiveness of the proposed method and high practical value for plant growth monitoring.