Cascade Region Proposal Network (CRPN) Based Segmentation And Convolutional Deep Belief Network (CDBN) For Turmeric Plant Disease Detection
Keywords:
Cascade Region Proposal Network (CRPN), Convolutional Deep Belief Network (CDBN), Gray Level Co-Occurrence Matrix (GLCM), Convolutional Restricted Boltzmann Machines (CRBMs), Convolutional Neural Network (CNN)Abstract
Agriculture is one of the fundamental elements of human civilization. Crops and plant leaves are susceptible to many illnesses when grown for agricultural purposes. There may be less possibility of further harm to the plants if the illnesses are identified and classified accurately and early on. Early detection and recognition of plant disease is a prerequisite for controlling plant disease, and one of the key steps is to segment plant diseased leaf images. In this paper, Cascade Region Proposal Network (CRPN) is introduced which adopts multiple stages to mine hard samples while extracting region proposals and learn stronger classifiers. Meanwhile, a feature chain and a score chain are proposed to help learning more discriminative representations for proposals. Moreover, a loss function of cascade stages is designed to train cascade classifiers through backpropagation. Once the diseases are segmented and then it is classified using Convolutional Deep Belief Network (CDBN) is a network model that consists of a CRBM. Experimental results show the effectiveness of the proposed model in terms of precision (P), recall (R), accuracy (A) and f-measure (F1).
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