Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks

• Published in: Materials today : proceedings Vol. 51; pp. 480 - 487
• Main Authors: Ashwinkumar, S., Rajagopal, S., Manimaran, V., Jegajothi, B.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-01-2022
• Subjects: Computer vision; Deep learning; Image processing; Intelligent models; MobileNet; Parameter tuning; Plant leaf disease; Deep learning; Computer vision; MobileNet; Image processing; Intelligent models; Parameter tuning; Plant leaf disease
• ISSN: 2214-7853; 2214-7853
• DOI: 10.1016/j.matpr.2021.05.584

Agriculture is the major occupation in India and it loses 35% of the crop productivity annually owing to plant diseases. Earlier plant disease detection is a tedious process because of improper laboratory facilities and expert knowledge. Automated plant disease detection techniques are advantageous for reducing the laborious task of monitoring large crop farms and for identifying disease symptoms early on, i.e., when they appear on plant leaves. Recent advances in computer vision and deep learning (DL) models have demonstrated the value of developing automatic plant disease detection models based on visible symptoms on leaves. With this in mind, this article proposes an automated model for detecting and classifying plant leaf diseases using an optimal mobile network-based convolutional neural network (OMNCNN). The proposed OMNCNN model operates on different stages namely preprocessing, segmentation, feature extraction, and classification. It involves bilateral filtering (BF) based preprocessing and Kapur’s thresholding based image segmentation to identify the affected portions of the leaf image. In addition, the MobileNet model is applied as a feature extraction technique in which the hyperparameters are optimized by the use of emperor penguin optimizer (EPO) algorithm to enhance the plant disease detection rate. Finally, extreme learning machine (ELM) based classifier is utilized to allocate proper class labels to the applied plant leaf images. An extensive set of simulations were performed to highlight the superior performance of the OMNCNN model. The experimental outcome has shown promising results of the OMNCNN model over the recent state-of-art methods with the maximum precision of 0.985, recall of 0.9892, accuracy of 0.987, F-score of 0.985, and kappa of 0. 985.