Self‐adaptive‐deer hunting optimization‐based optimal weighted features and hybrid classifier for automated disease detection in plant leaves. Issue 7 (9th March 2022)

• Record Type: Journal Article
• Authors: Sahu, Kalicharan; Minz, Sonajharia
• Other Names: Fernandes Steven Lawrence guestEditor.; Martis Roshan Joy guestEditor.; Lin Hong guestEditor.; Javadi Bahman guestEditor.; Tanik Urcun John guestEditor.; Sharif Muhammad guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-09
• Subjects: automated plants disease detection -- computer vision methods -- convolutional neural network -- grey‐level co‐occurrence matrix -- K‐means clustering -- local binary pattern -- optimal weighted feature extraction -- self‐adaptive deer hunting optimization -- support vector machine; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12982
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: This main intention of this paper is to adopt a new disease detection model for plant leaves. The proposed model involves several steps such as pre‐processing, leaf segmentation, abnormality segmentation, feature extraction and detection. Image scaling and contrast enhancement are performed during the pre‐processing phase. Once the pre‐processing is done, the segmentation phase starts with leaf segmentation by binary thresholding method and abnormality segmentation by K‐means clustering. Further, the local binary pattern and grey‐level co‐occurrence matrix features are extracted and a dimensionality reduced technique called principle component analysis is determined. As the main novelty, the weighted feature extraction is performed, in which the weight functions are optimized by the self‐adaptive deer hunting optimization (SA‐DHOA). Another contribution of this paper is to implement a hybrid classifier for disease detection. Here, the extracted weighted features are subjected to a support vector machine, and the abnormality segmented image is subjected to convolutional neural network (CNN), which is a deep learning algorithm that can learn the features automatically. Here, the same SA‐DHOA is used to improve the performance of CNN, which is termed as SA‐DHOA‐SCNN. Finally, the performance analysis confirms the maximum success rate of the proposed model over other conventional methods.