An automatic classification and early disease detection technique for herbs plant

• Published in: Computers & electrical engineering 2022-05, Vol.100, p.108026, Article 108026
• Main Authors: Sathiya, V., Josephine, M.S., Jeyabalaraja, V.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Automatic classification; CDD-H-HSC; Classification; Classifiers; Disease; Disease segmentation; FDB-DNN; Feature extraction; Feature selection; Global economy; Herbs; ICVSO; Image segmentation; MSCO; Optimization; Performance evaluation; Quality standards; Soft computing
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2022.108026

•This work uses a hybrid soft computing technique at each stage to classify and detection of herbs plant leaf disease.•Multi-swarm coyote optimization (MSCO) algorithm is used for segmentation.•For feature extraction an improved Chan-Vese snake optimization (ICVSO) algorithm helps to select best optimal among multiple features which reduces the dimensionality problem.•Fitness-distance balance deep neural network (FDB-DNN) classifier used to classify the leaf. Agriculture sector plays a major role for the growth of global economy. This is one of the reasons why disease detection in herbs plants is vital in the agricultural area, as disease in them are naturally occurring phenomenon. If sufficient precautions are not taken in this sector, it can have major consequences for plants, affecting quality standards, availability, and production. Hence the identification of non-pathogenic and non-pathogenic characteristics among plants will enrich the global plant production industry and the pharmaceutical industry. However, the current detection methodologies identify the type of herbs but which not suitable for real-time scenario because subject to prediction errors. This study proposes an automatic classification and early disease detection technique for herbs plant using hybrid soft computing techniques (CDD-H-HSC). First, a multi-swarm coyote optimization (MSCO) algorithm is used for disease segmentation which separate diseases area from input plant leaf image. Second, improved Chan-Vese snake optimization (ICVSO) algorithm is applied to optimize the features to select best optimal among multiple features which reduces the dimensionality problem. Then, a hybrid soft computing technique is developed i.e. fitness-distance balance deep neural network (FDB-DNN) classifier classifies the leaf and detect the disease. Finally, to evaluate the performance of proposed CDD-H-HSC technique with standard benchmark datasets. Then, the performance of proposed FDB-DNN classifier is compared with the existing state-of-art classifiers in terms of accuracy, precision, recall and F-measure. [Display omitted]