Plant leaf disease identification using exponential spider monkey optimization

• Published in: Sustainable computing informatics and systems Vol. 28; p. 100283
• Main Authors: Kumar, Sandeep, Sharma, Basudev, Sharma, Vivek Kumar, Sharma, Harish, Bansal, Jagdish Chand
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2020
• Subjects: Feature selection; Nature inspired algorithm; Optimization; Plant disease identification; Spider monkey optimization; Subtractive pixel adjacency model; Spider monkey optimization; Feature selection; Subtractive pixel adjacency model; Optimization; Plant disease identification; Nature inspired algorithm
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2018.10.004

•A new Exponential spider monkey optimization (ESMO) algorithm is proposed for plant leaf disease identification.•The Exponential SMO is compared with the basic SMO, PSO, GSA and DE.•The SPAM method used to extract 686 features for the considered image dataset.•The classification results have been analyzed over SVM, LDA, kNN and ZeroR classifier.•The proposed feature selection technique minimizes the irrelevant and redundant features while maintaining the high classification accuracy. Agriculture is one of the prime sources of economy and a large community is involved in cropping various plants based on the environmental conditions. However, a number of challenges are faced by the farmers including different diseases of plants. The detection and prevention of plant diseases are the serious concern and should be treated well on time for increasing the productivity. Therefore, an automated plant disease detection system can be more beneficial for monitoring the plants. Generally, the most diseases may be detected and classified from the symptoms appeared on the leaves. For the same, extraction of relevant features plays an important role. A number of methods exists to generate high dimensional features to be used in plant disease classification problem such as SPAM, CHEN, LIU, and many more. However, generated features also include unrelated and inessential features that lead to degradation in performance and computational efficiency of a classification problem. Therefore, the choice of notable features from the high dimensional feature set is required to increase the computational efficiency and accuracy of a classifier. This paper introduces a novel exponential spider monkey optimization which is employed to fix the significant features from high dimensional set of features generated by SPAM. Furthermore, the selected features are fed to support vector machine for classification of plants into diseased plants and healthy plants using some important characteristics of the leaves. The experimental outcomes illustrate that the selected features by Exponential SMO effectively increase the classification reliability of the classifier in comparison to the considered feature selection approaches.