An Artificial Intelligence System for Apple Fruit Disease Classification Based on Support Vector Machine and Cockroach Swarm Optimization

Abstract

This paper presents an artificial intelligent system for detection of apple diseases using Support Vector Machine (SVM) and Cockroach Swarm Optimization (CSO). This paper faces a challenge of content ambiguity. This challenge is mitigated by combining texture classification based on initial k-means clustering. The proposed system is able to extract useful features. There are 4 classes: scab disease, rot disease, blotch disease, and normal. The dataset consists of 320 apple images in total divided into 80 images for each class. SVM is actually a binary classifier. The paper shows how to extend it for multi-class classification. The performance of SVM strongly depends on proper choice of its parameters. CSO meta-heuristic performs fine-tunes for SVM parameters including nonlinear hyper planes, penalty parameter of error term, and degree which is used to find the hyper plane for splitting the data. Results show 94.65% accuracy in detecting the normal apple.

Original language	English
Title of host publication	Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020
Editors	Aboul-Ella Hassanien, Ahmad Taher Azar, Tarek Gaber, Diego Oliva, Fahmy M. Tolba
Publisher	Springer Paris
Pages	137-147
Number of pages	11
ISBN (Print)	9783030442880
DOIs	https://doi.org/10.1007/978-3-030-44289-7_14
Publication status	Published - 2020
Externally published	Yes
Event	1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020 - Cairo, Egypt Duration: 8 Apr 2020 → 10 Apr 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1153 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020
Country	Egypt
City	Cairo
Period	8.4.20 → 10.4.20

Keywords

Cockroach Swarm Optimization
K-means clustering
Support Vector Machine
Texture classification

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science(all)

Access to Document

10.1007/978-3-030-44289-7_14

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Cite this

El-dosuky, M. A., Oliva, D., & Hassanien, A. E. (2020). An Artificial Intelligence System for Apple Fruit Disease Classification Based on Support Vector Machine and Cockroach Swarm Optimization. In A-E. Hassanien, A. T. Azar, T. Gaber, D. Oliva, & F. M. Tolba (Eds.), Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020 (pp. 137-147). (Advances in Intelligent Systems and Computing; Vol. 1153 AISC). Springer Paris. https://doi.org/10.1007/978-3-030-44289-7_14

An Artificial Intelligence System for Apple Fruit Disease Classification Based on Support Vector Machine and Cockroach Swarm Optimization. / El-dosuky, Mohamed A.; Oliva, Diego; Hassanien, Aboul Ella.

Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. ed. / Aboul-Ella Hassanien; Ahmad Taher Azar; Tarek Gaber; Diego Oliva; Fahmy M. Tolba. Springer Paris, 2020. p. 137-147 (Advances in Intelligent Systems and Computing; Vol. 1153 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

El-dosuky, MA, Oliva, D & Hassanien, AE 2020, An Artificial Intelligence System for Apple Fruit Disease Classification Based on Support Vector Machine and Cockroach Swarm Optimization. in A-E Hassanien, AT Azar, T Gaber, D Oliva & FM Tolba (eds), Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. Advances in Intelligent Systems and Computing, vol. 1153 AISC, Springer Paris, pp. 137-147, 1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020, Cairo, Egypt, 8.4.20. https://doi.org/10.1007/978-3-030-44289-7_14

El-dosuky MA, Oliva D, Hassanien AE. An Artificial Intelligence System for Apple Fruit Disease Classification Based on Support Vector Machine and Cockroach Swarm Optimization. In Hassanien A-E, Azar AT, Gaber T, Oliva D, Tolba FM, editors, Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. Springer Paris. 2020. p. 137-147. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-44289-7_14

El-dosuky, Mohamed A. ; Oliva, Diego ; Hassanien, Aboul Ella. / An Artificial Intelligence System for Apple Fruit Disease Classification Based on Support Vector Machine and Cockroach Swarm Optimization. Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. editor / Aboul-Ella Hassanien ; Ahmad Taher Azar ; Tarek Gaber ; Diego Oliva ; Fahmy M. Tolba. Springer Paris, 2020. pp. 137-147 (Advances in Intelligent Systems and Computing).

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AB - This paper presents an artificial intelligent system for detection of apple diseases using Support Vector Machine (SVM) and Cockroach Swarm Optimization (CSO). This paper faces a challenge of content ambiguity. This challenge is mitigated by combining texture classification based on initial k-means clustering. The proposed system is able to extract useful features. There are 4 classes: scab disease, rot disease, blotch disease, and normal. The dataset consists of 320 apple images in total divided into 80 images for each class. SVM is actually a binary classifier. The paper shows how to extend it for multi-class classification. The performance of SVM strongly depends on proper choice of its parameters. CSO meta-heuristic performs fine-tunes for SVM parameters including nonlinear hyper planes, penalty parameter of error term, and degree which is used to find the hyper plane for splitting the data. Results show 94.65% accuracy in detecting the normal apple.

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