TY - GEN
T1 - An Artificial Intelligence System for Apple Fruit Disease Classification Based on Support Vector Machine and Cockroach Swarm Optimization
AU - El-dosuky, Mohamed A.
AU - Oliva, Diego
AU - Hassanien, Aboul Ella
PY - 2020
Y1 - 2020
N2 - 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.
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.
KW - Cockroach Swarm Optimization
KW - K-means clustering
KW - Support Vector Machine
KW - Texture classification
UR - http://www.scopus.com/inward/record.url?scp=85082984373&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082984373&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-44289-7_14
DO - 10.1007/978-3-030-44289-7_14
M3 - Conference contribution
AN - SCOPUS:85082984373
SN - 9783030442880
T3 - Advances in Intelligent Systems and Computing
SP - 137
EP - 147
BT - Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020
A2 - Hassanien, Aboul-Ella
A2 - Azar, Ahmad Taher
A2 - Gaber, Tarek
A2 - Oliva, Diego
A2 - Tolba, Fahmy M.
PB - Springer Paris
T2 - 1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020
Y2 - 8 April 2020 through 10 April 2020
ER -