TY - GEN
T1 - Machine Learning for Apple Fruit Diseases Classification System
AU - Abd El-aziz, Atrab A.
AU - Darwish, Ashraf
AU - Oliva, Diego
AU - Hassanien, Aboul Ella
PY - 2020
Y1 - 2020
N2 - There are a growing demand and an urgent need for fruits due to the increase in the world population. Fruit diseases cause a devastating problem in production losses worldwide. The healthy recognition of fruits and apples is an important issue for the economic and agricultural fields. In this paper, a recognition system for apple fruit diseases detection is proposed and experimentally validated. The K-Means based segmentation technique is applied. In regards to performance enhancement, different features extraction techniques are applied and classified using Support Vector Machine, K-NN, Multi-Class Support Vector Machine, and Multi-Label KNN (ML-KNN). The proposed model can significantly support accurate detection and automatic classification of apple fruit diseases. The average accuracy of diseases classification is achieved up to 97.5% and up to 99% for apple health classification.
AB - There are a growing demand and an urgent need for fruits due to the increase in the world population. Fruit diseases cause a devastating problem in production losses worldwide. The healthy recognition of fruits and apples is an important issue for the economic and agricultural fields. In this paper, a recognition system for apple fruit diseases detection is proposed and experimentally validated. The K-Means based segmentation technique is applied. In regards to performance enhancement, different features extraction techniques are applied and classified using Support Vector Machine, K-NN, Multi-Class Support Vector Machine, and Multi-Label KNN (ML-KNN). The proposed model can significantly support accurate detection and automatic classification of apple fruit diseases. The average accuracy of diseases classification is achieved up to 97.5% and up to 99% for apple health classification.
KW - Agriculture
KW - Fruit
KW - K-means
KW - Multi-Class Support Vector Machine
KW - Multi-Label KNN
UR - http://www.scopus.com/inward/record.url?scp=85082992216&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082992216&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-44289-7_2
DO - 10.1007/978-3-030-44289-7_2
M3 - Conference contribution
AN - SCOPUS:85082992216
SN - 9783030442880
T3 - Advances in Intelligent Systems and Computing
SP - 16
EP - 25
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 -