TY - JOUR
T1 - Evolutionary feature optimization for plant leaf disease detection by deep neural networks
AU - Albayati, Jalal Sadoon Hameed
AU - Üstündağ, Burak Berk
N1 - Publisher Copyright:
© 2020 The Authors.
PY - 2020/1
Y1 - 2020/1
N2 - Apple leaf disease is the foremost factor that restricts apple yield and quality. Usually, much time is taken for disease detection with the existing diagnostic techniques; therefore, farmers frequently miss the best time for preventing and treating diseases. The detection of apple leaf diseases is a significant research problem, and its main aim is to discover an efficient technique for disease leaf image diagnosis. This article has made an effort to propose a method that can detect the disease of apple plant leaf using deep neural network (DNN). Plant diseases detection system (PDDS) architecture is designed. Speeded up robust feature (SURF) is used for feature extraction and Grasshopper Optimization Algorithm (GOA) for feature optimization, which helps to achieve better detection and classification accuracy. Classification parameters, such as Precision, Recall, F-measure, Error, and Accuracy is computed, and a comparative analysis has been performed to depict the effectiveness of the proposed work.
AB - Apple leaf disease is the foremost factor that restricts apple yield and quality. Usually, much time is taken for disease detection with the existing diagnostic techniques; therefore, farmers frequently miss the best time for preventing and treating diseases. The detection of apple leaf diseases is a significant research problem, and its main aim is to discover an efficient technique for disease leaf image diagnosis. This article has made an effort to propose a method that can detect the disease of apple plant leaf using deep neural network (DNN). Plant diseases detection system (PDDS) architecture is designed. Speeded up robust feature (SURF) is used for feature extraction and Grasshopper Optimization Algorithm (GOA) for feature optimization, which helps to achieve better detection and classification accuracy. Classification parameters, such as Precision, Recall, F-measure, Error, and Accuracy is computed, and a comparative analysis has been performed to depict the effectiveness of the proposed work.
KW - Accuracy
KW - Apple leaf disease detection
KW - DNN
KW - GOA
KW - PDDS
KW - SURF
UR - http://www.scopus.com/inward/record.url?scp=85078696582&partnerID=8YFLogxK
U2 - 10.2991/ijcis.d.200108.001
DO - 10.2991/ijcis.d.200108.001
M3 - Article
AN - SCOPUS:85078696582
SN - 1875-6891
VL - 13
SP - 12
EP - 23
JO - International Journal of Computational Intelligence Systems
JF - International Journal of Computational Intelligence Systems
IS - 1
ER -