Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks

DOI

10.2991/ijcis.d.200108.001How to use a DOI?

Keywords

Apple leaf disease detection; PDDS; DNN; GOA; SURF; Accuracy

Abstract

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.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Jalal Sadoon Hameed Al-bayati
AU  - Burak Berk Üstündağ
PY  - 2020
DA  - 2020/01/13
TI  - Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
JO  - International Journal of Computational Intelligence Systems
SP  - 12
EP  - 23
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200108.001
DO  - 10.2991/ijcis.d.200108.001
ID  - Al-bayati2020
ER  -