Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
- https://doi.org/10.2991/ijcis.d.200108.001How to use a DOI?
- Apple leaf disease detection, PDDS, DNN, GOA, SURF, Accuracy
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.
- © 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/).
Cite this article
TY - JOUR AU - Jalal Sadoon Hameed Al-bayati AU - Burak Berk Üstündağ PY - 2020 DA - 2020/01 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 - https://doi.org/10.2991/ijcis.d.200108.001 ID - Al-bayati2020 ER -