Evolutionary feature optimization for plant leaf disease detection by deep neural networks

Jalal Sadoon Hameed Albayati*, Burak Berk Üstündağ

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Makalebilirkişi

36 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)12-23
Sayfa sayısı12
DergiInternational Journal of Computational Intelligence Systems
Hacim13
Basın numarası1
DOI'lar
Yayın durumuYayınlandı - Oca 2020

Bibliyografik not

Publisher Copyright:
© 2020 The Authors.

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