Entropy‐controlled deep features selection framework for grape leaf diseases recognition. Issue 7 (13th May 2020)

• Record Type: Journal Article
• Authors: Adeel, Alishba; Khan, Muhammad Attique; Akram, Tallha; Sharif, Abida; Yasmin, Mussarat; Saba, Tanzila; Javed, Kashif
• Other Names: Fernandes Steven Lawrence guestEditor.; Martis Roshan Joy guestEditor.; Lin Hong guestEditor.; Javadi Bahman guestEditor.; Tanik Urcun John guestEditor.; Sharif Muhammad guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-13
• Subjects: best features selection -- CNN -- feature extraction -- fruit diseases -- fusion; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12569
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Several countries are most reliant on agriculture either in terms of employment opportunities, national income, availability of a raw material, food production, to name but a few. However, it faces a big challenge such as climate changes, diseases, pets, weeds etc. Therefore, last decade has provided a machine learning‐based solution to the agricultural community, which helped farmers to identify the diseases at the early stages. In this article, our focus is on grape diseases, and proposes a novel framework to identify and classify the selected diseases at the early stages. A deep learning‐based solution is embedded into a conventional architecture for optimal performance. Three primary steps are involved; (a) feature extraction after applying transfer learning on pre‐trained deep models, AlexNet and ResNet101, (b) selection of best features using proposed Yager Entropy along with Kurtosis (YEaK) technique, (c) fusion of strong features using proposed parallel approach and later subject to classification step using least squared support vector machine (LS‐SVM). The simulations are performed on infected grape leaves obtained from the plant village dataset to achieving an accuracy of 99%. From the simulation results, we sincerely believe that our proposed approach performed exceptionally compared to several existing methods.