Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction

Sensors

Prabhjot Kaur¹  Shilpi Harnal¹  Shuchi Upadhyay²  Surbhi Bhatia³  Rajeev Tiwari⁴  Arwa Mashat⁵  Aliaa M. Alabdali⁵ 

[1] Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India;Department of Allied Health Sciences, School of Health Sciences, University of Petroleum and Energy Studies, Bidholi, Dehradun 248007, Uttarakhand, India;Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Hofuf 31982, Saudi Arabia;Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Bidholi, Dehradun 248007, Uttarakhand, India;Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia;

关键词: convolutional neural network;    image classification;    transfer learning;    EfficientNet B7;    leaf disease detection;    plant disease;   

DOI  :  10.3390/s22020575

来源: DOAJ

【 摘 要 】

Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

【 授权许可】

Unknown