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

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 2; p. 575
• Main Authors: Kaur, Prabhjot, Harnal, Shilpi, Tiwari, Rajeev, Upadhyay, Shuchi, Bhatia, Surbhi, Mashat, Arwa, Alabdali, Aliaa M.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2022; MDPI
• Subjects: Accuracy; Algorithms; Classification; convolutional neural network; Crop diseases; Crops; Data mining; Datasets; Deep learning; EfficientNet B7; Fruits; Grapes; image classification; Image processing; Image Processing, Computer-Assisted; leaf disease detection; Machine Learning; Medical diagnosis; Medical imaging equipment; Medical research; Medicine, Experimental; Neural networks; Neural Networks, Computer; Pattern recognition; plant disease; Plant Diseases; Research proposals; transfer learning; India; transfer learning; image classification; plant disease; convolutional neural network; feature reduction and extraction; leaf disease detection; EfficientNet B7
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22020575

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.