Big bang–big crunch-CNN: an optimized approach towards rice crop protection and disease detection. (20th January 2022)

• Record Type: Journal Article
• Authors: Sharma, Rahul; Singh, Amar
• Is Part Of: Archives of phytopathology and plant protection. Volume 55:Number 2(2022)
• Page Start: 143
• Page End: 161
• Publication Date: 2022-01-20
• Subjects: Big bang–big crunch -- CNN -- computer vision -- crop protection -- deep learning -- plant disease detection; Plants, Protection of -- Periodicals; Plant diseases -- Periodicals; 632
• DOI: 10.1080/03235408.2021.2003054
• Languages: English
• ISSNs: 0323-5408

Abstract

ABSTRACT: Convolutional neural networks (CNNs) are successfully used for solving image classification tasks. Manually designing efficient neural network architecture requires expertise in CNN and domain knowledge. In this paper, a novel approach is proposed for automatically evolving CNN architecture using big bang–big crunch (BB–BC) algorithm. The proposed approach can be used to automatically evolve CNN architecture and obtain tuned hyper-parameters for CNN. The CNN architecture is validated using the 5932 on-field rice leaf images infected with bacterial leaf blight, rice blast, brown leaf spot and tungro diseases. The proposed approach performed better than CNN evolved using genetic algorithm, SVM, KNN, decision tree, random forest with the test accuracy of 98.7%. The experimental results illustrate that the BB–BC CNN uses fewer trainable parameters compared to popular pre-trained CNN models like ResNet50, MobileNetV2, VGG16, VGG19, InceptionV3, etc.