A novel deep CNN model with entropy coded sine cosine for corn disease classification

• Published in: Journal of King Saud University. Computer and information sciences Vol. 36; no. 7; p. 102126
• Main Authors: Malik, Mehak Mushtaq, Fayyaz, Abdul Muiz, Yasmin, Mussarat, Abdulkadir, Said Jadid, Al-Selwi, Safwan Mahmood, Raza, Mudassar, Waheed, Sadia
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024; Elsevier
• Subjects: Classification; Convolutional neural network; Corn disease; Deep learning; Entropy coded sine cosine algorithm; Deep learning; Corn disease; Convolutional neural network; Entropy coded sine cosine algorithm; Classification
• ISSN: 1319-1578
• DOI: 10.1016/j.jksuci.2024.102126

Corn diseases significantly impact crop yields, posing a major challenge to agricultural productivity. Early and accurate detection of these diseases is crucial for effective management and mitigation. Existing methods, mostly relying on analyzing corn leaves, often lack the precision to identify and classify a wide range of diseases under varying conditions. This study introduces a novel approach to detecting corn diseases using image processing and deep learning techniques, aiming to enhance detection accuracy through pre-processing, improved feature extraction and selection, and classification algorithms. A new deep Convolutional Neural Network (CNN) model named TreeNet, with 35 layers and 38 connections, is proposed. TreeNet is pre-trained using the Plant Village imaging dataset. For image pre-processing, the YCbCr color space is utilized to improve color representation and contrast. Feature extraction is performed using TreeNet and two pre-trained models, Darknet-53, and DenseNet-201, with features fused using a serial-based fusion method. The Entropy-coded Sine Cosine Algorithm is applied for feature selection, optimizing the feature set for classification. The selected features are used to train Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers, with extensive experiments conducted using both 5-fold and 10-fold cross-validation, and feature sizes ranging from 200 to 1150. The proposed method achieves classification accuracy, precision, recall, and F1-score of 99.8%, 99%, 100%, and 99%, respectively, surpassing existing benchmarks. The integration of TreeNet with Darknet-53 and DenseNet-201, along with robust pre-processing and feature selection, significantly improves corn disease detection, highlighting the potential of advanced CNN architectures in agriculture.