Khan Ihtiram Raza, Sangari M Siva, Shukla Piyush Kumar, Aleryani Aliya, Alqahtani Omar, Alasiry Areej, Alouane M Turki-Hadj
Department of Computer Science, Jamia Hamdard, Delhi 110062, India.
Department of CSE, KPR Institute of Engineering and Technology, Coimbatore 641407, India.
Biomimetics (Basel). 2023 Sep 19;8(5):438. doi: 10.3390/biomimetics8050438.
In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset's mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA's performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%.
近年来，病害侵袭对农业构成了持续威胁，并造成了巨大的经济损失。因此，早期检测和分类可以最大限度地减少病害传播并有助于提高产量。与此同时，深度学习已成为检测和分类图像的重要方法。使用深度学习方法进行的分类主要依赖于大型数据集来防止过拟合问题。为克服上述问题，开发了自动分割和超参数优化人工兔算法（AS-HPOARA）。其目的是提高植物叶片病害分类。使用植物村数据集来评估所提出的AS-HPOARA方法。使用数据集的均值和标准差进行Z分数归一化以对图像进行归一化。在这项工作中使用了三种增强技术来平衡训练图像：旋转、缩放和平移。在分类之前，图像增强减少了过拟合问题并提高了分类准确率。改进的U-Net使用了更多数量的全连接层来更好地表示深层特征；它被用于分割。为了以配对方式将图像从一个域转换到另一个域，分类由基于超参数优化的人工兔算法执行，其中增加了训练数据并消除了统计偏差以提高分类准确率。通过调整减少过拟合问题的超参数来最小化模型复杂度。使用准确率、精确率、召回率和F1分数来分析AS-HPOARA的性能。与现有的CGAN-DenseNet121和RAHC_GAN相比，报告结果表明AS-HPOARA对十类的准确率高达99.7%。
