Crop diseases significantly threaten agricultural productivity, leading to unstable food supply and economic losses. The current approaches to automated crop disease recognition face challenges such as limited datasets, restricted coverage of disease types, and inefficient feature extraction, which hinder their generalization across diverse crops and disease patterns. To address these challenges, we propose an efficient data augmentation method to enhance the performance of deep learning models for crop disease recognition. By constructing a new large-scale dataset comprising 24 different classes, including both fruit and leaf samples, we intend to handle a variety of disease patterns and improve model generalization capabilities. Geometric transformations and color space augmentation techniques are applied to validate the efficiency of deep learning models, specifically convolution and transformer models, in recognizing multiple crop diseases. The experimental results show that these augmentation techniques improve classification accuracy, achieving F1 scores exceeding 98%. Feature map analysis further confirms that the models effectively capture key disease characteristics. This study underscores the importance of data augmentation in developing automated, energy-efficient, and environmentally sustainable crop disease detection solutions, contributing to more sustainable agricultural practices.

Plant diseases are a major cause of reduction in agricultural output, which leads to severe economic losses and unstable food supply. The citrus plant is an economically important fruit crop grown and produced worldwide. However, citrus plants are easily affected by various factors, such as climate change, pests, and diseases, resulting in reduced yield and quality. Advances in computer vision in recent years have been widely used for plant disease detection and classification, providing opportunities for early disease detection, and resulting in improvements in agriculture. Particularly, the early and accurate detection of citrus diseases, which are vulnerable to pests, is very important to prevent the spread of pests and reduce crop damage. Research on citrus pest disease is ongoing, but it is difficult to apply research results to cultivation owing to a lack of datasets for research and limited types of pests. In this study, we built a dataset by self-collecting a total of 20,000 citrus pest images, including fruits and leaves, from actual cultivation sites. The constructed dataset was trained, verified, and tested using a model that had undergone five transfer learning steps. All models used in the experiment had an average accuracy of 97% or more and an average f1 score of 96% or more. We built a web application server using the EfficientNet-b0 model, which exhibited the best performance among the five learning models. The built web application tested citrus pest disease using image samples collected from websites other than the self-collected image samples and prepared data, and both samples correctly classified the disease. The citrus pest automatic diagnosis web system using the model proposed in this study plays a useful auxiliary role in recognizing and classifying citrus diseases. This can, in turn, help improve the overall quality of citrus fruits.