Apple Leaf Disease Identification in Complex Background Based on BAM-Net

Abstract

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network’s attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network’s ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops.

Identification of Tea Disease Under Complex Backgrounds Based on Minimalism Neural Network Architecture and Channel Reconstruction Unit

“…It is minimal in size and has a rapid computing speed for the network model. Gao et al [18] suggested a backbone network BAM-Net based on aggregate coordinate attention mechanism (ACAM) and multi-scale feature refinement module (MFRM) to diagnose apple leaf diseases.…”

Classification of Apple Plant Leaf Diseases Using Deep Convolutional Neural Network

“…Notably, BAM-Net also demonstrated strong generalization when identifying diseases in other crops. This research holds promise for modern agriculture and crop disease identification (Yuxi Gao, 2023). To enhance data distribution learning and address issues related to small datasets and class imbalance, an improved CycleGAN is employed to generate synthetic samples.…”

New Trends and Challenges in Precision and Digital Agriculture

“…The most popular implementations involve the use of detailed images to assess the state of vegetation by analyzing vegetation indices; yield predictions in qualitative and quantitative terms; and combining the work of various systems that collect important environmental data-meteorological, soil, and yields-quantitatively and qualitatively. In more demanding and specialized production, i.e., vegetable, fruit, potato, and herb crops, the greatest importance is attributed to image analysis, allowing the identification of diseases, physiological disorders, and yield quality defects [8,[61][62][63].…”