A high-efficiency, nondestructive, rapid, and automatic crop disease classification method is essential for the modernization of agriculture. To more accurately extract and fit citrus disease image features, we designed a new 13-layer convolutional neural network (CNN13) consisting of multiple convolutional layer stacks and dropout in this study. To address the problem created by the uneven number of disease images in each category, we used the VGG16 network module for transfer learning, which we combined with the proposed CNN13 to form a new joint network, which we called OplusVNet. To verify the performance of the proposed OplusVNet network, we collected 1869 citrus pest and disease images and 202 normal citrus images from the field. The experimental results showed that the proposed OplusVNet can more effectively solve the problem caused by uneven data volume and has higher recognition accuracy, especially for image categories with a relatively small data volume. Compared with the state of the art networks, the generalization ability of the proposed OplusVNet network is stronger for classifying diseases. The classification accuracy of the model prediction results was 0.99, indicating the model can be used as a reference for crop image classification.

Deep learning has been widely applied to high-dimensional hyperspectral image classification and has achieved significant improvements in classification accuracy. However, most current hyperspectral image classification networks follow a patch-based learning framework, which divides the entire image into multiple overlapping patches and uses each patch as input to the network. Such locality-based methods have limitations in capturing global contextual information and incur high computational costs due to patch overlap. To alleviate these issues, we propose a global learning network with a large receptive fields network (GLNet) to capture more comprehensive and accurate global contextual information, thereby enriching the underlying feature representation for hyperspectral image classification. The proposed GLNet adopts an encoder-decoder architecture with skip connections. In the encoder phase, we introduce a large receptive field context exploration (LRFC) block to extract multi-scale contextual features. The LRFC block enables the network to enlarge the receptive field and capture more spectral-spatial information. In the decoder phase, to further extract rich semantic information, we propose a multi-scale simple attention (MSA) block, which extracts deep semantic information using multi-scale convolution kernels and fuses the obtained features with SimAM. Specifically, on the IP dataset, GLNet achieved overall accuracies (OA) of 98.72%, average accuracies (AA) of 98.63%, and Kappa coefficients of 98.3%; similar improvements were observed on the PU and HOS18 datasets, confirming its superior performance compared to baseline models. The experimental results demonstrate that GLNet performs exceptionally well in hyperspectral image classification tasks, particularly in capturing global contextual information. Compared to traditional patch-based methods, GLNet not only improves classification accuracy but also reduces computational complexity. Future work will further optimize the model structure, enhance computational efficiency, and explore its application potential in other types of remote sensing data.

Accurate diagnosis of tomato leaf diseases is essential to ensure optimised tomato production and reduce losses. However, their fine-grained visual features are challenging to capture and are susceptible to interference from complex agricultural environments. Conventional object detection methods rely on large-scale annotated datasets, which limit detection efficiency and accuracy. This study proposes a novel method, the Cross-layer Spatial Feature Fusion Mask Autoencoder (CSF-MAE), based on a self-supervised framework that integrates multi-scale spatial relationships to precisely detect nine distinct types of tomato leaf disease. The proposed method does not require many annotated samples. It exhaustively captures local and global image features using learnable spatial relationship modules and cross-layer feature fusion mechanisms, thereby enhancing fine-grained detection performance and environmental adaptability. Comparative experiments demonstrated that the proposed model outperformed the baseline, achieving 99.05% accuracy, 99.01% precision, 99% recall and 99% F1-score. The findings revealed that this technique displayed outstanding robustness across intricate backgrounds and high-occlusion scenes, efficiently accomplishing accurate detection of early tomato leaf diseases, with significant practical application value.