Existing object detection methods with many parameters and computations are not suitable for deployment on devices with poor performance in agricultural environments. Therefore, this study proposes a lightweight crop pest detection method based on convolutional neural networks, named YOLOLite-CSG. The basic architecture of the method is derived from a simplified version of YOLOv3, namely YOLOLite, and k-means++ is utilized to improve the generation process of the prior boxes. In addition, a lightweight sandglass block and coordinate attention are used to optimize the structure of residual blocks. The method was evaluated on the CP15 crop pest dataset. Its detection precision exceeds that of YOLOv3, at 82.9%, while the number of parameters is 5 million, only 8.1% of the number used by YOLOv3, and the number of computations is 9.8 GFLOPs, only 15% of that used by YOLOv3. Furthermore, the detection precision of the method is superior to all other commonly used object detection methods evaluated in this study, with a maximum improvement of 10.6%, and it still has a significant edge in the number of parameters and computation required. The method has excellent pest detection precision with extremely few parameters and computations. It is well-suited to be deployed on equipment for detecting crop pests in agricultural environments.

Accurate identification of rice diseases is crucial for improving rice yield and ensuring food security. In this study, we constructed an image dataset containing six classes of rice diseases captured under real field conditions to address challenges such as complex backgrounds, varying lighting, and symptom similarities. Based on the MobileViT-XXS architecture, we proposed an enhanced model named MobileViT-DAP, which integrates Channel Attention (CA), Efficient Channel Attention (ECA), and PoolFormer blocks to achieve precise classification of rice diseases. The experimental results demonstrated that the improved model achieved superior performance with 0.75 M Params and 0.23 G FLOPs, ensuring computational efficiency while maintaining high classification accuracy. On the testing set, the model achieved an accuracy of 99.61%, a precision of 99.64%, a recall of 99.59%, and a specificity of 99.92%. Compared to traditional lightweight models, MobileViT-DAP showed significant improvements in model complexity, computational efficiency, and classification performance, effectively balancing lightweight design with high accuracy. Furthermore, visualization analysis confirmed that the model’s decision-making process primarily relies on lesion-related features, enhancing its interpretability and reliability. This study provides a novel perspective for optimizing plant disease recognition tasks and contributes to improving plant protection strategies, offering a solution for accurate and efficient disease monitoring in agricultural applications.

Pest infestations remain a critical threat to global agriculture, significantly compromising crop yield and quality. While accurate pest detection forms the foundation of precision pest management, current approaches face two primary challenges: (1) the scarcity of comprehensive multi-scale, multi-category pest datasets and (2) performance limitations in detection models caused by substantial target scale variations and high inter-class morphological similarity. To address these issues, we present three key contributions: First, we introduce Insect25—a novel agricultural pest detection dataset containing 25 distinct pest categories, comprising 18,349 high-resolution images. This dataset specifically addresses scale diversity through multi-resolution acquisition protocols, significantly enriching feature distribution for robust model training. Second, we propose GC-Faster RCNN, an enhanced detection framework integrating a hybrid attention mechanism that synergistically combines channel-wise correlations and spatial dependencies. This dual attention design enables more discriminative feature extraction, which is particularly effective for distinguishing morphologically similar pest species. Third, we implement an optimized training strategy featuring a cosine annealing scheduler with linear warm-up, accelerating model convergence while maintaining training stability. Experiments have shown that compared with the original Faster RCNN model, GC-Faster RCNN has improved the average accuracy mAP0.5 on the Insect25 dataset by 4.5 percentage points, and mAP0.75 by 20.4 percentage points, mAP0.5:0.95 increased by 20.8 percentage points, and the recall rate increased by 16.6 percentage points. In addition, experiments have also shown that the GC-Faster RCNN detection method can reduce interference from multiple scales and high similarity between categories, improving detection performance.

In rice, infections caused by Pantoea ananatis or Enterobacter asburiae closely resemble the bacterial blight induced by Xanthomonas oryzae pv. oryzae, yet they differ in drug resistance and management strategies. This study explores the potential of combining hyperspectral imaging (HSI) with machine learning for the rapid and accurate detection of rice bacterial blight symptoms caused by various pathogens. One-dimensional convolutional neural networks (1DCNNs) were employed to construct a classification model, integrating various spectral preprocessing techniques and feature selection algorithms for comparison. To enhance model robustness and mitigate overfitting due to limited spectral samples, generative adversarial networks (GANs) were utilized to augment the dataset. The results indicated that the 1DCNN model, after feature selection using uninformative variable elimination (UVE), achieved an accuracy of 86.11% and an F1 score of 0.8625 on the five-class dataset. However, the dominance of Pantoea ananatis in mixed bacterial samples negatively impacted classification performance. After removing mixed-infection samples, the model attained an accuracy of 97.06% and an F1 score of 0.9703 on the four-class dataset, demonstrating high classification accuracy across different pathogen-induced infections. Key spectral bands were identified at 420–490 nm, 610–670 nm, 780–850 nm, and 910–940 nm, facilitating pathogen differentiation. This study presents a precise, non-destructive approach to plant disease detection, offering valuable insights into disease prevention and management in precision agriculture.

This study aimed to evaluate the effect of two circulation modes of a plasma deodorization unit on the air environment of pig-fattening houses in winter. Two pig-fattening houses were selected, one of which was installed with a plasma deodorizing device with two modes of operation, alternating internal and external circulation on a day-by-day basis. The other house did not have any form of treatment and was used as the control house. Upon installing the system, this study revealed that in the internal circulation mode, indoor temperature and humidity were sustained at elevated levels, with the NH₃ and H₂S concentrations decreasing by 63.87% and 100%, respectively, in comparison to the control house. Conversely, in the external circulation mode, the indoor temperature and humidity remained subdued, accompanied by a 16.43% reduction in CO₂ concentration. The adept interchange between these two operational modes facilitates the regulation of indoor air quality within a secure environment. This not only effectively diminishes deleterious gases in the pig-fattening house but also achieves the remote automation of environmental monitoring and hazardous gas management; thereby, it mitigates the likelihood of diseases and minimizes breeding risks.

Crop diseases are one of the important factors affecting crop yield and quality and are also an important research target in the field of agriculture. In order to quickly and accurately identify crop diseases, help farmers to control crop diseases in time, and reduce crop losses. Inspired by the application of convolutional neural networks in image identification, we propose a lightweight crop disease image identification model based on attentional feature fusion named DSGIResNet_AFF, which introduces self-built lightweight residual blocks, inverted residuals blocks, and attentional feature fusion modules on the basis of ResNet18. We apply the model to the identification of rice and corn diseases, and the results show the effectiveness of the model on the real dataset. Additionally, the model is compared with other convolutional neural networks (AlexNet, VGG16, ShuffleNetV2, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large), and the experimental results show that the accuracy, sensitivity, F1-score, AUC of the proposed model DSGIResNet_AFF are 98.30%, 98.23%, 98.24%, 99.97%, respectively, which are better than other network models, while the complexity of the model is significantly reduced (compared with the basic model ResNet18, the number of parameters is reduced by 94.10%, and the floating point of operations(FLOPs) is reduced by 86.13%). The network model DSGIResNet_AFF can be applied to mobile devices and become a useful tool for identifying crop diseases.