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IntroductionWeeds are a major factor affecting crop yield and quality. Accurate identification and localization of crops and weeds are essential for achieving automated weed management in precision agriculture, especially given the challenges in recognition accuracy and real-time processing in complex field environments. To address this issue, this paper proposes an efficient crop-weed segmentation model based on an improved UNet architecture and attention mechanisms to enhance both recognition accuracy and processing speed.MethodsThe model adopts the encoder-decoder structure of UNet, utilizing MaxViT (Multi-Axis Vision Transformer) as the encoder to capture both global and local features within images. Additionally, CBAM (Convolutional Block Attention Module) is incorporated into the decoder as a multi-scale feature fusion module, adaptively adjusting feature map weights to enable the model to focus more accurately on the edges and textures of crops and weeds.Results and discussionExperimental results show that the proposed model achieved 84.28% mIoU and 88.59% mPA on the sugar beet dataset, representing improvements of 3.08% and 3.15% over the baseline UNet model, respectively, and outperforming mainstream models such as FCN, PSPNet, SegFormer, DeepLabv3+, and HRNet. Moreover, the model’s inference time is only 0.0559 seconds, reducing computational overhead while maintaining high accuracy. Its performance on a sunflower dataset further verifies the model’s generalizability and robustness. This study, therefore, provides an efficient and accurate solution for crop-weed segmentation, laying a foundation for future research on automated crop and weed identification.

Abstract Due to the lack of direct assessment metrics, existing studies on the intensity of agricultural policies often utilize indicators such as Gross Domestic Product (GDP) of agriculture or the quantity of agricultural policies as measures. Optimizing methods for analyzing the intensity of agricultural policies will significantly impact parameter selection in agricultural policy research and the evaluation of policy effectiveness. In this study, we constructed a Chinese Agricultural Policy Corpus using agricultural policies released by various governmental agencies at the national level in China from 1982 to April 2023. We quantified the values of agricultural domain terms in the corpus and evaluated the intensity of each agricultural policy document. The validation results of this study indicate a strong correlation between the intensity of agricultural policies and agricultural GDP. The trend in agricultural GDP changes lags behind policy intensity by 2.5 years (at a 95% confidence level), thus validating the rationality of our constructed corpus, agricultural policy scoring dataset, and methodology.

IntroductionThe study addresses challenges in detecting cotton leaf pests and diseases under natural conditions. Traditional methods face difficulties in this context, highlighting the need for improved identification techniques.MethodsThe proposed method involves a new model named CFNet-VoV-GCSP-LSKNet-YOLOv8s. This model is an enhancement of YOLOv8s and includes several key modifications: (1) CFNet Module. Replaces all C2F modules in the backbone network to improve multi-scale object feature fusion. (2) VoV-GCSP Module. Replaces C2F modules in the YOLOv8s head, balancing model accuracy with reduced computational load. (3) LSKNet Attention Mechanism. Integrated into the small object layers of both the backbone and head to enhance detection of small objects. (4) XIoU Loss Function. Introduced to improve the model's convergence performance.ResultsThe proposed method achieves high performance metrics: Precision (P), 89.9%. Recall Rate (R), 90.7%. Mean Average Precision (mAP@0.5), 93.7%. The model has a memory footprint of 23.3MB and a detection time of 8.01ms. When compared with other models like YOLO v5s, YOLOX, YOLO v7, Faster R-CNN, YOLOv8n, YOLOv7-tiny, CenterNet, EfficientDet, and YOLOv8s, it shows an average accuracy improvement ranging from 1.2% to 21.8%.DiscussionThe study demonstrates that the CFNet-VoV-GCSP-LSKNet-YOLOv8s model can effectively identify cotton pests and diseases in complex environments. This method provides a valuable technical resource for the identification and control of cotton pests and diseases, indicating significant improvements over existing methods.

Grain legumes play a significant global role and are integral to agriculture and food production worldwide. Therefore, comprehending and analyzing the factors that influence grain legume yield are of paramount importance for guiding agricultural management and decision making. Traditional statistical analysis methods present limitations in interpreting results, but explainable artificial intelligence (AI) provides a visual representation of model results, offering insights into the key factors affecting grain legume yield. In this study, nine typical grain legume species were selected from a published global experimental dataset: garden pea (Pisum sativum), chickpea (Cicer arietinum), cowpea (Vigna unguiculata), garden vetch (Vicia sativa), faba bean (Vicia faba), lentil (Lens culinaris), pigeon pea (Cajanus cajan), peanut (Arachis hypogaea), and white lupine (Lupinus albus). Seven commonly used models were constructed for each legume species, and model performance evaluation was conducted using accuracy, AUC, recall, precision, and F1 score metrics. The best classification model was selected for each grain legume species. Employing Decision Tree analysis, Feature Importance Evaluation, and SHapley Additive exPlanations (SHAP) as explainable techniques, our study conducted both individual and comprehensive analyses of nine leguminous crops. This approach offers a novel perspective, unveiling not only the unique responses of each crop to the influencing factors but also demonstrating the common factors across different crops. According to the experimental results, XGboost (XGB) and Random Forests (RF) are the best-performing models among the nine types of grain legumes, and the classification accuracy of a specific species is as high as 87.33%. Insights drawn from the feature importance map reveal that several factors, including aerial biomass, precipitation, sunshine duration, soil conditions, growth cycle, and fertilization strategy, have a pivotal influence. However, it was found from the SHAP graph that the responses of various crops to these factors are not the same. This research furnishes novel perspectives and insights into understanding the factors influencing grain legume yields. The findings provide a robust scientific foundation for agricultural managers, experts, and policymakers in the pursuit of optimizing pulse yields and advancing agricultural sustainability.

This research tackles the intricate challenges of detecting densely distributed maize leaf diseases and the constraints inherent in YOLO-based detection algorithms. It introduces the GhostNet_Triplet_YOLOv8s algorithm, enhancing YOLO v8s by integrating the lightweight GhostNet (Ghost Convolutional Neural Network) structure, which replaces the YOLO v8s backbone. This adaptation involves swapping the head’s C2f (Coarse-to-Fine) and Conv (Convolutional) modules with C3 Ghost and GhostNet, simplifying the model architecture while significantly amplifying detection speed. Additionally, a lightweight attention mechanism, Triplet Attention, is incorporated to refine the accuracy in identifying the post-neck layer output and to precisely define features within disease-affected areas. By introducing the ECIoU_Loss (EfficiCLoss Loss) function, replacing the original CIoU_Loss, the algorithm effectively mitigates issues associated with aspect ratio penalties, resulting in marked improvements in recognition and convergence rates. The experimental outcomes display promising metrics with a precision rate of 87.50%, a recall rate of 87.70%, and an mAP@0.5 of 91.40% all within a compact model size of 11.20 MB. In comparison to YOLO v8s, this approach achieves a 0.3% increase in mean average precision (mAP), reduces the model size by 50.2%, and significantly decreases FLOPs by 43.1%, ensuring swift and accurate maize disease identification while optimizing memory usage. Furthermore, the practical deployment of the trained model on a WeChat developer mini-program underscores its practical utility, enabling real-time disease detection in maize fields to aid in timely agricultural decision-making and disease prevention strategies.

This study proposed a weighted sampling hierarchical classification learning method based on an efficient backbone network model to address the problems of high costs, low accuracy, and time-consuming traditional tea disease recognition methods. This method enhances the feature extraction ability by conducting hierarchical classification learning based on the EfficientNet model, effectively alleviating the impact of high similarity between tea diseases on the model's classification performance. To better solve the problem of few and unevenly distributed tea disease samples, this study introduced a weighted sampling scheme to optimize data processing, which not only alleviates the overfitting effect caused by too few sample data but also balances the probability of extracting imbalanced classification data. The experimental results show that the proposed method was significant in identifying both healthy tea leaves and four common leaf diseases of tea (tea algal spot disease, tea white spot disease, tea anthracnose disease, and tea leaf blight disease). After applying the "weighted sampling hierarchical classification learning method" to train 7 different efficient backbone networks, most of their accuracies have improved. The EfficientNet-B1 model proposed in this study achieved an accuracy rate of 99.21% after adopting this learning method, which is higher than EfficientNet-b2 (98.82%) and MobileNet-V3 (98.43%). In addition, to better apply the results of identifying tea diseases, this study developed a mini-program that operates on WeChat. Users can quickly obtain accurate identification results and corresponding disease descriptions and prevention methods through simple operations. This intelligent tool for identifying tea diseases can serve as an auxiliary tool for farmers, consumers, and related scientific researchers and has certain practical value. [ABSTRACT FROM AUTHOR]