Leaf spot disease is an extremely common disease in the growth process of maize in Northern China and its degree of harm is quite significant. Therefore, the rapid and accurate identification of maize leaf spot disease is crucial for reducing economic losses in maize. In complex field environments, traditional identification methods are susceptible to subjective interference and cannot quickly and accurately identify leaf spot disease through color or shape features. We present an advanced disease identification method utilizing YOLOv8. This method utilizes actual field images of diseased corn leaves to construct a dataset and accurately labels the diseased leaves in these images, thereby achieving rapid and accurate identification of target diseases in complex field environments. We have improved the model based on YOLOv8 by adding Slim-neck modules and GAM attention modules and introducing them to enhance the model’s ability to identify maize leaf spot disease. The enhanced YOLOv8 model achieved a precision (P) of 95.18%, a recall (R) of 89.11%, an average recognition accuracy (mAP50) of 94.65%, and an mAP50-95 of 71.62%, respectively. Compared to the original YOLOv8 model, the enhanced model showcased enhancements of 3.79%, 4.65%, 3.56%, and 7.3% in precision (P), recall (R), average recognition accuracy (mAP50), and mAP50-95, respectively. The model can effectively identify leaf spot disease and accurately calibrate its location. Under the same experimental conditions, we compared the improved model with the YOLOv3, YOLOv5, YOLOv6, Faster R-CNN, and SSD models. The results show that the improved model not only enhances performance, but also reduces parameter complexity and simplifies the network structure. The results indicated that the improved model enhanced performance, while reducing experimental time. Hence, the enhanced method proposed in this study, based on YOLOv8, exhibits the capability to identify maize leaf spot disease in intricate field environments, offering robust technical support for agricultural production.

In agricultural production, rapid and accurate detection of peach blossom bloom plays a crucial role in yield prediction, and is the foundation for automatic thinning. The currently available manual operation-based detection and counting methods are extremely time-consuming and labor-intensive, and are prone to human error. In response to the above issues, this paper proposes a natural environment peach blossom detection model based on the YOLOv5 model. First, a cascaded network is used to add an output layer specifically for small target detection on the basis of the original three output layers. Second, a combined context extraction module (CAM) and feature refinement module (FSM) are added. Finally, the network clusters and statistically analyzes the range of multi-scale channel elements using the K-means++ algorithm, obtaining candidate box sizes that are suitable for the dataset. A novel bounding box regression loss function (SIoU) is used to fuse the directional information between the real box and the predicted box to improve detection accuracy. The experimental results show that, compared with the original YOLOv5s model, our model has correspondingly improved AP values for identifying three different peach blossom shapes, namely, bud, flower, and falling flower, by 7.8%, 10.1%, and 3.4%, respectively, while the final mAP value for peach blossom recognition increases by 7.1%. Good results are achieved in the detection of peach blossom flowering volume. The proposed model provides an effective method for obtaining more intuitive and accurate data sources during the process of peach yield prediction, and lays a theoretical foundation for the development of thinning robots.

With the increasing global concerns over food safety, ensuring the transparency and traceability of agricultural products throughout their entire production-to-consumption process has become a key issue of societal interest. Blockchain technology, due to its decentralized, immutable, and transparent nature, offers a novel solution for the traceability of agricultural products. However, traditional blockchain systems encounter performance bottlenecks when handling large-scale agricultural data, particularly under conditions of high data volume and frequent transactions, where issues such as processing delays and insufficient throughput commonly arise. Sharding technology, a critical approach for improving blockchain performance, has the potential to significantly enhance system throughput and response time by partitioning the blockchain network into multiple independent shards for parallel processing. However, the application of sharding technology in agricultural traceability systems faces several challenges, including shard load imbalance, communication delays between shards, and inefficiencies in consensus algorithms. To address these challenges, two innovative approaches were proposed in this study: firstly, to address the issue of uneven load distribution in sharding, a jump hash sharding algorithm was designed to optimize shard allocation strategies, thereby improving resource utilization and processing efficiency; secondly, to tackle the performance bottleneck of consensus algorithms, an enhanced consensus algorithm was introduced to improve consensus efficiency while maintaining security. Experimental results demonstrated that the proposed method significantly outperformed traditional blockchain-based traceability systems in terms of performance and scalability, providing a more efficient and reliable technological solution for the application of blockchain technology in the agricultural sector. This research not only offers a more efficient technological solution for agricultural product traceability but also paves the way for new applications of blockchain technology within the agricultural domain.

Small fruits and partially occluded contours make strawberry instance segmentation difficult in greenhouse images. This study develops PMH-YOLO11-seg from YOLO11l-seg to examine whether finer spatial features and auxiliary localization supervision improve mask prediction. A stride-4 P2 branch extends the original P3/P4/P5 feature hierarchy. MultiSEAM modules process P2/P3/P4, while P5 connects directly to the segmentation head. The enhanced P2 feature supplies both mask prototypes and a center heatmap branch. During training, the center branch learns box-derived Gaussian targets using error-weighted binary cross-entropy; it is bypassed during inference. Evaluation used a separate test set of 200 images containing 1132 strawberry instances. In repeated training experiments, the model with a center-loss weight of 0.4 achieved mask mAP50 and mAP50–95 of $97.41\pm 0.07$% and $84.35\pm 0.11$%, respectively (mean ± sample standard deviation). The corresponding improvements over the baseline means were 1.33 and 3.71 percentage points. In the single-run ablation, the full model exceeded P2 + MultiSEAM by only 0.03 percentage points in mAP50–95, calculated before rounding. Repeated component comparisons are needed to determine whether this small additional gain is consistent across training runs.

This study introduces a non-destructive, quantitative method using low-field MRI to assess moisture mobility and content distribution in cherry tomatoes. This study developed an advanced 3D non-local mean denoising model to enhance tissue feature analysis and applied an optimized TransUNet model for structural segmentation, obtaining multi-echo data from six tissue types. The structural T2 relaxation inversion was refined by integrating an ACS-CIPSO algorithm. This approach addresses the challenge of low signal-to-noise ratios in multi-echo MRI images from low-field equipment by introducing an innovative solution that effectively reduces voxel noise while retaining structural relaxation variability. The study reveals that there are consistent patterns in the changes in moisture mobility and content across different structures of cherry tomatoes during their ripening process. Mono-exponential analysis reveals the patterns of changes in moisture mobility (T2) and content (A) across various structures. Furthermore, tri-exponential analysis elucidates the patterns of changes in bound water (T21), semi-bound water (T22), and free water (T23), along with their respective contents. These insights offer a novel perspective on the changes in moisture mobility throughout the ripening process of tomato fruit, thereby providing a research pathway for the precise assessment of moisture status and ripening expression in fruits.