The number of maize seedlings is a key determinant of maize yield. Thus, timely, accurate estimation of seedlings helps optimize and adjust field management measures. Differentiating “multiple seedlings in a single hole” of maize accurately using deep learning and object detection methods presents challenges that hinder effectiveness. Multivariate regression techniques prove more suitable in such cases, yet the presence of weeds considerably affects regression estimation accuracy. Therefore, this paper proposes a maize and weed identification method that combines shape features with threshold skeleton clustering to mitigate the impact of weeds on maize counting. The threshold skeleton method (TS) ensured that the accuracy and precision values of eliminating weeds exceeded 97% and that the missed inspection rate and misunderstanding rate did not exceed 6%, which is a significant improvement compared with traditional methods. Multi-image characteristics of the maize coverage, maize seedling edge pixel percentage, maize skeleton characteristic pixel percentage, and connecting domain features gradually returned to maize seedlings. After applying the TS method to remove weeds, the estimated R² is 0.83, RMSE is 1.43, MAE is 1.05, and the overall counting accuracy is 99.2%. The weed segmentation method proposed in this paper can adapt to various seedling conditions. Under different emergence conditions, the estimated R² of seedling count reaches a maximum of 0.88, with an RMSE below 1.29. The proposed approach in this study shows improved weed recognition accuracy on drone images compared to conventional image processing methods. It exhibits strong adaptability and stability, enhancing maize counting accuracy even in the presence of weeds.

The number of wheat ears is one of the most important factors in wheat yield composition. Rapid and accurate assessment of wheat ear number is of great importance for predicting grain yield and food security-related early warning signal generation. The current wheat ear counting methods rely on manual surveys, which are time-consuming, laborious, inefficient and inaccurate. Existing non-destructive wheat ear detection techniques are mostly applied to near-ground images and are difficult to apply to large-scale monitoring. In this study, we proposed a sampling survey method based on the unmanned aerial vehicle (UAV). Firstly, a small number of UAV images were acquired based on the five-point sampling mode. Secondly, an adaptive Gaussian kernel size was used to generate the ground truth density map. Thirdly, a density map regression network (DM-Net) was constructed and optimized. Finally, we designed an overlapping area of sub-images to solve the repeated counting caused by image segmentation. The MAE and MSE of the proposed model were 9.01 and 11.85, respectively. We compared the sampling survey method based on UAV images in this paper with the manual survey method. The results showed that the RMSE and MAPE of NM13 were 18.95 × 10⁴/hm² and 3.37%, respectively, and for YFM4, 13.65 × 10⁴/hm² and 2.94%, respectively. This study enables the investigation of the number of wheat ears in a large area, which can provide favorable support for wheat yield estimation.

The honey bee (Apis mellifera) is of great significance to both the ecological environment and human society, providing bee products and making a significant contribution to the pollination of crops

The three-dimensional (3D) morphological information of wheat grains is an important parameter for discriminating seed health, wheat yield, and wheat quality. High-throughput acquisition of 3D indicators of wheat grains is of great importance for wheat cultivation management, genetic breeding, and economic value. Currently, the 3D morphology of wheat grains still relies on manual investigation, which is subjective, inefficient, and poorly reproducible. The existing 3D acquisition equipment is complicated to operate and expensive, which cannot meet the requirements of high-throughput phenotype acquisition. In this paper, an automatic, economical, and efficient method for the 3D morphometry of wheat grain is proposed. A line laser binocular camera was used to obtain high-quality point-cloud data. A wheat grain 3D model was constructed by point-cloud segmentation, finding, clustering, projection, and reconstruction. Based on this, 3D morphological indicators of wheat grains were calculated. The results show that the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the length were 0.2256 mm and 2.60%, the width, 0.2154 mm and 5.83%, the thickness, 0.2119 mm and 5.81%, and the volume, 1.7740 mm³ and 4.31%. The scanning time was around 12 s and the data processing time was around 3.18 s under a scanning speed of 25 mm/s. This method can achieve the high-throughput acquisition of the 3D information of wheat grains, and it provides a reference for in-depth study of the 3D morphological indicators of wheat and other grains.

Fusarium head blight (FHB) poses a significant threat to global wheat yields and food security, underscoring the importance of timely detection and severity assessment. Although existing approaches based on semantic segmentation and stereo vision have shown promise, their scalability is constrained by limited training datasets and the high maintenance cost and complexity of visual sensor systems. In this study, AR glasses were employed for image acquisition, and wheat spike segmentation was performed using Depth Anything V2, a monocular depth estimation model. Through geometric localization methods—such as identifying abrupt changes in stem width—redundant elements (e.g., awns and stems) were effectively excluded, yielding high-precision spike masks (Precision: 0.945; IoU: 0.878) that outperformed leading semantic segmentation models including Mask R-CNN and DeepLabv3+. The study further conducted a comprehensive analysis of differences between diseased and healthy spikelets across RGB, HSV, and Lab color spaces, as well as three color indices: Excess Green–Excess Red (ExGR), Normalized Difference Index (NDI), and Visible Atmospherically Resistant Index (VARI). A dynamic fusion weighting strategy was developed by combining the Lab-a* component with the ExGR index, thereby enhancing visual contrast between symptomatic and asymptomatic regions. This fused index enabled quantitative assessment of FHB severity, achieving an R² of 0.815 and an RMSE of 8.91%, indicating strong predictive accuracy. The proposed framework offers an intelligent, cost-effective solution for FHB detection, and its core methodologies—depth-guided segmentation, geometric refinement, and multi-feature fusion—present a transferable model for similar tasks in other crop segmentation applications.

In the lower and middle sections of the Yangtze River Basin Region (YRBR) in China, challenges posed by climate change and delayed harvesting of preceding crops have hindered the timely sowing of wheat, leading to an increasing prevalence of late-sown wheat fields. This trend has emerged as a significant impediment to achieving high and stable production of wheat in this area. During the growing seasons of 2022–2023 and 2023–2024, an unmanned aerial vehicle (UAV)-based multispectral camera was used to monitor different wheat materials at various growth stages under normal sowing treatment (M1) and late sowing with increased plant density (M2). By assessing yield loss, the wheat tolerance to late sowing was quantified and categorized. The correlation between the differential vegetation indices (D-VIs) and late sowing resistance was examined. The findings revealed that the J2-Logistic model demonstrated optimal classification performance. The precision values of stable type, intermediate type, and sensitive type were 0.92, 0.61, and 1.00, respectively. The recall values were 0.61, 0.92, and 1.00. The mean average precision (mAP) of the model was 0.92. This study proposes a high-throughput and low-cost evaluation method for wheat tolerance to late sowing, which can provide a rapid predictive tool for screening suitable varieties for late sowing and facilitating late-sown wheat breeding.