Pod phenotypic traits are closely related to grain yield and quality. Pod phenotype detection in soybean populations in natural environments is important to soybean breeding, cultivation, and field management. For an accurate pod phenotype description, a dynamic detection method is proposed based on an improved YOLO-v5 network. First, two varieties were taken as research objects. A self-developed field soybean three-dimensional color image acquisition vehicle was used to obtain RGB and depth images of soybean pods in the field. Second, the red–green–blue (RGB) and depth images were registered using an edge feature point alignment metric to accurately distinguish complex environmental backgrounds and establish a red–green–blue-depth (RGB-D) dataset for model training. Third, an improved feature pyramid network and path aggregation network (FPN+PAN) structure and a channel attention atrous spatial pyramid pooling (CA-ASPP) module were introduced to improve the dim and small pod target detection. Finally, a soybean pod quantity compensation model was established by analyzing the influence of the number of individual plants in the soybean population on the detection precision to statistically correct the predicted pod quantity. In the experimental phase, we analyzed the impact of different datasets on the model and the performance of different models on the same dataset under the same test conditions. The test results showed that compared with network models trained on the RGB dataset, the recall and precision of models trained on the RGB-D dataset increased by approximately 32% and 25%, respectively. Compared with YOLO-v5s, the precision of the improved YOLO-v5 increased by approximately 6%, reaching 88.14% precision for pod quantity detection with 200 plants in the soybean population. After model compensation, the mean relative errors between the predicted and actual pod quantities were 2% to 3% for the two soybean varieties. Thus, the proposed method can provide rapid and massive detection for pod phenotyping in soybean populations and a theoretical basis and technical knowledge for soybean breeding, scientific cultivation, and field management.

Design of precision variable-rate spray system for unmanned aerial vehicle using automatic control method

Distribution regularity of downwash airflow under rotors of agricultural UAV for plant protection

Remote sensing has assisted precision fertilization by mapping crop and soil nutrient status, but these layers are often assessed separately. We developed a matched-unit diagnosis framework for 110 soybean and maize units at Heshan Farm, China. Linear spectral models estimated leaf nitrogen (N) and phosphorus (P) from ZiYuan 1-02D imagery and soil alkali-hydrolyzable nitrogen (SAN) and available phosphorus (SAP) from multi-source bare soil imagery. Soil sampling occurred 27–32 days after foliar sampling, supporting the retrospective comparison of post-harvest residual soil and earlier canopy nutrient status. Canopy models achieved moderate validation performance (R² = 0.424–0.571), while SAN and SAP models yielded R² values of 0.656 and 0.583, respectively. Matched-unit analyses revealed positive SAN–leaf N and SAP–leaf P associations, whereas cross-nutrient relationships were weak. The soil and canopy N-P status indices (SNPSI and CNPSI) identified four relative status classes, with correspondence quantified by the N-P coupling index (NPCI). Spatial block validation of the downstream CNPSI regression yielded a higher root mean square error than random cross-validation (1.712 versus 0.561), indicating limited spatial transferability. The framework supports targeted field verification; application elsewhere requires local recalibration and independent spatial validation.

Reliable segmentation of cherry tomato peduncles is difficult in greenhouse harvesting because peduncles are slender, occupy few pixels, resemble shoots and petioles, and are frequently occluded by clustered fruit. This study presents a peduncle-aware instance-segmentation and geometric-consistency framework for cluster-tomato harvesting perception. From the public 2022 Shanxi Agricultural Valley Tomato Town greenhouse cluster-tomato dataset, 1383 images with visible peduncles were selected and re-annotated in Labelme with polygon masks for fruit-cluster regions and peduncles. The framework combines boundary-weighted mask optimization, Coordinate Attention, and Soft-NMS. Evaluation on 208 held-out images showed a class-averaged mask AP50 of 0.9606 under the high-recall configuration; fruit-cluster and peduncle AP50 values were 0.9900 and 0.9313, respectively. At a score threshold of 0.50, overall precision, recall, F1 score, and instance-level Jaccard score were 0.8440, 0.9495, 0.8937, and 0.8078. In the 2D mask-geometric consistency evaluation, centroid-derived proxy points yielded matched-pair mean errors of 8.49 pixels for the peduncle and 5.94 pixels for the fruit-cluster region. These values quantify consistency between predicted-mask-derived and ground-truth-mask-derived points and do not represent physical robotic positioning accuracy.