ArticleFrontiers in plant science2022
Recognition of soybean pods and yield prediction based on improved deep learning model.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- A Lightweight model for accurate soybean pod detection: YOLO-MobilePod.BMC plant biology · 2026Article
- DELTA-SoyStage: A Lightweight Detection Architecture for Full-Cycle Soybean Growth Stage Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Amodal Segmentation and Trait Extraction of On-Branch Soybean Pods with a Synthetic Dual-Mask Dataset.Sensors (Basel, Switzerland) · 2025Article
- Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybean.Plant methods · 2025Article
- PodNet: Pod real-time instance segmentation in pre-harvest soybean fields.Plant phenomics (Washington, D.C.) · 2025Article
- GrainNet: efficient detection and counting of wheat grains based on an improved YOLOv7 modeling.Plant methods · 2025Article
- Rapid and accurate detection of peanut pod appearance quality based on lightweight and improved YOLOv5_SSE model.Frontiers in plant science · 2025Article
- Machine vision-based detection of key traits in shiitake mushroom caps.Frontiers in plant science · 2025Article
- Robust soybean seed yield estimation using high-throughput ground robot videos.Frontiers in plant science · 2025Article
- High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.Plants (Basel, Switzerland) · 2024Article
- How does climate change affect potential yields of four staple grain crops worldwide by 2030?PloS one · 2024Article
- A New Efficient Multi-Object Detection and Size Calculation for Blended Tobacco Shreds Using an Improved YOLOv7 Network and LWC Algorithm.Sensors (Basel, Switzerland) · 2023Article
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5 authors.
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Abstract
As a leaf homologous organ, soybean pods are an essential factor in determining yield and quality of the grain. In this study, a recognition method of soybean pods and estimation of pods weight per plant were proposed based on improved YOLOv5 model. First, the YOLOv5 model was improved by using the coordinate attention (CA) module and the regression loss function of boundary box to detect and accurately count the pod targets on the living plants. Then, the prediction model was established to reliably estimate the yield of the whole soybean plant based on back propagation (BP) neural network with the topological structure of 5-120-1. Finally, compared with the traditional YOLOv5 model, the calculation and parameters of the proposed model were reduced by 17% and 7.6%, respectively. The results showed that the average precision (AP) value of the improved YOLOv5 model reached 91.7% with detection rate of 24.39 frames per millisecond. The mean square error (MSE) of the estimation for single pod weight was 0.00865, and the average coefficients of determination R
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