ArticleFrontiers in plant science2024
Accurate and fast implementation of soybean pod counting and localization from high-resolution image.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- VT-SCC: Visual-text semantic alignment via multimodal guidance for unified staple crop organ counting.aBIOTECH · 2026Article
- Sensor-Centric Intelligent Systems for Soybean Harvest Mechanization in Challenging Agro-Environments of China: A Review.Sensors (Basel, Switzerland) · 2025Review
- 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
- Robust soybean seed yield estimation using high-throughput ground robot videos.Frontiers in plant science · 2025Article
- Enhancing multilevel tea leaf recognition based on improved YOLOv8n.Frontiers in plant science · 2025Article
- GrapeUL-YOLO: bidirectional cross-scale fusion with elliptical anchors for robust grape detection in orchards.Frontiers in plant science · 2025Article
- MTSC-Net: A Semi-Supervised Counting Network for Estimating the Number of Slash pine New Shoots.Plant phenomics (Washington, D.C.) · 2024Article
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Authors and funding
8 authors.
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Abstract
Introduction: Soybean pod count is one of the crucial indicators of soybean yield. Nevertheless, due to the challenges associated with counting pods, such as crowded and uneven pod distribution, existing pod counting models prioritize accuracy over efficiency, which does not meet the requirements for lightweight and real-time tasks. Methods: To address this goal, we have designed a deep convolutional network called PodNet. It employs a lightweight encoder and an efficient decoder that effectively decodes both shallow and deep information, alleviating the indirect interactions caused by information loss and degradation between non-adjacent levels. Results: We utilized a high-resolution dataset of soybean pods from field harvesting to evaluate the model's generalization ability. Through experimental comparisons between manual counting and model yield estimation, we confirmed the effectiveness of the PodNet model. The experimental results indicate that PodNet achieves an R Discussion: Compared to advanced computer vision methods, PodNet significantly enhances efficiency with almost no sacrifice in accuracy. Its lightweight architecture and high FPS make it suitable for real-time applications, providing a new solution for counting and locating dense objects.
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