Research on a Precision Counting Method and Web Deployment for Natural-form Bothriochloa ischaemum Spikes and Seeds Based on Object Detection
Date
Authors
Zhao, Huamin
Zhang, Yongzhuo
Zheng, Yabo
Zeng, Erkang
Jiang, Linjun
Yan, Weiqi
Xia, Fangshan
Xu, Defang
Supervisor
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Journal Article
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MDPI AG
Abstract
Bothriochloa ischaemum is a key forage species with strong grazing tolerance and high nutritional value, making precise quantification of spike and seed traits essential for germplasm evaluation and yield prediction. However, the compact architecture and minute seed size in natural field conditions render manual counting inefficient and labor-intensive. To address this limitation, this study presents a non-destructive and automated quantification framework integrating advanced object detection and regression analysis for accurate in situ estimation of spikes and seed numbers. To further address the challenges of dense spike detection caused by occlusion and small object sizes, this study developed a modified model named YOLOv12-DAN by integrating DySample dynamic upsampling, ASFF feature fusion, and NWD loss, which achieved a mean average precision (mAP) of 91.6%. Meanwhile, for the detection of dense kernels on compact spikes, an improved YOLOv12 architecture incorporating an Explicit Visual Center (EVC) module was proposed to enhance multi-scale feature representation. The optimized model attained a bounding box precision of 96.5%, a recall rate of 86.4%, an mAP50 of 94.3%, and an mAP50-95 of 73.9%. Furthermore, a univariate linear regression model based on 132 spike samples verified the reliable consistency between the predicted and actual seed counts, with a mean absolute error (MAE) of 6.30, a mean absolute percentage error (MAPE) of 9.35, and an R-squared (R2) value of 0.808. Finally, the model was deployed through a lightweight end-to-end web application, enabling real-time field operation and promoting its applicability in breeding programs and agronomic decision-making. This study provides a robust technical pathway for automated phenotyping and precision forage improvement.Description
Keywords
0502 Environmental Science and Management, 0703 Crop and Pasture Production, 3002 Agriculture, land and farm management, 3004 Crop and pasture production, Bothriochloa ischaemum, spike detection, seed counting, object detection
Source
Agronomy, ISSN: 2073-4395 (Print); 2073-4395 (Online), MDPI AG, 16(7), 706-706. doi: 10.3390/agronomy16070706
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Copyright: © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.