Multi-Crop Reproductive Structure Detection and Counting Using a Lightweight YOLOv8n with Spatial–Channel Attention and Re-parameterizable Convolutions

Md. Mushibur Rahman, Umme Fawzia Rahim
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:205-216, 2026.

Abstract

This study presents a lightweight YOLOv8n-based framework for multi-crop reproductive structure detection and counting. A Spatial–Channel Attention Convolution module (C2f-SCA) highlights informative spatial regions and feature channels, while Reparameterizable Depthwise Convolution (RepDWConv) strengthens multi-scale feature representation. Evaluated on Cauliflower, Tomato Flower, and Maize Tassel datasets, the model achieves mAP@0.5 of 0.981, 0.969, and 0.965 with F1-scores of 0.954, 0.921, and 0.936, respectively. These results are achieved with approximately 12% fewer parameters than the YOLOv8n baseline, demonstrating a compact and generalizable solution suitable for precision agriculture in resource-constrained environments.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-rahman26a, title = {Multi-Crop Reproductive Structure Detection and Counting Using a Lightweight {YOLOv8n} with Spatial–Channel Attention and Re-parameterizable Convolutions}, author = {Rahman, Md. Mushibur and Rahim, Umme Fawzia}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {205--216}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/rahman26a/rahman26a.pdf}, url = {https://proceedings.mlr.press/v319/rahman26a.html}, abstract = {This study presents a lightweight YOLOv8n-based framework for multi-crop reproductive structure detection and counting. A Spatial–Channel Attention Convolution module (C2f-SCA) highlights informative spatial regions and feature channels, while Reparameterizable Depthwise Convolution (RepDWConv) strengthens multi-scale feature representation. Evaluated on Cauliflower, Tomato Flower, and Maize Tassel datasets, the model achieves mAP@0.5 of 0.981, 0.969, and 0.965 with F1-scores of 0.954, 0.921, and 0.936, respectively. These results are achieved with approximately 12% fewer parameters than the YOLOv8n baseline, demonstrating a compact and generalizable solution suitable for precision agriculture in resource-constrained environments.} }
Endnote
%0 Conference Paper %T Multi-Crop Reproductive Structure Detection and Counting Using a Lightweight YOLOv8n with Spatial–Channel Attention and Re-parameterizable Convolutions %A Md. Mushibur Rahman %A Umme Fawzia Rahim %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-rahman26a %I PMLR %P 205--216 %U https://proceedings.mlr.press/v319/rahman26a.html %V 319 %X This study presents a lightweight YOLOv8n-based framework for multi-crop reproductive structure detection and counting. A Spatial–Channel Attention Convolution module (C2f-SCA) highlights informative spatial regions and feature channels, while Reparameterizable Depthwise Convolution (RepDWConv) strengthens multi-scale feature representation. Evaluated on Cauliflower, Tomato Flower, and Maize Tassel datasets, the model achieves mAP@0.5 of 0.981, 0.969, and 0.965 with F1-scores of 0.954, 0.921, and 0.936, respectively. These results are achieved with approximately 12% fewer parameters than the YOLOv8n baseline, demonstrating a compact and generalizable solution suitable for precision agriculture in resource-constrained environments.
APA
Rahman, M.M. & Rahim, U.F.. (2026). Multi-Crop Reproductive Structure Detection and Counting Using a Lightweight YOLOv8n with Spatial–Channel Attention and Re-parameterizable Convolutions. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:205-216 Available from https://proceedings.mlr.press/v319/rahman26a.html.

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