YOLO-LF: a lightweight multi-scale feature fusion algorithm for wheat spike detection
Author:
Funder
Humanities and Social Sciences Planning Fund Projects of Ministry of Education of China
Hunan Provincial Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11554-024-01529-2.pdf
Reference46 articles.
1. Wen, C., Wu, J., Chen, H., Su, H., Chen, X., Li, Z., Yang, C.: Wheat spike detection and counting in the field based on SpikeRetinaNet. Front. Plant Sci. 13, 821717 (2022)
2. Alkhudaydi, T., Lglesia, B.: Counting spikelets from infield wheat crop images using fully convolutional networks. Neural Comput. Appl. 34(20), 17539–17560 (2022)
3. Ullah, E., Ullah, M., Sajjad, M., Cheikh, F.A.: Deep learning based wheat ears count in robot images for wheat phenotyping. Electron. Imaging 34, 1–6 (2022)
4. Dandrifosse, S., Ennadifi, E., Carlier, A., Gosselin, B., Dumont, B., Mercatoris, B.: Deep learning for wheat ear segmentation and ear density measurement: from heading to maturity. Comput. Electron. Agric. 199, 107161 (2022)
5. Zaji, A., Liu, Z., Xiao, G., Bhowmik, P., Sangha, J.S., Ruan, Y.: AutoOLA: automatic object level augmentation for wheat spikes counting. Comput. Electron. Agric. 205, 107623 (2023)
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