Abstract
This paper highlights the importance of deep learning-based litchi segmentation in precision agriculture using machine vision. The proposed method involves preparing a mixed UAV litchi and MinneApple database, consisting of 2000 images of the same size \(256\times 256\). This paper introduces a modified Mask-RCNN-based instance segmentation model; incorporating a spatial attention block in the backbone network ResNet101, to mitigate one of the significant challenges in litchi counting, i.e., occlusion. The results demonstrate that the proposed model achieves a mean Average Precision (mAP), recall, and F1-score of 90.81%, 89.00%, and 90.35%, respectively, for separated and unoccluded litchis, and an mAP, recall, and F1-score of 81.41%, 82.42%, and 81.91%, respectively, for occluded litchis. The proposed model provides better detection accuracy while minimizing computational burden, showing its potential for efficient and accurate litchi detection and counting in precision agriculture.
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Acknowledgements
Authors are grateful to the proprietors of Ram Narayan Singh Lychee Garden, Parmaighuli, Tezpur, Assam 784501 for generously giving their consent for UAV data collection for the current research.
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Chakraborty, D., Deka, B. (2023). Litchi Fruit Instance Segmentation from UAV Sensed Images Using Spatial Attention-Based Deep Learning Model. In: Maji, P., Huang, T., Pal, N.R., Chaudhury, S., De, R.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2023. Lecture Notes in Computer Science, vol 14301. Springer, Cham. https://doi.org/10.1007/978-3-031-45170-6_90
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