Abstract:
Objective Litchi is one of the most representative characteristic fruit trees in the Lingnan region, and the management of its autumn and winter shoots as well as nutrient regulation is directly related to fruit tree yield. Affected by climatic conditions and the vertical canopy structure of the tree crown, litchi trees are prone to apical flushing and asynchronous shoot emergence of autumn and winter shoots, resulting in nutrient waste. Therefore, achieving accurate segmentation of litchi autumn and winter shoots provides a critical basis for subsequent precision management.
Method First, high-resolution images of litchi autumn and winter shoots were acquired via low-altitude UAVs over two years. Second, the SMA-DeepLab model was proposed for accurate segmentation of litchi autumn and winter shoots. In this model, the backbone network of DeepLabv3+ was replaced with SMANet. The main network of SMANet improved feature quality through Starnet and integrated features with the adaptive spatial feature fusion (ASFF) module for multi-scale feature fusion. Meanwhile, a receptive field aggregator (RFA) was introduced to enhance boundary precision.
Result In terms of accuracy performance, the mean pixel accuracy (mPA) and mean intersection over union (mIoU) were 93.46% and 87.84%, respectively, representing improvements of 2.74% and 2.75% compared with the baseline model. In terms of efficiency, the floating-point operations per second (FLOPs) and frames per second (FPS) were 111.44 and 31.63, respectively, and the number of parameters was reduced by 51.3% compared with the baseline model. In addition, visualization of segmentation results showed that the proposed model can achieve accurate segmentation of slender shoots and motion-blurred regions when facing interfering factors like complex backgrounds.
Conclusion The SMA-DeepLab model proposed in this study provides an effective solution for the segmentation of litchi autumn and winter shoots and serves as a technical reference for other object segmentation tasks in the field of smart agriculture.
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