| A small target detection and yield prediction method with YOLO-LT under UVA aerial photography in apple orchards |
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| DOI：10.7606/j.issn.1000-7601.2025.05.25 |
| Key Words: apple orchard small target detection yield forecasting feature extraction YOLO-LT UVA aerial imagery |
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| Abstract: |
| In order to solve the problem of insufficient detection accuracy of small target fruit under unmanned aerial vehicle (UAV) shooting conditions and yield prediction of fruit trees in natural environments, a YOLO-LT UAV aerial photography method for detecting small targets and predicting yield in apple orchards was proposed. Firstly, the YOLO-LT model was proposed, and the Feature Fusion Attention Network (FFA-Net) was added to the backbone network of the YOLOv8n model to enhance the clarity of the image in complex environments such as foggy days. Focal Modulation Networks (Focal Nets) were used to replace the Spatial Pyramid Pooling Fast (SPPF) to improve the extraction ability of small target features. In the neck part, a dilation\|wise residual segmentation (DWR Seg) network was introduced to optimize the performance of the C2f and bottleneck modules. At the same time, a Hybrid Attention Transformer (HAT) was integrated into the object detection layer to further improve the attention and detection sensitivity of the model to small targets. Secondly, the YOLO-LT model was used to detect the number of fruits in the fruit tree image, combined with the canopy area of the fruit tree obtained by the UAV image, and the above detection results were used as input features to construct the CNN-LSTM spatial time series yield prediction model. Experiments showed that the proposed YOLO-LT model not only had a significant improvement in detection accuracy but also increased its precision, recall, and mean average precision (mAP) by 5.3, 3.9, and 4.3 percentage points, respectively; the inference speed reaches 49 frames per second, and the model size was only 12.6 MB. The coefficient of determination (R2) of the CNN-LSTM yield prediction model reached 0.8060, and the root mean square error (RMSE) was 1.8167 kg. This method can meet the actual needs of fruit tree yield measurement in the natural environment and provides effective technical support for the intelligent management of modern orchards. |
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