The tea yield estimation provides information support for the harvest time and amount and serves as a decision-making basis for farmer management and picking. However, the manual counting of tea buds is troublesome and inefficient. To improve the efficiency of tea yield estimation, this study presents a deep-learning-based approach for efficiently estimating tea yield by counting tea buds in the field using an enhanced YOLOv5 model with the Squeeze and Excitation Network. This method combines the Hungarian matching and Kalman filtering algorithms to achieve accurate and reliable tea bud counting. The effectiveness of the proposed model was demonstrated by its mean average precision of 91.88% on the test dataset, indicating that it is highly accurate at detecting tea buds. The model application to the tea bud counting trials reveals that the counting results from test videos are highly correlated with the manual counting results (
R
2
= 0.98), indicating that the counting method has high accuracy and effectiveness. In conclusion, the proposed method can realize tea bud detection and counting in natural light and provides data and technical support for rapid tea bud acquisition.
Tea is one of the most consumed beverages in the whole world. Premium tea is a kind of tea with high nutrition, quality, and economic value. This study solves the problem of detecting premium tea buds in automatic plucking by training a modified Mask R-CNN network for tea bud detection in images. A new anchor generation method by adding additional anchors and the CIoU loss function were used in this modified model. In this study, the keypoint detection branch was optimized to locate tea bud keypoints, which, containing a fully convolutional network (FCN), is also built to locate the keypoints of bud objects. The built convolutional neural network was trained through our dataset and obtained an 86.6% precision and 88.3% recall for the bud object detection. The keypoint localization had a precision of 85.9% and a recall of 83.3%. In addition, a dataset for the tea buds and picking points was constructed in study. The experiments show that the developed model can be robust for a range of tea-bud-harvesting scenarios and introduces the possibility and theoretical basis for fully automated tea bud harvesting.
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