Tea yield estimation using UAV images and deep learning.

Traditional estimation of tea yield significantly depends on fields observation, which is facing operational and management challenges due to increasing farm sizes and rising labor costs. Development of artificial intelligence such as deep learning (DL) and unmanned aerial vehicles (UAVs) provide opportunities where intelligent system of tea fields will be constructed. This study carried out the application of tea yield estimation model to practical field scenarios. A large dataset of UAV tea buds' images was built, which contained 5899 images and 29,958 labelled tea buds. Based on the large dataset, YOLOv5 model was used to train, and CSPDarknet53 performed best over Swin Transformer and ConvNeXt as backbone. The mean average precision (mAP), precision, recall and F1 score of CSPDarknet53 were respectively 85.63%, 84.91%,72.19% and 78.00%. Meanwhile, the large database weakened the differences among models of different sizes. Squeeze-and-excitation (SE) block was inserted, and the mAP of all models with CSPDarknet53 and SE was over 85.00%, indicating models could be applied to tea fields. To reduce the number of repeated tea bud detection boxes in the prediction process, a second non-maximum suppression (NMS) filter was used with threshold value of 0.20. The yield of fresh tea leaves before and after picking was estimated according to the number of tea buds detected in different seasons and different types of tea plants. The yield of fresh tea leaves in spring before picking was estimated to 1223.22 kg/ha. This study is a complete report from model training to tea field application, and will advance the industrialization development of agriculture. [Display omitted] • A large dataset with 5899 images and 29,958 labelled tea buds was built. • The mean average precision of tea bud detection was over 85.00%. • CSPDarknet53 as backbone of YOLOv5 performed best. • Tea yields in spring and summer were accurately estimated in tea fields. [ABSTRACT FROM AUTHOR]