Xiang Li

Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700–1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R²) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R² values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations.

Tea plants are one of the most widely planted agricultural crops in the world. The traditional method of surveying germination density is mainly manual checking, which is time-consuming and inefficient. In this research, the Improved YOLOv5 model was used to identify tea buds and detect germination density based on tea trees canopy visible images. Firstly, five original YOLOv5 models were trained for tea trees germination recognition, and performance and volume were compared. Secondly, backbone structure was redesigned based on the lightweight theory of Xception and ShuffleNetV2. Meanwhile, reverse attention mechanism (RA) and receptive field block (RFB) were added to enhance the network feature extraction ability, achieving the purpose of optimizing the YOLOv5 network from both lightweight and accuracy improvement. Finally, the recognition ability of the Improved YOLOv5 model was analyzed, and the germination density of tea trees was detected according to the tea bud count. The experimental results show that: (1) The parameter numbers of the five original YOLOv5 models were inversely proportional to the detection accuracy. The YOLOv5m model with the most balanced comprehensive performance contained 20,852,934 parameters, the precision rate of the YOLOv5m recognition model was 74.9%, the recall rate was 75.7%, and the mAP_0.5 was 0.758. (2) The Improved YOLOv5 model contained 4,326,815 parameters, the precision rate of the Improved YOLOv5 recognition model was 94.9%, the recall rate was 97.67%, and the mAP_0.5 was 0.758. (3) The YOLOv5m model and the Improved YOLOv5 model were used to test the validation set, and the true positive (TP) values identified were 86% and 94%, respectively. The Improved YOLOv5 network model was effectively improved in both volume and accuracy according to the result. This research is conducive to scientific planning of tea bud picking, improving the production efficiency of the tea plantation and the quality of tea production in the later stage.