Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea. Traditional tea-picking machines may compromise the quality of the tea leaves. High-quality teas are often handpicked and need more delicate operations in intelligent picking machines. Compared with traditional image processing techniques, deep learning models have stronger feature extraction capabilities, and better generalization and are more suitable for practical tea shoot harvesting. However, current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks. We propose a tea shoot instance segmentation model based on multi-scale mixed attention (Mask2FusionNet) using a dataset from the tea garden in Hangzhou. We further analyzed the characteristics of the tea shoot dataset, where the proportion of small to medium-sized targets is 89.9%. Our algorithm is compared with several mainstream object segmentation algorithms, and the results demonstrate that our model achieves an accuracy of 82% in recognizing the tea shoots, showing a better performance compared to other models. Through ablation experiments, we found that ResNet50, PointRend strategy, and the Feature Pyramid Network (FPN) architecture can improve performance by 1.6%, 1.4%, and 2.4%, respectively. These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets. The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments, realizing the individual distinction of tea shoots, and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%. The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.

Spectroscopy can be used for detecting crop characteristics. A goal of crop spectrum analysis is to extract effective features from spectral data for establishing a detection model. An ideal spectral feature set should have high sensitivity to target parameters but low information redundancy among features. However, feature-selection methods that satisfy both requirements are lacking. To address this issue, in this study, a novel method, the continuous wavelet projections algorithm (CWPA), was developed, which has advantages of both continuous wavelet analysis (CWA) and the successive projections algorithm (SPA) for generating optimal spectral feature set for crop detection. Three datasets collected for crop stress detection and retrieval of biochemical properties were used to validate the CWPA under both classification and regression scenarios. The CWPA generated a feature set with fewer features yet achieving accuracy comparable to or even higher than those of CWA and SPA. With only two to three features identified by CWPA, an overall accuracy of 98% in classifying tea plant stresses was achieved, and high coefficients of determination were obtained in retrieving corn leaf chlorophyll content (R² = 0.8521) and equivalent water thickness (R² = 0.9508). The mechanism of the CWPA ensures that the novel algorithm discovers the most sensitive features while retaining complementarity among features. Its ability to reduce the data dimension suggests its potential for crop monitoring and phenotyping with hyperspectral data.