Plant pigment content is a crucial indicator for assessing photosynthetic efficiency, nutritional status, and physiological health. Its spatial distribution is significantly influenced by variety, location, and environmental factors. However, existing methods for measuring pigment content are often destructive, inefficient, and costly, making them unsuitable for the demands of modern precision agriculture. This study proposes a cross-scale, non-destructive detection method for lettuce pigments by integrating hyperspectral imaging (HSI) technology with deep learning algorithms, addressing the limitations of existing techniques in high-throughput and spatial resolution analysis. In this study, we built a multidimensional dataset based on eight different types of lettuce and developed a deep learning model named LPCNet to predict the contents of chlorophyll a (Chl a), chlorophyll b (Chl b), carotenoids (Car), and total pigment content (TPC) in lettuce. The LPCNet model integrates convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and multi-head self-attention (MHSA) mechanisms, enabling automatic extraction of pigment-related key features and simplifying the complex preprocessing and feature selection procedures required in traditional machine learning. Compared to multivariate analysis methods in machine learning, LPCNet demonstrated superior predictive accuracy, with coefficients of determination (R_P²) of 0.9449, 0.8613, 0.9121, and 0.8476 for Chl a, Chl b, Car, and TPC, respectively. Additionally, by combining the hyperspectral reflectance of lettuce canopies with the leaf-level inversion model, we visualized the spatial distribution of pigment content on the canopy of lettuce, achieving cross-scale analysis from leaf to canopy. This study provides an innovative approach for the rapid and accurate assessment of lettuce pigment content and offers an effective visualization tool for revealing the physiological processes and growth development of lettuce.

In order to address the challenge of non-destructive detection of tomato fruit ripeness in controlled environments, this study proposed a real-time instance segmentation method based on the edge device. This method combined the principles of phenotype robots and machine vision based on deep learning. A compact and remotely controllable phenotype detection robot was employed to acquire precise data on tomato ripeness. The video data were then processed by using an efficient backbone and the FeatFlowNet structure for feature extraction and analysis of key-frame to non-key-frame mapping from video data. To enhance the diversity of training datasets and the generalization of the model, an innovative approach was chosen by using random enhancement techniques. Besides, the PolyLoss optimization technique was applied to further improve the accuracy of the ripeness multi-class detection tasks. Through validation, the method of this study achieved real-time processing speeds of 90.1 fps (RTX 3070Ti) and 65.5 fps (RTX 2060 S), with an average detection accuracy of 97% compared to manually measured results. This is more accurate and efficient than other instance segmentation models according to actual testing in a greenhouse. Therefore, the results of this research can be deployed in edge devices and provide technical support for unmanned greenhouse monitoring devices or fruit-picking robots in facility environments.

Plant vascular bundles are responsible for water and material transportation, and their quantitative and functional evaluation is desirable in plant research. At the single-plant level, the number, size, and distribution of vascular bundles vary widely, posing a challenge to automatically and accurately identifying and quantifying them. In this study, a deep learning-integrated phenotyping pipeline was developed to robustly and accurately detect vascular bundles in Computed Tomography (CT) images of stem internodes. Two semantic indicators were used to evaluate and identify a suitable feature extraction network for semantic segmentation models. The epidermis thickness of maize stem was evaluated for the first time and adjacent vascular bundles were improved using an adaptive watershed-based approach. The counting accuracy (R²) of vascular bundles was 0.997 for all types of stem internodes, and the measured accuracy of size traits was over 0.98. Combining sap flow experiments, multiscale traits of vascular bundles were evaluated at the single-plant level, which provided an insight into the water use efficiency of the maize plant.

With the rapid development of genetic analysis techniques and crop population size, phenotyping has become the bottleneck restricting crop breeding. Breaking through this bottleneck will require phenomics, defined as the accurate, high-throughput acquisition and analysis of multi-dimensional phenotypes during crop growth at organism-wide levels, ranging from cells to organs, individual plants, plots, and fields. Here we offer an overview of crop phenomics research from technological and platform viewpoints at various scales, including microscopic, ground-based, and aerial phenotyping and phenotypic data analysis. We describe recent applications of high-throughput phenotyping platforms for abiotic/biotic stress and yield assessment. Finally, we discuss current challenges and offer perspectives on future phenomics research.