Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R² values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R² values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.

Soybean is an important oil crop and plant protein source, and phenotypic traits' detection for soybean diseases, which seriously restrict yield and quality, is of great significance for soybean breeding, cultivation, and fine management. The recognition accuracy of traditional deep learning models is not high, and the chemical analysis operation process of soybean diseases is time-consuming. In addition, artificial observation and experience judgment are easily affected by subjective factors and difficult to guarantee the accuracy of the objective. Thus, a rapid identification method of soybean diseases was proposed based on a new residual attention network (RANet) model. First, soybean brown leaf spot, soybean frogeye leaf spot, and soybean phyllosticta leaf spot were used as research objects, the OTSU algorithm was adopted to remove the background from the original image. Then, the sample dataset of soybean disease images was expanded by image enhancement technology based on a single leaf image of soybean disease. In addition, a residual attention layer (RAL) was constructed using attention mechanisms and shortcut connections, which further embedded into the residual neural network 18 (ResNet18) model. Finally, a new model of RANet for recognition of soybean diseases was established based on attention mechanism and idea of residuals. The result showed that the average recognition accuracy of soybean leaf diseases was 98.49%, and the F1-value was 98.52 with recognition time of 0.0514 s, which realized an accurate, fast, and efficient recognition model for soybean leaf diseases.

A reasonable plant type is an essential factor for improving canopy structure, ensuring a reasonable expansion of the leaf area index and obtaining a high-quality spatial distribution of light. It is of great significance in promoting effective selection of the ecological breeding index and production practices for maize. In this study, a method for calculating the phenotypic traits of the maize canopy in three-dimensional (3D) space was proposed, focusing on the problems existing in traditional measurement methods in maize morphological structure research, such as their complex procedures and relatively large error margins. Specifically, the whole maize plant was first scanned with a FastSCAN hand-held scanner to obtain 3D point cloud data for maize. Subsequently, the raw point clouds were simplified by the grid method, and the effect of noise on the quality of the point clouds in maize canopies was further denoised by bilateral filtering. In the last step, the 3D structure of the maize canopy was reconstructed. In accordance with the 3D reconstruction of the maize canopy, the phenotypic traits of the maize canopy, such as plant height, stem diameter and canopy breadth, were calculated by means of a fitting sphere and a fitting cylinder. Thereafter, multiple regression analysis was carried out, focusing on the calculated data and the actual measured data to verify the accuracy of the calculation method proposed in this study. The corresponding results showed that the calculated values of plant height, stem diameter and plant width based on 3D scanning were highly correlated with the actual measured data, and the determinant coefficients R² were 0.9807, 0.8907 and 0.9562, respectively. In summary, the method proposed in this study can accurately measure the phenotypic traits of maize. Significantly, these research findings provide technical support for further research on the phenotypic traits of other crops and on variety breeding.

Geometric three-dimensional (3D) reconstruction has emerged as a powerful tool for plant phenotyping and plant breeding. Although laser scanning is one of the most intensely used sensing techniques for 3D reconstruction projects, it still has many limitations, such as the high investment cost. To overcome such limitations, in the present study, a low-cost, novel, and efficient imaging system consisting of a red-green-blue (RGB) camera and a photonic mixer detector (PMD) was developed, and its usability for plant phenotyping was demonstrated via a 3D reconstruction of a soybean plant that contains color information. To reconstruct soybean canopies, a density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to extract canopy information from the raw 3D point cloud. Principal component analysis (PCA) and iterative closest point (ICP) algorithms were then used to register the multisource images for the 3D reconstruction of a soybean plant from both the side and top views. We then assessed phenotypic traits such as plant height and the greenness index based on the deviations of test samples. The results showed that compared with manual measurements, the side view-based assessments yielded a determination coefficient (R²) of 0.9890 for the estimation of soybean height and a R² of 0.6059 for the estimation of soybean canopy greenness index; the top view-based assessment yielded a R² of 0.9936 for the estimation of soybean height and a R² of 0.8864 for the estimation of soybean canopy greenness. Together, the results indicated that an assembled 3D imaging device applying the algorithms developed in this study could be used as a reliable and robust platform for plant phenotyping, and potentially for automated and high-throughput applications under both natural light and indoor conditions.

Improving the speed and accuracy of chlorophyll (Ch1) content prediction in different light areas of apple trees is a central priority for understanding the growth response to light intensity and in turn increasing the primary production of apples. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time-consuming. Over the years, alternative methods—both rapid and nondestructive—were explored, and many vegetation indices (VIs) were developed to retrieve Ch1 content at the canopy level from meter- to decameter-scale reflectance observations, which have lower accuracy due to the possible confounding influence of the canopy structure. Thus, the spatially continuous distribution of Ch1 content in different light areas within an apple tree canopy remains unresolved. Therefore, the objective of this study is to develop methods for Ch1 content estimation in areas of different light intensity by using 3D models with color characteristics acquired by a 3D laser scanner with centimeter spatial resolution. Firstly, to research relative light intensity (RLI), canopies were scanned with a FARO Focus3D 120 laser scanner on a calm day without strong light intensity and then divided into 180 cube units for each canopy according to actual division methods in three-dimensional spaces based on distance information. Meanwhile, four different types of RLI were defined as 0–30%, 30–60%, 60–85%, and 85–100%, respectively, according to the actual division method for tree canopies. Secondly, Ch1 content in the 180 cubic units of each apple tree was measured by a leaf chlorophyll meter (soil and plant analyzer development, SPAD). Then, color characteristics were extracted from each cubic area of the 3D model and calculated by two color variables, which could be regarded as effective indicators of Ch1 content in field crop areas. Finally, to address the complexity and fuzziness of relationships between the color characteristics and Ch1 content of apple tree canopies (which could not be expressed by an accurate mathematical model), a three-layer artificial neural network (ANN) was constructed as a predictive model to find Ch1 content in different light areas in apple tree canopies. The results indicated that the mean highest and mean lowest value of Ch1 content distributed in 60–85% and 0–30% of RLI areas, respectively, and that there was no significant difference between adjacent RLI areas. Additionally, color characteristics changed regularly as the RLI rose within canopies. Moreover, the prediction of Ch1 content was strongly correlated with those of actual measurements (R = 0.9755) by the SPAD leaf chlorophyll meter. In summary, the color characteristics in 3D apple tree canopies combined with ANN technology could be used as a potential rapid technique for predicting Ch1 content in different areas of light in apple tree canopies.