Automatic and efficient plant monitoring offers accurate plant management. Construction of three-dimensional (3D) models of plants and acquisition of their spatial information is an effective method for obtaining plant structural parameters. Here, 3D images of leaves constructed with multiple scenes taken from different positions were segmented automatically for the automatic retrieval of leaf areas and inclination angles. First, for the initial segmentation, leave images were viewed from the top, then leaves in the top-view images were segmented using distance transform and the watershed algorithm. Next, the images of leaves after the initial segmentation were reduced by 90%, and the seed regions for each leaf were produced. The seed region was re-projected onto the 3D images, and each leaf was segmented by expanding the seed region with the 3D information. After leaf segmentation, the leaf area of each leaf and its inclination angle were estimated accurately via a voxel-based calculation. As a result, leaf area and leaf inclination angle were estimated accurately after automatic leaf segmentation. This method for automatic plant structure analysis allows accurate and efficient plant breeding and growth management.

The leaf inclination angle is a fundamental variable for determining the plant profile. In this study, the leaf inclination angle was estimated automatically from voxel-based three-dimensional (3D) images obtained from lidar (light detection and ranging). The distribution of the leaf inclination angle within a tree was then calculated. The 3D images were first converted into voxel coordinates. Then, a plane was fitted to some voxels surrounding the point (voxel) of interest. The inclination angle and azimuth angle were obtained from the normal. The measured leaf inclination angle and its actual value were correlated and indicated a high correlation (R² = 0.95). The absolute error of the leaf inclination angle estimation was 2.5°. Furthermore, the leaf inclination angle can be estimated even when the distance between the lidar and leaves is about 20 m. This suggests that the inclination angle estimation of leaves in a top part is reliable. Then, the leaf inclination angle distribution within a tree was calculated. The difference in the leaf inclination angle distribution between different parts within a tree was observed, and a detailed tree structural analysis was conducted. We found that this method enables accurate and efficient leaf inclination angle distribution.

It is important to grasp the number and location of trees, and measure tree structure attributes, such as tree trunk diameter and height. The accurate measurement of these parameters will lead to efficient forest resource utilization, maintenance of trees in urban cities, and feasible afforestation planning in the future. Recently, light detection and ranging (LiDAR) has been receiving considerable attention, compared with conventional manual measurement techniques. However, it is difficult to use LiDAR for widespread applications, mainly because of the costs. We propose a method for tree measurement using 360° spherical cameras, which takes omnidirectional images. For the structural measurement, the three-dimensional (3D) images were reconstructed using a photogrammetric approach called structure from motion. Moreover, an automatic tree detection method from the 3D images was presented. First, the trees included in the 360° spherical images were detected using YOLO v2. Then, these trees were detected with the tree information obtained from the 3D images reconstructed using structure from motion algorithm. As a result, the trunk diameter and height could be accurately estimated from the 3D images. The tree detection model had an F-measure value of 0.94. This method could automatically estimate some of the structural parameters of trees and contribute to more efficient tree measurement.

To produce high-quality citrus, the harvest time of citrus should be determined by considering its maturity. To evaluate citrus maturity, the Brix/acid ratio, which is the ratio of sugar content or soluble solids content to acid content, is one of the most commonly used indicators of fruit maturity. To estimate the Brix/acid ratio, fluorescence spectroscopy, which is a rapid, sensitive, and cheap technique, was adopted. Each citrus peel was extracted, and its fluorescence value was measured. Then, the fluorescent spectrum was analyzed using a convolutional neural network (CNN). In fluorescence spectroscopy, a matrix called excitation and emission matrix (EEM) can be obtained, in which each fluorescence intensity was recorded at each excitation and emission wavelength. Then, by regarding the EEM as an image, the Brix/acid ratio of juice from the flesh was estimated via performing a regression with a CNN (CNN regression). As a result, the Brix/acid ratio absolute error was estimated to be 2.48, which is considerably better than the values obtained by the other methods in previous studies. Hyperparameters, such as depth of layers, learning rate, and the number of filters used for this estimation, could be observed using Bayesian optimization, and the optimization contributed to the high accuracy.

In this article, we proposed an automated tree segmentation method using light detection and ranging (LiDAR) point cloud data. Tree segmentation was performed accurately even with bumpy ground, and was validated on more than 1000 samples. For example, 371 out of 374 trees were detected from dataset 2, and the error was caused by the trees with low point densities located in the area far from the LiDAR. Segmentation was accurately performed, including the branches, leading to the retrieval of high-level parameters such as the leaf areas. To obtain the parameters regarding the leaf area from the segmented trees, a method for classifying the leaf and branch points in the three-dimensional point clouds obtained using a terrestrial LiDAR method was proposed. After preprocessing the input point cloud, such as by voxelization, the fast point feature histogram (FPFH) features were calculated. Then, the classifier for classification into leaves and branches was trained using the training dataset to calculate the test accuracy with the test data. Moreover, an unsupervised method for classification using the FPFH feature and k -means algorithm was also performed. Consequently, the recall and precision values of the classification were determined as 98.14% and 96.03%, respectively, with the supervised approach.

Monitoring the maturation process of Satsuma mandarin (Citrus unshiu Marc.) by determining the soluble solids (SS) and acid content non-destructively is needed. Fluorescence components potentially offer such means of accessing fruit maturity characteristics in the orchard. The aim of this study was to determine the potential of fluorescence spectroscopy for monitoring the stage of citrus maturity. Four major fluorescent components in peel and/or flesh were found including chlorophyll-a (excitation (Ex) 410 nm, emission (Em) 675 nm) and chlorophyll-b (Ex 460 nm, Em 650 nm),polymethoxyflavones (PMFs) (Ex 260 nm and 370 nm, Em 540 nm), coumarin (Ex 330 nm, Em 400 nm), and a tryptophan-like compound (Ex 260 nm, Em 330 nm). Our results indicated a significant (R² = 0.9554) logarithmic ratio between tryptophan-like compoundsExEm and chlorophyll-aExEm with the SS:acid ratio. Also, the log of the ratio of PMFs from the peel (ExExEm was significantly correlated with the SS:acid ratio (R² = 0.8207). While the latter correlation was not as strong as the former, it does demonstrate the opportunity to develop a non-destructive field measurement of fluorescent peel compounds as an indirect index of fruit maturity.