Kenta Itakura 교수

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 to

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. The

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 angl

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 u

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 hig

Lidar (light detection and ranging) has been widely utilized for estimating the structural parameters of plants, such as tree height, leaf inclination angle, and biomass. However, individual trees have been primarily manually extracted from three-dimensional (3D) point cloud images. Automatically detecting each tree and analyzing its structural parameters is desirable. In this study, we propose a method to (1) detect each tree from 3D point cloud images obtained from ground-based lidar, (2) esti

Pears and apples in videos recorded while walking were detected automatically using a deep-learning-based method referred to as YOLO. The same fruits in the successive video frames were then identified using a Kalman filter. The average precision of the pear detection was 0.97, while the number of correctly counted pears was 226, out of 234. A YOLO v2 network with a larger input image size and data augmentation method contributed to the high accuracy in the counting. The pears and apples in the

Image analysis is widely used for accurate and efficient plant monitoring. Plants have complex three-dimensional (3D) structures; hence, 3D image acquisition and analysis is useful for determining the status of plants. Here, 3D images of plants were reconstructed using a photogrammetric approach, called "structure from motion". Chlorophyll content is an important parameter that determines the status of plants. Chlorophyll content was estimated from 3D images of plants with color information. To

Recently, structure from motion (SfM), which converts multiple images to a detailed three-dimensional (3D) model, has been used to extract 3D structural information about vegetation. However, multiple still images (e.g., >100 images) are necessary for the 3D reconstruction. This requires multiple shutter releases, but taking many images is time consuming and labor intensive. One possible solution is to take video recordings from which many images can be obtained by dividing the video clips into

In this study, a method to automatically segment plant leaves from three-dimensional (3D) images using structure from motion is proposed. First, leaves in the 3D images are roughly segmented using a region-growing method in which near points with distances less than 0.2 cm are assigned to the same group. By repeating this process, the leaves not touching each other can be segmented. Then, each segmented leaf is projected onto two-dimensional (2D) images, and the watershed algorithm is executed.

Trees in 3D images obtained from lidar were automatically extracted in the presence of other objects that were not trees. We proposed a method combining 3D image processing and machine learning techniques for this automatic detection. Consequently, tree detection could be done with 95% accuracy. First, the objects in the 3D images were segmented one by one; then, each of the segmented objects was projected onto 2D images. Finally, the 2D image was classified into "tree" and "not tree" using a on

Leaf area is one of the most important elements of information in plant management. Leaf area is associated with many agronomic and physiological processes including growth, photosynthesis, transpiration, photon interception, and energy balance. Three-dimensional (3D) plant architecture is required for monitoring, since plants have three-dimensionally complex structures. A photogrammetric approach called structure from motion (SfM) was used for the 3D measurement. A method using the total area o

Tree trunk diameter and tree species are two of the most important parameters in analyzing trees in urban areas and forests. Conventionally, diameters have been measured manually, and the species were determined by sight. An automated tool for these assessments was developed. Tree trunks are automatically detected from captured stereo images. Then, tree trunk diameters are estimated, and the species are determined. The developed graphical user interface tool enables fast and accurate estimation

Tofu is an ancient soybean product that is produced by heating soymilk containing a coagulation agent. Owing to its benefits to human health, it has become popular all over the world. An important index that determines the final product’s (tofu’s) quality is firmness. Coagulants such as CaSO4 and MgCl2 affect the firmness. With the increasing demand for tofu, a monitoring methodology that ensures high-quality tofu is needed. In our previous paper, an opportunity to monitor changes in the physica