Shape is one of the most important traits of agricultural products due to its relationships with the quality, quantity, and value of the products. For strawberries, the nine types of fruit shape were defined and classified by humans based on the sampler patterns of the nine types. In this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification, and we introduce the concept of computerization into this field. Four types of descriptors were extracted from the digital images of strawberries: (1) the Measured Values (MVs) including the length of the contour line, the area, the fruit length and width, and the fruit width/length ratio; (2) the Ellipse Similarity Index (ESI); (3) Elliptic Fourier Descriptors (EFDs), and (4) Chain Code Subtraction (CCS). We used these descriptors for the classification test along with the random forest approach, and eight of the nine shape types were classified with combinations of MVs + CCS + EFDs. CCS is a descriptor that adds human knowledge to the chain codes, and it showed higher robustness in classification than the other descriptors. Our results suggest machine learning's high ability to classify fruit shapes accurately. We will attempt to increase the classification accuracy and apply the machine learning methods to other plant species.
Plant shape measurements have conventionally been conducted in plant science by classifying their shape features, by measuring their widths and lengths with a Vernier caliper, or by similar methods. Those measurements rely heavily on human senses and manual labor, making it difficult to acquire massive data. Additionally, they are prone to large measurement differences. To cope with those problems of conventional measuring methods, we are developing a three-dimensional (3D) shape-measuring system using images and a reliable assessment technique. 3D objects enable us to assess and measure shape features with high accuracy and to automatically measure volume, which conventional methods cannot. Thus, our new system is capable of automatically and efficiently measuring objects. Our goal is to obtain wide acceptance of our method at actual research sites. Unlike industrial products, it is difficult to properly assess the measurements of plants because of their object fluctuations and shape complexities. This paper describes our automatic 3D shape-measuring system, the method for assessing measurement accuracy, and the assessment results. The measurement accuracy of the developed system for strawberry fruits is 0.6 mm or less for 90% or more of the fruit and 0.3 mm or less for 80%. This evidence supports the system’s capability of shape assessment. The developed system can fully automate photographing, measuring, and modeling objects and can semi-automatically analyze them, reducing the time required for the entire process from the conventional time of 6–7 h to 1.5 h. The developed system is designed for users with no technical knowledge so that they can easily use it to acquire 3D measurement data on plants. Thus, we intend to expand measurable objects from strawberry fruits to other plants and their parts, including leaves, stalks, and flowers
Digital image phenotyping has become popular in plant research. Plants are complex in shape, and occlusion can often occur. Three-dimensional (3D) data are expected to measure the morphological traits of plants with higher accuracy. Plants have organs with flat and/or narrow shapes and similar component structures are repeated. Therefore, it is difficult to construct an accurate 3D model by applying methods developed for industrial materials and architecture. Here, we review noncontact and all-around 3D modeling and configuration of camera systems to measure the morphological traits of plants in terms of system composition, accuracy, cost, and usability. Typical noncontact 3D measurement methods can be roughly classified into active and passive methods. We describe their advantages and disadvantages. Structure-from-motion/multi-view stereo (SfM/MVS), a passive method, is the most frequently used measurement method for plants. It is described in terms of “forward intersection” and “backward resection.” We recently developed a novel SfM/MVS approach by mixing the forward and backward methods, and we provide a brief overview of our approach in this paper. While various fields are adopting 3D model construction, nonexpert users struggle to use them and end up selecting inadequate methods, which lead to model failure. We hope that this review will help users who are considering starting to construct and measure 3D models.
In this study, we developed an all-around 3D plant modeling system that operates using images and is capable of measuring plants non-destructively without any contact. During the fabrication of this device, we selected a method capable of performing 3D model reconstruction from multiple images. We then developed an improved SfM-MVS (Structure from Motion / Multi-View-Stereo) method that enables 3D reconstruction by simply capturing images with a camera. The resulting image-based method offers a high degree of freedom because the hardware and software can comprise commercially available products, and it permits the use of one or more cameras according to the shape and size of the plant. The advantages of the image-based method are that 3D reconstruction can be conducted at any time as long as the images are already taken, and that the desired locations can be observed, measured, and analyzed from 2D images and a 3D point cloud. The device we developed is capable of 3D measurements and modeling of plants from a few millimeters to 2.4 m of height using this method. This article explains this device, the principles of its composition, and the accuracy of the models obtained from it.
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