{"title":"Growth Measurement of a Community of Strawberries Using Three-Dimensional Sensor","authors":"Satoshi Yamamoto, S. Hayashi, S. Tsubota","doi":"10.2525/ECB.53.49","DOIUrl":null,"url":null,"abstract":"Monitoring plant health is an important technique to guarantee high quality and quantity of agricultural production. In case of greenhouse-grown strawberry plants, farmers generally measure the plants’ height, number of leaves, and area of a single leaf to obtain growth information and estimate the plant’s health. These indices have been used successfully for many years in traditional strawberrygrowing. However, this method of monitoring plant health in an industrial-scale greenhouse would take up a lot of time. Furthermore, traditional growth information is not usually obtained every day. Even when the information is obtained, it involves taking only a sample value, which does not accurately report the condition of every plant. A circulating-type movable bench system for strawberry cultivation has been studied in Japan (Yoshida et al., 2008; Hayashi et al., 2011; Saito et al., 2012). With this cultivation system, all plants pass daily through a single location, called the access point, to be watered. The access point is thus the ideal place for continually and precisely obtaining information on the growth of strawberry plants. Machine vision is a promising technique for effectively monitoring every plant at the access point, since it enables non-destructive measurement. For effective plant health monitoring, many imaging techniques such as digital color imaging, multi-spectral imaging, thermal imaging and fluorescence imaging have been investigated (Takayama and Nishina, 2009). On the other hand, the availability of low-cost depth sensors that generate depth images is on the rise because of the development of natural user interfaces (NUIs), as seen in Microsoft’s Kinect sensor, which is commonly used as a motion controller for TV games. The accuracy of the Kinect sensor has been reported as a percentage of measurement error: between 2 cm to 2 cm is 90.9%, 82.9% and 81.2% for data along the x, y and z axes of the camera coordinates, respectively, using an uncalibrated Kinect sensor (Khoshelham and Elberink, 2012). In the camera coordinates, x and y axes are included in the CMOS plane of the camera. Namely, x axis is horizontal direction in the plane, and y axis is vertical direction. Direction of z axis is equivalent to the normal vector of the plane. For the leaf segmentation in rosebushes and the measurement of the leaf angle of ornamental plants, the low-cost depth sensor has been applied (Chéné et al., 2012). The Kinect sensor of these researches was equipped with an active-stereo system to obtain depth information. In 2014, new Kinect sensor was released which measures the depth by way of timeof-flight method. It is easily predicted that more researches will be conducted using the new sensor in the foreseeable future. We have developed a plant growth measurement method for strawberries using the active-stereo type Kinect sensor and have investigated the measurement accuracies of plant height and width and the area of leaves using a potted strawberry plant (Yamamoto et al., 2012). In this study, we propose an algorithm for a 3D measurement of a plant community of strawberries on a 1-meter long bench. We then report the results of a three-month observation of the plant community.","PeriodicalId":11762,"journal":{"name":"Environmental Control in Biology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Control in Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2525/ECB.53.49","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
引用次数: 8
Abstract
Monitoring plant health is an important technique to guarantee high quality and quantity of agricultural production. In case of greenhouse-grown strawberry plants, farmers generally measure the plants’ height, number of leaves, and area of a single leaf to obtain growth information and estimate the plant’s health. These indices have been used successfully for many years in traditional strawberrygrowing. However, this method of monitoring plant health in an industrial-scale greenhouse would take up a lot of time. Furthermore, traditional growth information is not usually obtained every day. Even when the information is obtained, it involves taking only a sample value, which does not accurately report the condition of every plant. A circulating-type movable bench system for strawberry cultivation has been studied in Japan (Yoshida et al., 2008; Hayashi et al., 2011; Saito et al., 2012). With this cultivation system, all plants pass daily through a single location, called the access point, to be watered. The access point is thus the ideal place for continually and precisely obtaining information on the growth of strawberry plants. Machine vision is a promising technique for effectively monitoring every plant at the access point, since it enables non-destructive measurement. For effective plant health monitoring, many imaging techniques such as digital color imaging, multi-spectral imaging, thermal imaging and fluorescence imaging have been investigated (Takayama and Nishina, 2009). On the other hand, the availability of low-cost depth sensors that generate depth images is on the rise because of the development of natural user interfaces (NUIs), as seen in Microsoft’s Kinect sensor, which is commonly used as a motion controller for TV games. The accuracy of the Kinect sensor has been reported as a percentage of measurement error: between 2 cm to 2 cm is 90.9%, 82.9% and 81.2% for data along the x, y and z axes of the camera coordinates, respectively, using an uncalibrated Kinect sensor (Khoshelham and Elberink, 2012). In the camera coordinates, x and y axes are included in the CMOS plane of the camera. Namely, x axis is horizontal direction in the plane, and y axis is vertical direction. Direction of z axis is equivalent to the normal vector of the plane. For the leaf segmentation in rosebushes and the measurement of the leaf angle of ornamental plants, the low-cost depth sensor has been applied (Chéné et al., 2012). The Kinect sensor of these researches was equipped with an active-stereo system to obtain depth information. In 2014, new Kinect sensor was released which measures the depth by way of timeof-flight method. It is easily predicted that more researches will be conducted using the new sensor in the foreseeable future. We have developed a plant growth measurement method for strawberries using the active-stereo type Kinect sensor and have investigated the measurement accuracies of plant height and width and the area of leaves using a potted strawberry plant (Yamamoto et al., 2012). In this study, we propose an algorithm for a 3D measurement of a plant community of strawberries on a 1-meter long bench. We then report the results of a three-month observation of the plant community.
植物健康监测是保证农业生产保质保量的重要技术手段。对于温室栽培的草莓植株,农民一般通过测量植株的高度、叶片数量和单叶面积来获取植株的生长信息和估计植株的健康状况。这些指标已在传统草莓种植中成功应用多年。然而,在工业规模的温室中,这种监测植物健康的方法将花费大量时间。此外,传统的增长信息通常不是每天都能获得的。即使获得了信息,它也只涉及一个样本值,这并不能准确地报告每个植物的状况。日本研究了一种用于草莓栽培的循环式移动工作台系统(Yoshida et al., 2008;Hayashi et al., 2011;Saito et al., 2012)。有了这个栽培系统,所有的植物每天都要经过一个叫做接入点的地方来浇水。因此,接入点是连续准确地获取草莓植物生长信息的理想场所。机器视觉是一种很有前途的技术,可以在接入点有效地监测每个工厂,因为它可以进行非破坏性测量。为了有效地监测植物健康,研究了许多成像技术,如数字彩色成像、多光谱成像、热成像和荧光成像(Takayama和Nishina, 2009)。另一方面,由于自然用户界面(NUIs)的发展,产生深度图像的低成本深度传感器的可用性正在上升,例如微软的Kinect传感器,它通常被用作电视游戏的运动控制器。Kinect传感器的精度被报告为测量误差的百分比:在2厘米到2厘米之间,使用未校准的Kinect传感器,沿着相机坐标的x、y和z轴的数据分别为90.9%、82.9%和81.2%。在相机坐标中,x轴和y轴包含在相机的CMOS平面中。即x轴为平面内水平方向,y轴为垂直方向。z轴的方向等于平面的法向量。对于玫瑰丛的叶片分割和观赏植物叶片角度的测量,采用了低成本的深度传感器(ch奈斯et al., 2012)。本研究的Kinect传感器配备了主动立体系统来获取深度信息。2014年,新的Kinect传感器发布,通过飞行时间法测量深度。很容易预测,在可预见的未来,将会有更多的研究使用这种新型传感器。我们开发了一种使用主动立体Kinect传感器的草莓植物生长测量方法,并使用盆栽草莓植物研究了植物高度、宽度和叶片面积的测量精度(Yamamoto et al., 2012)。在这项研究中,我们提出了一种算法,用于在1米长的长凳上对草莓植物群落进行三维测量。然后,我们报告了对植物群落为期三个月的观察结果。