Portable device for contactless, non-destructive and in situ outdoor individual leaf area measurement
•Portable device to capture, segment and measure individual leaves surface area.•Device utilizes a Kinect v2 that can capture 3D data of plants outdoors in daylight.•Leaf segmentation procedure does not require pre-training and was easy to set-up.•Leaf area can be measured non-destructively and from...
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creator | Yau, Weng Kuan Ng, Oon-Ee Lee, Sze Wei |
description | •Portable device to capture, segment and measure individual leaves surface area.•Device utilizes a Kinect v2 that can capture 3D data of plants outdoors in daylight.•Leaf segmentation procedure does not require pre-training and was easy to set-up.•Leaf area can be measured non-destructively and from-a-distance outdoors.•Developed process capable of segmenting and measuring area of individual leaves well.
Plant phenotyping is a research area concerned with the quantitative measurement of a plant’s structural and functional properties. In the case of measuring a leaf’s surface area, it is more often than not laborious as well as stressful to the plant. In this paper, we present the use of the RGB-D sensor, Kinect v2 as part of a portable device for non-destructive measurements of individual leaf areas (cm2/leaf) outdoors in daylight. The Kinect v2 was utilized to capture a single viewpoint 2.5D frame of plant foliage. An unsupervised clustering method, HDBSCAN was used to segment out individual leaves from the captured 2.5D frame of the subject plant. Performance of the leaf segmentation was measured by evaluating the 10 nearest (max) clusters from the sensor for each frame into 3 different categories, individual leaves (non-occluded, occluded), under-segmented and over-segmented. Probability of segmenting individual leaves differs from plant to plant, ranging from a low of 0.7178 to a high of 0.8975. The surface area of all individual non-occluded leaves obtained via the segmentation method was calculated and compared to its ground truth. The calculated individual leaf surface areas R2 was recorded to range from 0.792 to 0.911 with respect to its best fit regression line while the RMSE range from 4.9482 to 14.4941 cm2. The proposed system and method was shown to be capable of segmenting individual leaves from dense foliage and measuring its surface area. |
doi_str_mv | 10.1016/j.compag.2021.106278 |
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Plant phenotyping is a research area concerned with the quantitative measurement of a plant’s structural and functional properties. In the case of measuring a leaf’s surface area, it is more often than not laborious as well as stressful to the plant. In this paper, we present the use of the RGB-D sensor, Kinect v2 as part of a portable device for non-destructive measurements of individual leaf areas (cm2/leaf) outdoors in daylight. The Kinect v2 was utilized to capture a single viewpoint 2.5D frame of plant foliage. An unsupervised clustering method, HDBSCAN was used to segment out individual leaves from the captured 2.5D frame of the subject plant. Performance of the leaf segmentation was measured by evaluating the 10 nearest (max) clusters from the sensor for each frame into 3 different categories, individual leaves (non-occluded, occluded), under-segmented and over-segmented. Probability of segmenting individual leaves differs from plant to plant, ranging from a low of 0.7178 to a high of 0.8975. The surface area of all individual non-occluded leaves obtained via the segmentation method was calculated and compared to its ground truth. The calculated individual leaf surface areas R2 was recorded to range from 0.792 to 0.911 with respect to its best fit regression line while the RMSE range from 4.9482 to 14.4941 cm2. The proposed system and method was shown to be capable of segmenting individual leaves from dense foliage and measuring its surface area.</description><identifier>ISSN: 0168-1699</identifier><identifier>EISSN: 1872-7107</identifier><identifier>DOI: 10.1016/j.compag.2021.106278</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>3D sensor ; Clustering ; Foliage ; Kinect v2 ; Leaf area measurement ; Leaf segmentation ; Leaf surface reconstruction ; Leaves ; Mathematical analysis ; Point cloud data (pcd) ; Portable equipment ; Segmentation ; Statistical analysis ; Surface area</subject><ispartof>Computers and electronics in agriculture, 2021-08, Vol.187, p.106278, Article 106278</ispartof><rights>2021 Elsevier B.V.</rights><rights>Copyright Elsevier BV Aug 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c297t-cc3db3597a3d7b3a568f73e61463efd422343ba81c963d01d397e8167d2281073</citedby><cites>FETCH-LOGICAL-c297t-cc3db3597a3d7b3a568f73e61463efd422343ba81c963d01d397e8167d2281073</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0168169921002957$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Yau, Weng Kuan</creatorcontrib><creatorcontrib>Ng, Oon-Ee</creatorcontrib><creatorcontrib>Lee, Sze Wei</creatorcontrib><title>Portable device for contactless, non-destructive and in situ outdoor individual leaf area measurement</title><title>Computers and electronics in agriculture</title><description>•Portable device to capture, segment and measure individual leaves surface area.•Device utilizes a Kinect v2 that can capture 3D data of plants outdoors in daylight.•Leaf segmentation procedure does not require pre-training and was easy to set-up.•Leaf area can be measured non-destructively and from-a-distance outdoors.•Developed process capable of segmenting and measuring area of individual leaves well.
Plant phenotyping is a research area concerned with the quantitative measurement of a plant’s structural and functional properties. In the case of measuring a leaf’s surface area, it is more often than not laborious as well as stressful to the plant. In this paper, we present the use of the RGB-D sensor, Kinect v2 as part of a portable device for non-destructive measurements of individual leaf areas (cm2/leaf) outdoors in daylight. The Kinect v2 was utilized to capture a single viewpoint 2.5D frame of plant foliage. An unsupervised clustering method, HDBSCAN was used to segment out individual leaves from the captured 2.5D frame of the subject plant. Performance of the leaf segmentation was measured by evaluating the 10 nearest (max) clusters from the sensor for each frame into 3 different categories, individual leaves (non-occluded, occluded), under-segmented and over-segmented. Probability of segmenting individual leaves differs from plant to plant, ranging from a low of 0.7178 to a high of 0.8975. The surface area of all individual non-occluded leaves obtained via the segmentation method was calculated and compared to its ground truth. The calculated individual leaf surface areas R2 was recorded to range from 0.792 to 0.911 with respect to its best fit regression line while the RMSE range from 4.9482 to 14.4941 cm2. The proposed system and method was shown to be capable of segmenting individual leaves from dense foliage and measuring its surface area.</description><subject>3D sensor</subject><subject>Clustering</subject><subject>Foliage</subject><subject>Kinect v2</subject><subject>Leaf area measurement</subject><subject>Leaf segmentation</subject><subject>Leaf surface reconstruction</subject><subject>Leaves</subject><subject>Mathematical analysis</subject><subject>Point cloud data (pcd)</subject><subject>Portable equipment</subject><subject>Segmentation</subject><subject>Statistical analysis</subject><subject>Surface area</subject><issn>0168-1699</issn><issn>1872-7107</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kE9LxDAQxYMouK5-Aw8Br3Ztkm6SXgRZ_AcLetBzyCZTSekma5IW_PZmqWdPwwzvzcz7IXRN6hWpCb_rVybsD_prRWtKyohTIU_QgkhBK0FqcYoWRSYrwtv2HF2k1Nelb6VYIHgPMevdANjC5AzgLkRsgs_a5AFSusU--MpCynE02U2AtbfYeZxcHnEYsw3F4Lx1k7OjHvAAusM6gsZ70GmMsAefL9FZp4cEV391iT6fHj82L9X27fl187CtDG1FroxhdsfWrdDMih3Tay47wYCThjPobEMpa9hOS2JazmxNLGsFSMKFpVSWnGyJbua9hxi-x_K06sMYfTmp6JqTljf1WhZVM6tMDClF6NQhur2OP4rU6ghU9WoGqo5A1Qy02O5nG5QEk4OoknHgDVgXwWRlg_t_wS9Ti4EO</recordid><startdate>202108</startdate><enddate>202108</enddate><creator>Yau, Weng Kuan</creator><creator>Ng, Oon-Ee</creator><creator>Lee, Sze Wei</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>202108</creationdate><title>Portable device for contactless, non-destructive and in situ outdoor individual leaf area measurement</title><author>Yau, Weng Kuan ; Ng, Oon-Ee ; Lee, Sze Wei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c297t-cc3db3597a3d7b3a568f73e61463efd422343ba81c963d01d397e8167d2281073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>3D sensor</topic><topic>Clustering</topic><topic>Foliage</topic><topic>Kinect v2</topic><topic>Leaf area measurement</topic><topic>Leaf segmentation</topic><topic>Leaf surface reconstruction</topic><topic>Leaves</topic><topic>Mathematical analysis</topic><topic>Point cloud data (pcd)</topic><topic>Portable equipment</topic><topic>Segmentation</topic><topic>Statistical analysis</topic><topic>Surface area</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yau, Weng Kuan</creatorcontrib><creatorcontrib>Ng, Oon-Ee</creatorcontrib><creatorcontrib>Lee, Sze Wei</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers and electronics in agriculture</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yau, Weng Kuan</au><au>Ng, Oon-Ee</au><au>Lee, Sze Wei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Portable device for contactless, non-destructive and in situ outdoor individual leaf area measurement</atitle><jtitle>Computers and electronics in agriculture</jtitle><date>2021-08</date><risdate>2021</risdate><volume>187</volume><spage>106278</spage><pages>106278-</pages><artnum>106278</artnum><issn>0168-1699</issn><eissn>1872-7107</eissn><abstract>•Portable device to capture, segment and measure individual leaves surface area.•Device utilizes a Kinect v2 that can capture 3D data of plants outdoors in daylight.•Leaf segmentation procedure does not require pre-training and was easy to set-up.•Leaf area can be measured non-destructively and from-a-distance outdoors.•Developed process capable of segmenting and measuring area of individual leaves well.
Plant phenotyping is a research area concerned with the quantitative measurement of a plant’s structural and functional properties. In the case of measuring a leaf’s surface area, it is more often than not laborious as well as stressful to the plant. In this paper, we present the use of the RGB-D sensor, Kinect v2 as part of a portable device for non-destructive measurements of individual leaf areas (cm2/leaf) outdoors in daylight. The Kinect v2 was utilized to capture a single viewpoint 2.5D frame of plant foliage. An unsupervised clustering method, HDBSCAN was used to segment out individual leaves from the captured 2.5D frame of the subject plant. Performance of the leaf segmentation was measured by evaluating the 10 nearest (max) clusters from the sensor for each frame into 3 different categories, individual leaves (non-occluded, occluded), under-segmented and over-segmented. Probability of segmenting individual leaves differs from plant to plant, ranging from a low of 0.7178 to a high of 0.8975. The surface area of all individual non-occluded leaves obtained via the segmentation method was calculated and compared to its ground truth. The calculated individual leaf surface areas R2 was recorded to range from 0.792 to 0.911 with respect to its best fit regression line while the RMSE range from 4.9482 to 14.4941 cm2. The proposed system and method was shown to be capable of segmenting individual leaves from dense foliage and measuring its surface area.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.compag.2021.106278</doi></addata></record> |
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subjects | 3D sensor Clustering Foliage Kinect v2 Leaf area measurement Leaf segmentation Leaf surface reconstruction Leaves Mathematical analysis Point cloud data (pcd) Portable equipment Segmentation Statistical analysis Surface area |
title | Portable device for contactless, non-destructive and in situ outdoor individual leaf area measurement |
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