title: Node Detection and Internode Length Estimation of Tomato Seedlings Based on Image Analysis and Machine Learning.
abstract: Seedling vigor in tomatoes determines the quality and growth of fruits and total plant productivity. It is well known that the salient effects of environmental stresses appear on the internode length; the length between adjoining main stem node (henceforth called node). In this study, we develop a method for internode length estimation using image processing technology. The proposed method consists of three steps: node detection, node order estimation, and internode length estimation. This method has two main advantages: (i) as it uses machine learning approaches for node detection, it does not require adjustment of threshold values even though seedlings are imaged under varying timings and lighting conditions with complex backgrounds; and (ii) as it uses affinity propagation for node order estimation, it can be applied to seedlings with different numbers of nodes without prior provision of the node number as a parameter. Our node detection results show that the proposed method can detect 72% of the 358 nodes in time-series imaging of three seedlings (recall = 0.72, precision = 0.78). In particular, the application of a general object recognition approach, Bag of Visual Words (BoVWs), enabled the elimination of many false positives on leaves occurring in the image segmentation based on pixel color, significantly improving the precision. The internode length estimation results had a relative error of below 15.4%. These results demonstrate that our method has the ability to evaluate the vigor of tomato seedlings quickly and accurately.
authors.affiliation: Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishi-Tokyo, Tokyo 188-0002, Japan. kyosuke.yamamoto@15.alumni.u-tokyo.ac.jp.|PS Solutions Corp., 1-5-2 Higashi-Shimbashi, Minato-ku, Tokyo 105-7104, Japan. kyosuke.yamamoto@15.alumni.u-tokyo.ac.jp.
authors.forename: Kyosuke
authors.initials: K
authors.lastname: Yamamoto
authors.affiliation: Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishi-Tokyo, Tokyo 188-0002, Japan. guowei@isas.a.u-tokyo.ac.jp.
authors.forename: Wei
authors.initials: W
authors.lastname: Guo
authors.affiliation: Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishi-Tokyo, Tokyo 188-0002, Japan. snino@isas.a.u-tokyo.ac.jp.
authors.forename: Seishi
authors.initials: S
authors.lastname: Ninomiya
mesh_terms: D018548:Cotyledon / Q000033:anatomy & histology; D007091:Image Processing, Computer-Assisted; D018551:Solanum lycopersicum / Q000033:anatomy & histology*; D000069550:Machine Learning; D018515:Plant Leaves / Q000033:anatomy & histology; D018547:Plant Stems / Q000033:anatomy & histology*; D036226:Seedlings / Q000033:anatomy & histology*; D013997:Time Factors
keywords: BoVWs; affinity propagation; image analysis; internode length estimation; machine learning; node detection