PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants

<div class="floats-full-page floats-full-page-other"><h2>Table 3</h2><div class="floats-full-page-caption"><div>The quantitative measures of PSegNet on semantic segmentation.</div></div><div class="floats-full-page-content"><table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="7"><hr /></td></tr><tr class="thead"><td class="align_left" rowspan="2"></td><td class="align_center" colspan="2">Tobacco</td><td class="align_center" colspan="2">Tomato</td><td class="align_center" colspan="2">Sorghum</td></tr><tr class="thead"><td class="align_center">Stem</td><td class="align_center">Leaf</td><td class="align_center">Stem</td><td class="align_center">Leaf</td><td class="align_center">Stem</td><td class="align_center">Leaf</td></tr><tr><td class="thead-hr" colspan="7"><hr /></td></tr><tr><td class="align_left">Prec (%)</td><td class="align_center">92.71</td><td class="align_center">96.76</td><td class="align_center">96.36</td><td class="align_center">97.98</td><td class="align_center">89.54</td><td class="align_center">98.04</td></tr><tr><td class="align_left">Rec (%)</td><td class="align_center">87.42</td><td class="align_center">97.73</td><td class="align_center">95.02</td><td class="align_center">98.59</td><td class="align_center">85.69</td><td class="align_center">98.63</td></tr><tr><td class="align_left">F1 (%)</td><td class="align_center">89.99</td><td class="align_center">97.24</td><td class="align_center">95.68</td><td class="align_center">98.29</td><td class="align_center">87.57</td><td class="align_center">98.33</td></tr><tr><td class="align_left">IoU (%)</td><td class="align_center">81.79</td><td class="align_center">94.63</td><td class="align_center">91.73</td><td class="align_center">96.63</td><td class="align_center">77.89</td><td class="align_center">96.72</td></tr><tr class="table-tr"><td colspan="7"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 3 | PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants

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Table 3: PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants

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Table 3

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<p>The open access journal <em>Plant Phenomics</em>, published in association with NAU, publishes novel research that advances plant phenotyping and connects phenomics with other research domains.</p>


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<p><em>Plant Phenomics'</em> editorial board is led by Seishi Ninomiya (University of Tokyo), Frédéric Baret (French National Research Institute for Agriculture, Food and Environment), and Zong-Ming Cheng (Nanjing Agricultural University/University of Tennessee) and is comprised of leading experts in the field.</p>


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<p>Open to submit: <strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/plant-functional-phenotyping/">Plant Functional Phenotyping</a></strong>; <strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/time-series-phenotyping/">Advances in Proximal Sensing and Image Analysis for Time-Series Phenotyping</a></strong>​​​​​; <strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/pma2022/">PMA2022</a></strong>;&nbsp;<strong><a href="https://spj.sciencemag.org/plant-phenomics-special-issues/object-detection/">Object Detection and Image Segmentation for Plant Phenotyping</a></strong></p>

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Plant Phenomics | Science Partner Journal

The open access journal Plant Phenomics, published in association with NAU, publishes novel research that advances plant phenotyping and connects phenomics with other research domains.