Workshop Programme

Due to Covid-19 ECCV and its workshops were held online:

  • All papers, oral or poster, had a pre-recorded video presentation, i.e. 10-minute video. It was made available together with the paper to the attendees about a week before the workshop day.
  • In addition, all contributions had a pre-recorded video 1-min teaser shown in the live Q&A sessions.
  • Every workshop had two sessions of two hours scheduled interaction time during the workshop day. Time slots were 10:00-12:00 and 18:00-20:00 (UTC+1) on 28. August 2020.
  • The two sessions had essentially identical program to allow attendees from all over the world to attend in good conditions. Authors had a ~5min Q&A after presentation of their 1-min teaser in a set of 3 teasers. Please compare with the program given below. Thus, for both sessions, at least one author of every contribution needed to be present.


Time slots were 10:00-12:00 and 18:00-20:00 (UTC+1) on 28. August 2020. Time given is relative to the start time of the session. Presentations were 1-min prerecordeed video teasers. Each set of 3 to 4 teasers was followed by a ~5-7 min Q&A live session.

PDFs of extended abstracts can be found for download below. Full papers will be made available through the official ECCV workshop proceedings. Currently they are available through the ECCV workshop and tutorial site (login required).

0:00 -- 0:07      Welcome
0:07 -- 0:16      Detection and Classification

  • Detection in agricultural contexts: Are we close to human level? Omer Wosner, Guy Farjon, Faina Khoroshevsky, Aharon Bar-Hillel
  • Expanding CNN-based Plant Phenotyping Systems to Larger Environments. Jonas Krause, Kyungim Baek, Lipyeow Lim
  • Patch-based CNN evaluation for bark classification. Debaleena MISRA, Carlos Crispim-Junior, Laure Tougne

0:16 -- 0:25      Classification and Scoring

  • Automatic Differentiation of Damaged and Unharmed Grapes Using RGB Images and Convolutional Neural Networks. Laura Zabawa, Jonas Bömer, Philipp Sieren, Ribana Roscher, Lasse Klingbeil, Uwe Rascher, Heiner Kuhlmann, Onno Muller, Anna Kicherer
  • Abiotic Stress Prediction from RGB-T Images of Banana Plantlets. Sagi Levanon, Oshry Markovich, Itamar Gozlan, Ortal Bakhshian, Alon Zvirin, Yaron Honen, Ron Kimmel
  • Germination Detection of Seedlings in Soil: A System, Dataset and Challenge. Hanno Scharr, Benjamin Bruns, Andreas Fischbach, Johanna Roussel, Lukas Scholtes, Jonas vom Stein

0:25 -- 0:34      Field Tools

  • CorNet: Unsupervised Deep Homography Estimation on Agriculture Aerial Imagery. Toni Kazic, Dewi Kharismawati, Hadi Aliakbarpour, Rumana Aktar, Filiz Bunyak, Kannappan Palaniappan
  • EasyRFP: An Easy to Use Edge Computing Toolkit for Real-time Field Phenotyping. Sai Vikas Desai, Akshay L Chandra, Masayuki Hirafuji, Seishi Ninomiya, Vineeth N Balasubramanian, Wei Guo [pdf]
  • Towards Confirmable Automated Plant Cover Determination. Matthias Körschens, Christine Römermann, Paul Bodesheim, Solveig Franziska Bucher, Josephine Ulrich, Joachim Denzler

0:34 -- 0:43      In Field Operation

  • Development of a UAV image Dataset for Cauliflowers Ripeness Classi cation with Deep Learning. Johan De Groot, O. Enrique Apolo-Apolo, João Valente [pdf]
  • A Deep Learning-Based In-field Fruit Counting Method Using Video Sequences. Jiaqi Wang, Wenli Zhang, Kaizhen Chen, Huibin Li, Wei Guo, Yun Shi [pdf]
  • Automated vegetable growth analysis from outdoor images acquired with a cablebot.. Aldo Sollazzo, David Colliaux, Soroush Garivani, Jonathan Minchin, Lisa Garlanda, Peter Hanappe [pdf]

0:43 -- 0:52      Time series and 3D

  • Time Series Modeling for Phenotypic Prediction and Phenotype-Genotype Mapping using Neural Networks. Sruti Das Choudhury
  • 3D Plant Phenotyping: All You Need is Labelled Point Cloud Data. Ayan Chaudhury, Frederic Boudon, Christophe Godin
  • Sorghum Segmentation by Skeleton Extraction. Mathieu Gaillard, Bedrich Benes, James Schnable, Chenyong Miao

0:52 -- 1:04      Segmentation, Instance Segmentation and Counting

  • Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM. Guohao Yu, Alina Zare, Weihuang Xu, Roser Matamala, Joel Reyes-Cabrera, Felix B. Fritschi, Thomas E. Juenger
  • Phenotyping problems of Parts-per-object count. Faina Khoroshevsky, Stanislav Khoroshevsky, Aharon Bar-Hillel
  • Automatic Calculation of Infection Rate of Arbuscular Mycorrhizal Fungi Using Deep CNN. Kaoru Muta, Shiho Takada, Yuzuko Utsumi, Atsushi Matsumura, Masakazu Iwamura, Koichi Kise [pdf]
  • Improving Pixel Embedding Learning through Intermediate Distance Regression Supervision for Instance Segmentation. Yuli Wu, Long Chen, Dorit Merhof

1:04 -- 1:13      Counting

  • Counting Pollen Viability via Deep Learning. Simon Castle-Green, Michael Pound, Tony Pridmore [pdf]
  • AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images. Jordan Ubbens, Tewodros Ayalew, Steve Shirtliffe, Anique Josuttes, Curtis Pozniak, Ian Stavness
  • Unsupervised Domain Adaptation For Plant Organ Counting. Tewodros Ayalew, Jordan Ubbens, Ian Stavness

1:13 -- 1:30      Global Wheat Detection Challenge

  • Introduction to the Global Wheat Detection Challenge. Etienne David, Ian Stavness
  • Winning Team, 3rd place . Dung Nguyen Ba
  • BTWD: Bag of Tricks for Wheat Detection. YifanWu, YahanHu, Lei Li
  • Trustworthy Detection: Global Wheat Detection Based on Confident Learning. ChangliHuang, Jianqiu Chen

1:30 -- 1:55      Discussion

1:55 -- 2:00      Adjourn

Organized by

IPPN Working Group: Imaging for Phenotyping

Sponsored by




Some example images from data set