[S03-P] International Symposium on Innovative Technologies and Production Strategies for Smart Greenhouse | IHC2026

Session Details

PosterInternational Symposium on Innovative Technologies and Production Strategies for Smart Greenhouse

[S03-P]International Symposium on Innovative Technologies and Production Strategies for Smart Greenhouse

Tue. Aug 25, 2026 2:00 PM - 3:00 PM JST
Tue. Aug 25, 2026 5:00 AM - 6:00 AM UTC

Event Hall and New Hall-S03P

[S03-P-01]Estimation of Leaf Area Index and Light Interception Using Scattered Near-Infrared (NIR) and Visible (VIS) Light

*Yuko Ogo¹, Yusuke Kakei¹, Naoki Ohishi², Natsuru Futamata², Dong-Hyuk Ahn¹ (1. National Agriculture and Food Research Organization (Japan), 2. Advanced Agriculture Division Shizuoka, Prefectural Research Institute of Agric. and Forest (Japan))

[S03-P-02]Dry Matter Production-Based Crop Model for Greenhouse Strawberries

*Tomomi Sugiyama¹, Yusuke Kakei¹, Yasunaga Iwasaki², Atsushi Oda¹, Masahide Isozaki¹ (1. National Agriculture and Food Research Organization (Japan), 2. Meiji University (Japan))

[S03-P-03]Optimized Measurement Protocol and Comparative Evaluation of Light-Response Models for Determining the Light-Saturation Point in Lettuce

*Jeongmin Yun¹, Ryukyoung Oh¹, Joonwoo Lee¹ (1. Jeonbuk National University (Korea))

[S03-P-04]Analysis of Environmental Factors Influencing Pollen Germination in Strawberry Using a Robust Deep Learning Model

*Akira Yamazaki¹, Natsuki Uematsu², Taro Sano³, Yoshito Tatsumi³, Keiji Mine³, Hiroshi Asao³, Haruyuki Atsumi³, Yuhei Ueda³, Ginyu Inaba¹, Kyosuke Kawabe⁴, Kenichiro Yasuba⁴, Yuichi Yoshida⁴ (1. Kindai University (Japan), 2. Tokushima Agriculture, Forestry, and Fisheries Technology Support Center (Japan), 3. Nara Prefecture Integrated Agricultural Research Center (Japan), 4. Okayama University (Japan))

[S03-P-05]Assessing Inversion, Statistical Models, and Physics-Informed Learning Methods for Stomatal Parameter Estimation

*Yu Hyun Moon¹, Ha Seon Sim, Tae Yeon Lee, Jinwoo Lee, Tae Hyun Kim, Jung Hyuk Roh, Sumin Lee, Shin Ae Kwon, Yu Kyeong Shin, Sung Kyeom Kim (1. Kyungpook National University)

[S03-P-06]Temperature-Dependent Modeling of Leaf Emergence and Expansion with Leaf-Order Effects in Kale

*Seungwon Seok¹, Jaeyoung Jung, Solhee Kim, Taegon Kim (1. Jeonbuk National University)

[S03-P-07]Development of Leaf Temperature Sensors Using Thermal Plant Models

*Yuta Ohashi^1,2, Ryota Tsuchiya¹, Masahisa Ishii¹, Makio Hayashi¹ (1. Institute for Rural Engineering, NARO (Japan), 2. Faculty of Agriculture, Takasaki University of Health and Welfare (Japan))

[S03-P-08]Effect of Fruit Thinning Guided by Machine Learning Based Fruit Weight Prediction in Greenhouse Tomato Cultivation

*Masaaki Takahashi¹, Yasushi Kawasaki¹, Unseok Lee¹, Koichi Yoshi¹ (1. National Agriculture and Food Research Organization (Japan))

[S03-P-09]A Geometry-Augmented Hierarchical Gompertz Model for Strawberry Fruit Weight

*Tae Yeon Lee¹, Jinwoo Lee¹, Ha Seon Sim¹, Yu Hyun Moon¹, Tae Hyun Kim¹, Jung Hyuk Roh¹, Sumin Lee¹, Shin Ae Kwon¹, Yu Kyeong Shin², Sung Kyeom Kim^1,3 (1. Kyungpook National University (Korea), 2. National Institute of Horticultural and Herbal Science (Korea), 3. Worlds Horti Center (Korea))

[S03-P-10]Predicting Cherry Tomato Fruit Ripeness Based on Non-Invasive Spectrophotometric Colour Measurement and Multiple Regression Modelling

*Klaudia Blahut¹, Katarzyna Mazur, Jaroslaw Przybyl, Marek Gajewski (1. Szkoła Główna Gospodarstwa Wiejskiego w Warszawie)

[S03-P-11]A CFD–UDF Framework for Predicting Spatially Resolved Leaf Temperature

*Anh Khoa To¹, Il-Hwan Seo², Siwon Yoon³ (1. Department of AI in Agriculture and Life Sciences, Jeonbuk National University (Korea), 2. Department of Rural Construction Engineering, Jeonbuk National University (Korea), 3. Department of Smart Farm, Jeonbuk National University (Korea))

[S03-P-12]Simulation of Tomato Yield Under LED Interlighting in Greenhouse

*Mizuho Itoh¹, Yusuke Kakei¹, Atsushi Oda¹, Dong-Hyuk Ahn¹ (1. National Agriculture and Food Research Organization (Japan))

[S03-P-13]Comparison for Intercepted Light per Plant Canopy Between the Methods Using Monsi–Saeki Theory and 3-D Depth Camera: A Case Study in Sweet Pepper, Tomato, and Strawberry Production

*Masaru Homma¹, Takaya Ishii¹, Phyo Han Thwin², Masahide Isozaki¹ (1. National Agriculture and Food Research Organization (Japan), 2. The University of Arizona (USA))

[S03-P-14]Effect of Organic Photovoltaic Deployment Duration in Greenhouses on the Physiological Characteristics and Yield of Oriental Melons

*Chia-Hsun Ho¹, Min-Jui Chiang¹, Ming-Hwi Yao¹, Mei-Kuan Lee¹, Wei-Ting Lin¹, Hua-Tse Liao¹ (1. Taiwan Agricultural Research Institute (Taiwan))

[S03-P-15]Evaluation of Growth and Yield of Two Leafy Vegetables Under an Agrivoltaic Simulated Greenhouse Roof

*Chiao Ling Hsiao¹, Ming Hwi Yao¹, Chia Hsun Ho¹, Ya Ling Hou¹ (1. Taiwan Agricultural Research Institute (Taiwan))

[S03-P-16]Effects of Supplemental Lighting on the Growth, Fruit Quality and Postharvest Physiology of Greenhouse Produced Bell Peppers (Capsicum annuum L.)

*Yuan-Kai Tu¹, Yu-Shen Liang², Hsing-Ying Chung² (1. Crop Genetic Resources and Biotechnology Division, Taiwan Agricultural Research Institute, Ministry of Agriculture (Taiwan), 2. Department of Plant Industry, National Pingtung University of Science and Technology (Taiwan))

[S03-P-17]Assessment of Photoinhibition Responses in Lettuce and Cabbage via Simultaneous Photosynthetic and Fluorescence Measurements

*Ryukyoung Oh¹, Joonwoo Lee¹ (1. Jeonbuk National University (Korea))

[S03-P-18]Blue Light Supplementation at Night in Combination with Brassinosteroids Treatment Enhances Both Tomato Fruit Yield and Quality

*Wentian Xin¹, Xiaojian Xia (1. Zhejiang University)

[S03-P-19]Phenotyping of Plant Growth and Quality in Controlled Environments

*Laura Jaakola^1,2, Marius Dobbe Klemetsen^1,2, Thomas Bawin^1,3, Wojciech Leoniuk¹, Katja Karppinen¹ (1. UiT The Arctic University of Norway (Norway), 2. Norwegian Institute of Bioeconomy (NIBIO) (Norway), 3. Norwegian University of Life Sciences (NMBU) (Norway))

[S03-P-20]Optimization of Tomato Seedling Production Using LED Lighting

*Miguel Joel Bailon Peña¹, J. Jesus Magdaleno Villar¹, Felipe Sanchez del Castillo¹ (1. Universidad Autónoma Chapingo (Mexico))

[S03-P-21]Balancing Photosynthesis with PSI Excitation Spectra for Greenhouse Production

*Satu Engström¹, Tuomo Laine, Pedro Aphalo, Titta Kotilainen, Esa Tyystjärvi, Alexey Shapiguzov (1. Natural Resources Institute Finland (Luke))

[S03-P-22]Improving ‘Amara’ Mustard Greens (Brassica carinata) Growth, Nutritional Quality, and Post-Harvest Longevity via End-of-Production Lighting

*Jacob Friedrich¹, Sarah Armstrong¹, W. Garrett Owen², Kellie Walters¹ (1. University of Tennessee, Knoxville (USA), 2. The Ohio State University (USA))

[S03-P-23]Effects of Different Light Distribution Strategies on the Quality of Cut Roses

*Hsingying Chung¹, Chuanh Jui Che, Yuan Kai Tu, Chien Hsing Chen, Chia Ying Chang, Yu Shen Liang, Wei Fang (1. National Pingtung University of Science and Technology)

[S03-P-24]Early and Late Blight Detection in a Greenhouse Tomato Crop by Means of Multispectral Imaging

*Nikolaos Katsoulas¹, Dimitrios Kapetas², Ioannis Naounoulis¹, Sofia Faliagka¹, Eleftheria Pechlivani² (1. University of Thessaly (Greece), 2. Centre for Research & Technology, Hellas (Greece))

[S03-P-25]LED Spectral Quality and Lighting Regime Modulate Productivity and Oxidative Metabolism in Aeroponic Seed Potato Cultivation

*Jose Magno Luz¹, Fábia B Silva², Fabiano G Silva², Márcio Rosa³, Carlos H P Bento² (1. Federal University of Uberlândia (Brazil), 2. Goiano Federal Institute, Rio Verde Campus (Brazil), 3. University of Rio Verde (Brazil))

[S03-P-26]Photosynthetic Imaging Combining Chlorophyll Fluorescence and PPFD Distribution Analysis

*Yasuomi Ibaraki¹, Tomonori Saeki¹ (1. Yamaguchi University (Japan))

[S03-P-27]Blue Light and Water Deficiency Modulate Growth and Water Dynamics in Cucumber via Aquaporin Regulation

*Qingming Li

[S03-P-28]Development of an Optimization Framework for Profit-Driven Greenhouse Control

*Yusuke Kakei¹, Dong-hyuk Ahn¹ (1. National Agriculture and Food Research Organization (Japan))

[S03-P-29]Utilizing Heat Storage Method with a Ground Source Heat Pump Can Provide More Efficient Nighttime Heating for Greenhouses

*Kotaro Seki¹, Takashi Miki¹, Yukiyoshi Iwata², Masayuki Tateno³, Takayoshi Igarashi³, Shinji Takasugi³, Masahisa Ishii¹ (1. National Agriculture and Food Research Organization (Japan), 2. Kyushu University (Japan), 3. Geo-system Co.,Ltd. (Japan))

[S03-P-30]Physics-Informed Neural Networks for Analyzing Indoor Environment and Flow Characteristics in Smart Farm Greenhouses

*Sangik Lee¹, Anis Adewale Rabiu¹, Jong-hyuk Lee¹, Jaewon Choi¹, Jungwoo Bae¹, Sunyoung Yeo¹, Seoyun Kim¹, Hyeon Ryu¹ (1. Kyungpook National University (Korea))

[S03-P-31]Development of a Web-Based Interface of Greenhouse Energy Simulation API Using the Agro-Meteorological Grid Square Data System for Kochi Prefecture Users in Japan

*Takeshi Kuroyanagi¹, Yudai Goto², Marina Tsutsui², Mitsuki Shimomoto³ (1. Western Region Agricultural Research Center, National Agriculture and Food Research Organization (Japan), 2. Kochi Agricultural Research Center (Japan), 3. Kochi prefecture (Japan))

[S03-P-32]Diurnal Changes in the Number of Bumblebees Entering and Exiting the Nest Box in a Commercial Strawberry Greenhouse: A Case Study

*Rina Kojima¹, Katsumi Ohyama, Hiroki Naito, Norio Hirai (1. Osaka Metropolitan University)

[S03-P-33]Virtual Greenhouses for Cucumber Harvesting Robots

*Joseph Peller¹, Arjan Vroegop¹, Maarten van der Meer¹ (1. Wageningen University and Research (Netherlands))

[S03-P-34]Technical Development Strategy and On-Site Demonstration for Expanding Smart Greenhouse Horticulture in Southeast Asia

*Tadahisa Higashide¹, Atsushi Oda¹, Masaru Homma¹, Katsutomo Sasaki¹, Dong-Hyuk Ahn¹, Masahide Isozaki¹ (1. National Agriculture and Food Research Organization (Japan))

[S03-P-35]Smart Electricity-Price Based Lighting Control in Greenhouses

Quade Digweed¹, Jason Lanoue¹, *Xiuming Hao¹ (1. Agriculture and Agri-Food Canada (Canada))

[S03-P-36]Dynamic Web-Based Visualization of Psychrometric and Mollier Diagrams for Greenhouse Microclimate Analysis

*Ozik Putra Jarwo¹, Jeong-Won Yoon¹, Gi-Min Im¹, Hyun-Mun Kim¹, Kyoung Sub Park^1,2 (1. Department of Horticultural Science, Mokpo National University (Korea), 2. Global Smart Agribusiness Research Center, Mokpo National University (Korea))

[S03-P-37]A Physics-Based Greenhouse Model for Microclimate Estimation

Bo-Seong Seo¹, *Siwon Yoon^1,2, Anh To Khoa¹ (1. Department of AI in Agriculture, Jeonbuk National University (Korea), 2. Department of Smart Farm, Jeonbuk National University (Korea))

[S03-P-38]Environmental Control for Strawberry Production in Japan

*Masahide Isozaki¹, Atsushi Oda¹, Tadahisa Higashide¹ (1. National Agriculture and Food Research Organization (Japan))

[S03-P-39]Evaluation of Reference Evapotranspiration Models Under Diverse Greenhouse Conditions

*Junwoo Moon¹, Joonwoo Lee¹ (1. Jeonbuk National University (Korea))

[S03-P-40]Integrated Crop Growth Monitoring System with Raspberry Pi and Image Processing for Indoor Hydroponics

*Liping Wang¹, Menuka Jayalath, Rona Lei Duldulao, Lyne Salero (1. University of Wyoming)

[S03-P-41]Analysis of Flower Visitation Frequency by Green Blow Fly and Incidence of Malformed Fruit Using AI in Strawberries

*Ken-ichiro Yasuba¹, Rinka Ishii¹, Ko Motoki¹, Minori Hikawa-Endo¹, Natsuki Uematsu^1,2, Daisuke Miyaji¹ (1. Okayama University (Japan), 2. Tokushima Prefectural Agricultural, Forestry and Fisheries Technology Support Center (Japan))

[S03-P-42]Applying Artificial Intelligence to Optimize the Industrial Chain and Precision Production of Phalaenopsis

*Yi-Chien Lu¹, Rong-Show Shen, Chien-Chuan Ko, Tzyy-Chyang Lu (1. National Chiayi University)

[S03-P-43]Early Detection of Water Quality Shifts and Pythium in Hydroponic Systems Using a Low-Cost Real-Time Sensor System in Urban Agriculture

*Ankit Singh¹, Caitlin Howell¹, Liza White¹, Stepanie Burnett¹, Jamieson Snape¹ (1. University of Maine (USA))

[S03-P-44]Development of an Automated Imaging-Based Phenotyping System for Non-Destructive Growth Estimation of Greenhouse Strawberry

*Sooho Jung¹, Gayun Yun¹, Juhye Kim¹, Younsup Cho¹ (1. Jeollanam-do Agricultural Research & Extension Services (Korea))

[S03-P-45]Integrating Hyperspectral Imaging and ¹⁴C Tracer Imaging in a Rhizotron System to Quantify Root Carbon Allocation

*Yuta Miyoshi¹, Fengjiao Lu, Angelo Mencarelli, Viola Willemsen, Joseph Peller, Rick van de Zedde (1. National Institutes for Quantum Science and Technology)

[S03-P-46]Growth Responses of Strawberry to Air Anion Treatment at Different Growth Stages

*Yunsik Kim^1,2, Myung-Min Oh^1,2 (1. Division of Animal, Horticultural and Food Sciences, Chungbuk National University (Korea), 2. Brain Korea 21 Center for Smart GreenBio Convergence and Sustainable Regional Development, Chungbuk National University (Korea))

[S03-P-47]Real-Time Growth Monitoring of Greenhouse Strawberries Using Cultivation Images

*Tomomi Ueki¹, Yasuomi Ibaraki², Kohsuke Harada³ (1. Tottori University (Japan), 2. Yamaguchi University (Japan), 3. Yamaguchi Prefectural Agriculture and Forestry General Technology Center (Japan))

[S03-P-48]Deep Learning-Based Classification of Flower Bud Differentiation Stages in Seolhyang Strawberry for Optimizing Planting Window in Forcing Systems

Ji-Eun Park¹, Kyoung Hee Lee², Jongbin Park¹, Deog-Bae Lee², Sook Yoon³, *Alvaro Fuentes¹ (1. Jeonbuk National University (Korea), 2. Agricultural Technology Center of Wanju-gun (Korea), 3. Mokpo National University (Korea))

[S03-P-49]Towards Non-Destructive Monitoring of Ultrasonic Acoustic Emissions from Plants in Hydroponic Green Building Systems

*Kloster Nina¹, Nick Paul¹ (1. TH Köln – University of Applied Sciences Cologne (Germany))

[S03-P-50]Growth and Nutrient Dynamics of Chrysanthemum in Peat-Alternative Substrates Enriched with Fungal Amendment

*Bozena Pawlowska¹, Bozena Szewczyk-Taranek, Anna Grzesiak (1. University of Agriculture in Krakow)

[S03-P-51]Preparation of a Foundational Dataset for AI-Based Nutrient Prediction in Hydroponic Systems Using Electrical Conductivity, Ion-Selective Electrodes, and ICP-OES Analyses

Andrius Grigas¹, Tomas Krilavicius¹, Ricardas Krikstolaitis¹, Ernestas Zaleckas¹, Rolandas Domeika¹, Mantas Lukosevicius², Ieva Gaide¹, Audrius Adomelis¹, Aiva Stankaityte¹, *Dainius Savickas¹ (1. Vytautas Magnus University (Lithuania), 2. Kaunas University of Technology (Lithuania))

Session Details