Plant phenomics (Washington, D.C.)

Keyphrases

Publications

volume 2024, 2024

Weizhen Liu, Yuxi Chen, Zhaoxin Lu, Xiaoyu Lu, Ze Wu, Ziyao Zheng, Yongqiang Suo, Caixia Lan, Xiaohui Yuan
StripeRust-Pocket: A Mobile-Based Deep Learning Application for Efficient Disease Severity Assessment of Wheat Stripe Rust. Plant phenomics (Washington, D.C.) 2024 (2024)
Weizhen Liu, Yuxi Chen, Zhaoxin Lu, Xiaoyu Lu, Ze Wu, Ziyao Zheng, Yongqiang Suo, Caixia Lan, Xiaohui Yuan
StripeRust-Pocket: A Mobile-Based Deep Learning Application for Efficient Disease Severity Assessment of Wheat Stripe Rust. Plant phenomics (Washington, D.C.) 2024 (2024)

volume 6, 2024

Yaichi Kawakatsu, Ryo Okada, Mitsuo Hara, Hiroki Tsutsui, Naoki Yanagisawa, Tetsuya Higashiyama, Akihide Arima, Yoshinobu Baba, Ken-Ichi Kurotani, Michitaka Notaguchi
Microfluidic Device for Simple Diagnosis of Plant Growth Condition by Detecting miRNAs from Filtered Plant Extracts. Plant phenomics (Washington, D.C.) 6 (2024)
Yandong Wu, Weiliang Wen, Shenghao Gu, Guanmin Huang, Chuanyu Wang, Xianju Lu, Pengliang Xiao, Xinyu Guo, Linsheng Huang
Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence. Plant phenomics (Washington, D.C.) 6 (2024)
Sajid Ullah, Klára Panzarová, Martin Trtílek, Matej Lexa, Vojtěch Máčala, Kerstin Neumann, Thomas Altmann, Jan Hejátko, Markéta Pernisová, Evgeny Gladilin
High-Throughput Spike Detection in Greenhouse Cultivated Grain Crops with Attention Mechanisms-Based Deep Learning Models. Plant phenomics (Washington, D.C.) 6 (2024)
Xun Zhao, Jianbo Qi, Zhexiu Yu, Lijuan Yuan, Huaguo Huang
Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data. Plant phenomics (Washington, D.C.) 6 (2024)
Juan Camilo Rivera Palacio, Christian Bunn, Eric Rahn, Daisy Little-Savage, Paul Schimidt, Masahiro Ryo
Geographic-Scale Coffee Cherry Counting with Smartphones and Deep Learning. Plant phenomics (Washington, D.C.) 6 (2024)
Wenfeng Hu, Weihao Tang, Chuang Li, Jinjing Wu, Hong Liu, Chao Wang, Xiaochuan Luo, Rongnian Tang
Handling the Challenges of Small-Scale Labeled Data and Class Imbalances in Classifying the N and K Statuses of Rubber Leaves Using Hyperspectroscopy Techniques. Plant phenomics (Washington, D.C.) 6 (2024)
Daniel Krafft, Clifton G Scarboro, William Hsieh, Colleen J Doherty, Peter J Balint-Kurti, Michael W Kudenov
Mitigating Illumination-, Leaf-, and View-Angle Dependencies in Hyperspectral Imaging Using Polarimetry. Plant phenomics (Washington, D.C.) 6 (2024)
Jean Velluet, Antonin Della Noce, Véronique Letort
Practical Identifiability of Plant Growth Models: A Unifying Framework and Its Specification for Three Local Indices. Plant phenomics (Washington, D.C.) 6 (2024)
Liming Du, Yong Pang
Identifying Regenerated Saplings by Stratifying Forest Overstory Using Airborne LiDAR Data. Plant phenomics (Washington, D.C.) 6 (2024)
Zhanhao Zhang, Jianmao Guo, Shihui Han, Shuyuan Jin, Lei Zhang
Establishing a Gross Primary Productivity Model by SIF and PRI on the Rice Canopy. Plant phenomics (Washington, D.C.) 6 (2024)
Elizabeth M Berrigan, Lin Wang, Hannah Carrillo, Kimberly Echegoyen, Mikayla Kappes, Jorge Torres, Angel Ai-Perreira, Erica McCoy, Emily Shane, Charles D Copeland, Lauren Ragel, Charidimos Georgousakis, Sanghwa Lee, Dawn Reynolds, Avery Talgo, Juan Gonzalez, Ling Zhang, Ashish B Rajurkar, Michel Ruiz, Erin Daniels, Liezl Maree, Shree Pariyar, Wolfgang Busch, Talmo D Pereira
Fast and Efficient Root Phenotyping via Pose Estimation. Plant phenomics (Washington, D.C.) 6 (2024)
Jiaquan Lin, Jun Li, Zhe Ma, Can Li, Guangwen Huang, Huazhong Lu
A Framework for Single-Panicle Litchi Flower Counting by Regression with Multitask Learning. Plant phenomics (Washington, D.C.) 6 (2024)
Yaichi Kawakatsu, Ryo Okada, Mitsuo Hara, Hiroki Tsutsui, Naoki Yanagisawa, Tetsuya Higashiyama, Akihide Arima, Yoshinobu Baba, Ken-Ichi Kurotani, Michitaka Notaguchi
Microfluidic Device for Simple Diagnosis of Plant Growth Condition by Detecting miRNAs from Filtered Plant Extracts. Plant phenomics (Washington, D.C.) 6 (2024)
Xun Zhao, Jianbo Qi, Zhexiu Yu, Lijuan Yuan, Huaguo Huang
Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data. Plant phenomics (Washington, D.C.) 6 (2024)
Hui Feng, Yongqi Chen, Jingyan Song, Bingjie Lu, Caixia Shu, Jiajun Qiao, Yitao Liao, Wanneng Yang
Maturity Classification of Rapeseed Using Hyperspectral Image Combined with Machine Learning. Plant phenomics (Washington, D.C.) 6 (2024)
Hui Feng, Yongqi Chen, Jingyan Song, Bingjie Lu, Caixia Shu, Jiajun Qiao, Yitao Liao, Wanneng Yang
Maturity Classification of Rapeseed Using Hyperspectral Image Combined with Machine Learning. Plant phenomics (Washington, D.C.) 6 (2024)
Wenfeng Hu, Weihao Tang, Chuang Li, Jinjing Wu, Hong Liu, Chao Wang, Xiaochuan Luo, Rongnian Tang
Handling the Challenges of Small-Scale Labeled Data and Class Imbalances in Classifying the N and K Statuses of Rubber Leaves Using Hyperspectroscopy Techniques. Plant phenomics (Washington, D.C.) 6 (2024)
Gonzalo H Villarino, Signe Dahlberg-Wright, Ling Zhang, Marianne Schaedel, Lin Wang, Karyssa Miller, Jack Bartlett, Albert Martin Dang Vu, Wolfgang Busch
PAT (Periderm Assessment Toolkit): A Quantitative and Large-Scale Screening Method for Periderm Measurements. Plant phenomics (Washington, D.C.) 6 (2024)
Jean Velluet, Antonin Della Noce, Véronique Letort
Practical Identifiability of Plant Growth Models: A Unifying Framework and Its Specification for Three Local Indices. Plant phenomics (Washington, D.C.) 6 (2024)
Sajid Ullah, Klára Panzarová, Martin Trtílek, Matej Lexa, Vojtěch Máčala, Kerstin Neumann, Thomas Altmann, Jan Hejátko, Markéta Pernisová, Evgeny Gladilin
High-Throughput Spike Detection in Greenhouse Cultivated Grain Crops with Attention Mechanisms-Based Deep Learning Models. Plant phenomics (Washington, D.C.) 6 (2024)
Liming Du, Yong Pang
Identifying Regenerated Saplings by Stratifying Forest Overstory Using Airborne LiDAR Data. Plant phenomics (Washington, D.C.) 6 (2024)
Jiaquan Lin, Jun Li, Zhe Ma, Can Li, Guangwen Huang, Huazhong Lu
A Framework for Single-Panicle Litchi Flower Counting by Regression with Multitask Learning. Plant phenomics (Washington, D.C.) 6 (2024)
Wenli Zhang, Chao Zheng, Chenhuizi Wang, Wei Guo
DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling. Plant phenomics (Washington, D.C.) 6 (2024)
Wenli Zhang, Chao Zheng, Chenhuizi Wang, Wei Guo
DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling. Plant phenomics (Washington, D.C.) 6 (2024)
Faina Khoroshevsky, Kaining Zhou, Sharon Chemweno, Yael Edan, Aharon Bar-Hillel, Ofer Hadar, Boris Rewald, Pavel Baykalov, Jhonathan E Ephrath, Naftali Lazarovitch
Automatic Root Length Estimation from Images Acquired In Situ without Segmentation. Plant phenomics (Washington, D.C.) 6 (2024)
Qiushi Yu, Nan Wang, Hui Tang, JiaXi Zhang, Rui Xu, Liantao Liu
In Situ Root Dataset Expansion Strategy Based on an Improved CycleGAN Generator. Plant phenomics (Washington, D.C.) 6 (2024)
Andrés Aguilar Ariza, Naoyuki Sotta, Toru Fujiwara, Wei Guo, Takehiro Kamiya
A Multi-Target Regression Method to Predict Element Concentrations in Tomato Leaves Using Hyperspectral Imaging. Plant phenomics (Washington, D.C.) 6 (2024)
Rui Kang, Jiaxin Huang, Xuehai Zhou, Ni Ren, Shangpeng Sun
Toward Real Scenery: A Lightweight Tomato Growth Inspection Algorithm for Leaf Disease Detection and Fruit Counting. Plant phenomics (Washington, D.C.) 6 (2024)
Hui Tang, Xue Cheng, Qiushi Yu, JiaXi Zhang, Nan Wang, Liantao Liu
Improved Transformer for Time Series Senescence Root Recognition. Plant phenomics (Washington, D.C.) 6 (2024)
Jiarui Feng, Shenghui Zhang, Zhaoyu Zhai, Hongfeng Yu, Huanliang Xu
DC 2 Net: An Asian Soybean Rust Detection Model Based on Hyperspectral Imaging and Deep Learning. Plant phenomics (Washington, D.C.) 6 (2024)
Jiarui Feng, Shenghui Zhang, Zhaoyu Zhai, Hongfeng Yu, Huanliang Xu
DC 2 Net: An Asian Soybean Rust Detection Model Based on Hyperspectral Imaging and Deep Learning. Plant phenomics (Washington, D.C.) 6 (2024)
Juan Camilo Rivera Palacio, Christian Bunn, Eric Rahn, Daisy Little-Savage, Paul Schimidt, Masahiro Ryo
Geographic-Scale Coffee Cherry Counting with Smartphones and Deep Learning. Plant phenomics (Washington, D.C.) 6 (2024)
Ismael K Mayanja, Christine H Diepenbrock, Vincent Vadez, Tong Lei, Brian N Bailey
Practical Considerations and Limitations of Using Leaf and Canopy Temperature Measurements as a Stomatal Conductance Proxy: Sensitivity across Environmental Conditions, Scale, and Sample Size. Plant phenomics (Washington, D.C.) 6 (2024)
Gonzalo H Villarino, Signe Dahlberg-Wright, Ling Zhang, Marianne Schaedel, Lin Wang, Karyssa Miller, Jack Bartlett, Albert Martin Dang Vu, Wolfgang Busch
PAT (Periderm Assessment Toolkit): A Quantitative and Large-Scale Screening Method for Periderm Measurements. Plant phenomics (Washington, D.C.) 6 (2024)
Tang Li, Motoaki Asai, Yoichiro Kato, Yuya Fukano, Wei Guo
Channel Attention GAN-Based Synthetic Weed Generation for Precise Weed Identification. Plant phenomics (Washington, D.C.) 6 (2024)
Hui Yu, Lin Weng, Songquan Wu, Jingjing He, Yilin Yuan, Jun Wang, Xiaogang Xu, Xian-Zhong Feng
Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling. Plant phenomics (Washington, D.C.) 6 (2024)
Tang Li, Motoaki Asai, Yoichiro Kato, Yuya Fukano, Wei Guo
Channel Attention GAN-Based Synthetic Weed Generation for Precise Weed Identification. Plant phenomics (Washington, D.C.) 6 (2024)
Daniel Krafft, Clifton G Scarboro, William Hsieh, Colleen J Doherty, Peter J Balint-Kurti, Michael W Kudenov
Mitigating Illumination-, Leaf-, and View-Angle Dependencies in Hyperspectral Imaging Using Polarimetry. Plant phenomics (Washington, D.C.) 6 (2024)
Hui Yu, Lin Weng, Songquan Wu, Jingjing He, Yilin Yuan, Jun Wang, Xiaogang Xu, Xian-Zhong Feng
Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling. Plant phenomics (Washington, D.C.) 6 (2024)
Yandong Wu, Weiliang Wen, Shenghao Gu, Guanmin Huang, Chuanyu Wang, Xianju Lu, Pengliang Xiao, Xinyu Guo, Linsheng Huang
Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence. Plant phenomics (Washington, D.C.) 6 (2024)
Rui Kang, Jiaxin Huang, Xuehai Zhou, Ni Ren, Shangpeng Sun
Toward Real Scenery: A Lightweight Tomato Growth Inspection Algorithm for Leaf Disease Detection and Fruit Counting. Plant phenomics (Washington, D.C.) 6 (2024)
Junqing Chen, Ya Guo, Jinglu Tan
Dynamic Analysis of Chlorophyll a Fluorescence in Response to Time-Variant Excitations during Strong Actinic Illumination and Application in Probing Plant Water Loss. Plant phenomics (Washington, D.C.) 6 (2024)
Junqing Chen, Ya Guo, Jinglu Tan
Dynamic Analysis of Chlorophyll a Fluorescence in Response to Time-Variant Excitations during Strong Actinic Illumination and Application in Probing Plant Water Loss. Plant phenomics (Washington, D.C.) 6 (2024)
Kaiyan Lu, Huiying Gong, Dengcheng Yang, Meixia Ye, Qing Fang, Xiao-Yu Zhang, Rongling Wu
Genome-Wide Network Analysis of Above- and Below-Ground Co-growth in Populus euphratica . Plant phenomics (Washington, D.C.) 6 (2024)
Ismael K Mayanja, Christine H Diepenbrock, Vincent Vadez, Tong Lei, Brian N Bailey
Practical Considerations and Limitations of Using Leaf and Canopy Temperature Measurements as a Stomatal Conductance Proxy: Sensitivity across Environmental Conditions, Scale, and Sample Size. Plant phenomics (Washington, D.C.) 6 (2024)
Andrés Aguilar Ariza, Naoyuki Sotta, Toru Fujiwara, Wei Guo, Takehiro Kamiya
A Multi-Target Regression Method to Predict Element Concentrations in Tomato Leaves Using Hyperspectral Imaging. Plant phenomics (Washington, D.C.) 6 (2024)
Hui Tang, Xue Cheng, Qiushi Yu, JiaXi Zhang, Nan Wang, Liantao Liu
Improved Transformer for Time Series Senescence Root Recognition. Plant phenomics (Washington, D.C.) 6 (2024)
Zhanhao Zhang, Jianmao Guo, Shihui Han, Shuyuan Jin, Lei Zhang
Establishing a Gross Primary Productivity Model by SIF and PRI on the Rice Canopy. Plant phenomics (Washington, D.C.) 6 (2024)