Article
Environmental Sciences
Mariam K. Al-Lami, Dane Nguyen, Nadege Oustriere, Joel G. Burken
Summary: Historical hard-rock mine activities have left almost half a million mining-impacted sites across the US, with phytostabilization being a cost-effective method. Utilizing PlantCV technology in greenhouse studies can identify potential native species for restoration of mine-impacted sites.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Plant Sciences
Yanwei Li, Xinyi Wu, Wenzhao Xu, Yudong Sun, Ying Wang, Guojing Li, Pei Xu
Summary: Phenomics is a new branch of science that quantifies plant and animal traits at the systems level, with challenges remaining in precise phenotyping of physiological traits. High-throughput physiology-based phenotyping, or physiolomics, for drought stress responses is highlighted in this review, emphasizing the need for routine physiological assays for phenotyping stress response traits in horticultural plants.
HORTICULTURAL PLANT JOURNAL
(2021)
Article
Plant Sciences
Yangyang Zhang, Wenjing Zhang, Qicong Cao, Xiaojian Zheng, Jingting Yang, Tong Xue, Wenhao Sun, Xinrui Du, Lili Wang, Jing Wang, Fengying Zhao, Fengning Xiang, Shuo Li
Summary: Soil stress, such as salinity, is a primary factor leading to reduced crop yields worldwide. However, existing tools for studying plant responses to soil stress are inadequate. In this study, a low-cost and high-throughput plant soil cultivation and phenotyping system called WinRoots was developed. This system provides uniform and controlled soil stress conditions and accurately measures the overall phenotypes of plants, including their roots.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Agronomy
Sinomar Moreira Andrade, Larissa Pereira Ribeiro Teodoro, Fabio Henrique Rojo Baio, Cid Naudi Silva Campos, Cassiano Garcia Roque, Carlos Antonio da Silva Junior, Paulo Carteri Coradi, Paulo Eduardo Teodoro
Summary: The study aimed to evaluate the nutritional status and agronomic performance of soybean genotypes grown in low and recommended base saturation conditions using high-throughput phenotyping, and found that the use of vegetation indices can efficiently assess the performance of soybean genotypes at different base saturation levels.
JOURNAL OF AGRONOMY AND CROP SCIENCE
(2021)
Review
Plant Sciences
Nathan T. Hein, Ignacio A. Ciampitti, S. V. Krishna Jagadish
Summary: The flowering and grain-filling stages of crops are sensitive to heat and drought stress, affecting yields. Remote sensing offers low-cost, high-throughput phenotyping methods to enhance crop resilience to stress.
JOURNAL OF EXPERIMENTAL BOTANY
(2021)
Article
Environmental Sciences
Lukas Roth, Christoph Barendregt, Claude-Alain Betrix, Andreas Hund, Achim Walter
Summary: This study demonstrates the importance of high-throughput field phenotyping in the context of physiological and breeding-related analyses of crops using the example of soybean production in Switzerland. The results show that analyzing the dynamics of vegetative growth from remote sensing imagery can predict yield and protein content in soybean genotypes adapted to a temperate oceanic climate.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Multidisciplinary Sciences
Diana M. Perez-Valencia, Maria Xose Rodriguez-Alvarez, Martin P. Boer, Lukas Kronenberg, Andreas Hund, Llorenc Cabrera-Bosquet, Emilie J. Millet, Fred A. van Eeuwijk
Summary: In this paper, a two-stage approach is proposed for the analysis of longitudinal HTP data. It corrects for design features and spatial trends in the first stage and focuses on the longitudinal modeling of the spatially corrected data in the second stage. A flexible hierarchical three-level P-spline growth curve model is used to capture the growth patterns. The approach is illustrated using HTP data from two different platforms.
SCIENTIFIC REPORTS
(2022)
Review
Plant Sciences
Robert D. Hall, John C. D'Auria, Antonio C. Silva Ferreira, Yves Gibon, Dariusz Kruszka, Puneet Mishra, Rick van de Zedde
Summary: High-throughput plant phenotyping approaches are rapidly developing and helping bridge the genotype-phenotype gap. Metabolites play a crucial role in defining key physiological and agronomic traits in plants. Despite challenges, novel technological innovations have the potential to fully integrate metabolomics approaches into plant phenotyping pipelines in the near future.
TRENDS IN PLANT SCIENCE
(2022)
Article
Plant Sciences
Long Chen, Matthias Daub, Hans-Georg Luigs, Marcus Jansen, Martin Strauch, Dorit Merhof
Summary: This study introduces a high-throughput system based on computer vision for quantifying beet cyst nematode infestation and measuring cyst phenotypic traits. The computer vision approach accurately counts and segments nematode cysts, allowing for the identification of phenotypic differences between nematode populations in different soils and planting periods.
FRONTIERS IN PLANT SCIENCE
(2022)
Review
Plant Sciences
Rupesh Tayade, Jungbeom Yoon, Liny Lay, Abdul Latif Khan, Youngnam Yoon, Yoonha Kim
Summary: This article provides an overview of various VIs used in agricultural research, focusing on those often employed for crop or vegetation evaluation, as they have a linear relationship to crop output and are frequently used for crop chlorophyll, health, moisture, and production predictions. In addition, the importance of VIs in crop research and precision agriculture, their utilization in high-throughput phenotyping, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features are described.
Editorial Material
Plant Sciences
Michela Janni, Roland Pieruschka
Summary: Climate change, environmental degradation, and stagnating yields pose threats to crop production and global food security. Developing sustainable and resilient agroecosystems is crucial, which requires reducing agricultural inputs and addressing decreasing land availability. Innovative solutions from the farming sector and agricultural and seed industries are essential for improving plant production in a knowledge-driven manner.
JOURNAL OF EXPERIMENTAL BOTANY
(2022)
Article
Plant Sciences
Francisco Pinto, Mainassara Zaman-Allah, Matthew Reynolds, Urs Schulthess
Summary: Recent advances in sensors, image-processing technology, and data analysis have provided opportunities for multiple scales phenotyping methods and systems, including satellite imagery. However, the low spatial resolution of satellite images has limited their deployment in breeding trials. The new generation of high-resolution satellites, such as the SkySat constellation, offers potential solutions to overcome these limitations. In this study, we used time series SkySat images to estimate NDVI and evaluated its reliability and capacity to detect seasonal changes and genotypic differences in wheat and maize breeding plots. We discuss the advantages, limitations, and perspectives of this approach for high-throughput phenotyping in breeding programs.
FRONTIERS IN PLANT SCIENCE
(2023)
Review
Plant Sciences
Patrick Langan, Villo Bernad, Jason Walsh, Joey Henchy, Mortaza Khodaeiaminjan, Eleni Mangina, Sonia Negrao
Summary: This review discusses the application of a modern phenotyping approach in improving waterlogging tolerance of temperate crop species. Waterlogging is a yield limiting stress that is expected to become a more frequent and costly issue in some regions of the world. The review highlights the difficulties of phenotyping for waterlogging tolerance due to the variability of waterlogging conditions and outlines the methods and traits used in assessing tolerance. The review also discusses the challenges and future trends in improving waterlogging tolerance.
JOURNAL OF EXPERIMENTAL BOTANY
(2022)
Review
Biochemistry & Molecular Biology
Minsu Kim, Chaewon Lee, Subin Hong, Song Lim Kim, Jeong-Ho Baek, Kyung-Hwan Kim
Summary: Understanding plant responses to drought stress is crucial, and high-throughput phenotyping (HTP) has emerged as a promising method to address the limitations in genomic and phenomic studies. HTP provides researchers with a non-destructive and accurate way to analyze large-scale phenotypic data, making it an increasingly popular tool in studying plant traits.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Computer Science, Information Systems
Hossein Estiri, Zachary H. Strasser, Shawn N. Murphy
Summary: This study aimed to develop a high-throughput phenotyping method leveraging temporal sequential patterns from electronic health records. Using temporal sequences for phenotyping resulted in superior classification performance compared to standard representations, and transitive sequences offered more accurate phenotype characterization.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
(2021)
Article
Agronomy
Sumanta Das, Jack Christopher, Armando Apan, Malini Roy Choudhury, Scott Chapman, Neal W. Menzies, Yash P. Dang
Summary: This study proposed a thermal remote sensing and machine learning-based approach to predict biomass and grain yields of wheat genotypes grown in sodic soil with varying water stress. The findings showed that a classification and regression tree accurately predicted biomass and grain yields, indicating that highly sodic soil constraints have a greater impact on wheat yield compared to moderately sodic soil.
AGRICULTURAL AND FOREST METEOROLOGY
(2021)
Article
Agronomy
Etienne David, Mario Serouart, Daniel Smith, Simon Madec, Kaaviya Velumani, Shouyang Liu, Xu Wang, Francisco Pinto, Shahameh Shafiee, Izzat S. A. Tahir, Hisashi Tsujimoto, Shuhei Nasuda, Bangyou Zheng, Norbert Kirchgessner, Helge Aasen, Andreas Hund, Pouria Sadhegi-Tehran, Koichi Nagasawa, Goro Ishikawa, Sebastien Dandrifosse, Alexis Carlier, Benjamin Dumont, Benoit Mercatoris, Byron Evers, Ken Kuroki, Haozhou Wang, Masanori Ishii, Minhajul A. Badhon, Curtis Pozniak, David Shaner LeBauer, Morten Lillemo, Jesse Poland, Scott Chapman, Benoit de Solan, Frederic Baret, Ian Stavness, Wei Guo
Summary: The Global Wheat Head Detection dataset created in 2020 attracted attention from computer vision and agricultural science communities. The 2021 version of the dataset is larger, more diverse, and less noisy than the previous version after improvements and updates.
Article
Agronomy
Jonathan J. Ojeda, Graeme Hammer, Kai-Wei Yang, Mitchell R. Tuinstra, Peter DeVoil, Greg McLean, Isaiah Huber, Jeffrey J. Volenec, Sylvie M. Brouder, Sotirios Archontoulis, Scott C. Chapman
Summary: Regional-scale estimations of sorghum biomass production can identify the optimal combinations of genotype, environment, and management for bioenergy generation. This study determined the contributions of genotype, environment, and management to sorghum biomass variability in the United States. The results showed that genotype had the largest impact on biomass variability, followed by environment and management. The findings emphasize the importance of considering these factors in future biomass projections of energy sorghum genotypes.
GLOBAL CHANGE BIOLOGY BIOENERGY
(2022)
Article
Plant Sciences
Pengcheng Hu, Scott C. Chapman, Sivakumar Sukumaran, Matthew Reynolds, Bangyou Zheng
Summary: Fine-tuning the duration of the late reproductive phase can increase the yield potential of wheat by increasing grain number. However, an excessively long late reproductive phase can reduce the yield potential.
JOURNAL OF EXPERIMENTAL BOTANY
(2022)
Article
Agronomy
Xiaoyu Zhi, Sean Reynolds Massey-Reed, Alex Wu, Andries Potgieter, Andrew Borrell, Colleen Hunt, David Jordan, Yan Zhao, Scott Chapman, Graeme Hammer, Barbara George-Jaeggli
Summary: This study used sorghum as a model to predict photosynthetic capacity traits using hyperspectral sensing and genetic analysis. The researchers identified candidate genes associated with these traits and demonstrated the potential of this method for screening large germplasm collections for enhanced photosynthesis.
Article
Plant Sciences
Qiaomin Chen, Bangyou Zheng, Tong Chen, Scott C. Chapman
Summary: A conceptual framework integrating crop growth model and radiative transfer model was proposed to introduce biological constraints in a synthetic training dataset for improving estimation accuracy of crop traits. The results demonstrated the potential advantages of adding biological constraints and utilizing deep learning for simultaneously predicting multiple crop traits from synthetic datasets. The predictive models were further validated on real unmanned aerial vehicle-based multispectral images, confirming the effectiveness of the proposed framework.
JOURNAL OF EXPERIMENTAL BOTANY
(2022)
Article
Agronomy
Qiaomin Chen, Bangyou Zheng, Karine Chenu, Pengcheng Hu, Scott C. Chapman
Summary: This study developed a hybrid method to estimate leaf area index (LAI) from UAV-based multispectral images using random forest regression (RFR) models. The results showed that the RFR models accurately predicted LAI from canopy reflectance, although there was some overestimation for LAI<2 which could be addressed by background correction. The method also captured the spatiotemporal variation of observed LAI and identified variations for different growing stages and treatments.
Article
Agronomy
Andries B. Potgieter, Andrew Schepen, Jason Brider, Graeme L. Hammer
Summary: Foresight of crop yield is crucial for managing climate risks and uncertainties in the Australian agricultural industry. This study compares the skill of a wheat yield forecasting system using a statistical ENSO-analogue climate forecasting system and a dynamic GCM-derived climate forecasting system. The results show that the GCM-based approach has improved skill and provides reliable wheat yield forecasts, particularly in the early months of the season. The shift in forecast yield distributions varies by location and time, with the GCM-derived forecasts showing more widespread and earlier shifts. Overall, the GCM-based climate/crop forecasting system demonstrates significant improvement in lead time and offers potential for enhanced relevance and utility in commodity forecasting frameworks.
AGRICULTURAL AND FOREST METEOROLOGY
(2022)
Article
Multidisciplinary Sciences
Simon Madec, Kamran Irfan, Kaaviya Velumani, Frederic Baret, Etienne David, Gaetan Daubige, Lucas Bernigaud Samatan, Mario Serouart, Daniel Smith, Chrisbin James, Fernando Camacho, Wei Guo, Benoit De Solan, Scott C. Chapman, Marie Weiss
Summary: Applying deep learning to images of cropping systems offers new knowledge and insights in research and commercial applications. Semantic segmentation or pixel-wise classification of ground level RGB images into vegetation and background is crucial for estimating canopy traits. Current CNN-based methodologies trained on controlled or indoor datasets cannot generalize to real-world images, requiring fine-tuning with new labeled datasets. The creation of the VegAnn dataset, consisting of 3775 multicrop RGB images acquired under diverse illumination conditions, aims to improve segmentation algorithm performance and facilitate benchmarking in large-scale crop vegetation segmentation research.
Review
Agronomy
Andries B. Potgieter, Yan Zhao, Pablo J. Zarco-Tejada, Karine Chenu, Yifan Zhang, Kenton Porker, Ben Biddulph, Yash P. Dang, Tim Neale, Fred Roosta, Scott Chapman
Summary: The downside risk of crop production affects the entire supply chain of the agricultural industry nationally and globally, impacting food security and livelihoods worldwide. The advancement in remote sensing platforms and machine learning technologies in recent years has helped in resolving complex ecophysiological interactions previously deemed too difficult to solve.
Article
Agronomy
Brian Collins, Scott Chapman, Graeme Hammer, Karine Chenu
Summary: The study found that limiting transpiration at high evaporative demands can increase crop productivity, mainly through changes in water use pattern, alleviation of water deficit during grain filling period and higher harvest index. The greatest productivity gains were observed in the northeast region of the Australian wheatbelt, where the LTR trait effectively conserved water for critical stages, and this effect was enhanced under future climate scenarios.
Article
Agronomy
Kai-Wei Yang, Scott Chapman, Neal Carpenter, Graeme Hammer, Greg McLean, Bangyou Zheng, Yuhao Chen, Edward Delp, Ali Masjedi, Melba Crawford, David Ebert, Ayman Habib, Addie Thompson, Clifford Weil, Mitchell R. Tuinstra
Summary: The study utilized crop models and remote-sensing data to predict phenotype and yield variation in different sorghum hybrids, with results indicating that photoperiod-sensitive hybrids perform better in suitable environments and parameterized models perform well in above-ground biomass simulations across different time and locations.
Review
Agronomy
M. Cooper, O. Powell, K. P. Voss-Fels, C. D. Messina, C. Gho, D. W. Podlich, F. Technow, S. C. Chapman, C. A. Beveridge, D. Ortiz-Barrientos, G. L. Hammer
Summary: Plant-breeding programs aim to systematically change the genetic makeup of plants over multiple cycles to improve trait performance for target environments. Selection within a structured reference population of genotypes is the primary mechanism for genetic changes, informing breeding strategies through the breeder's equation and quantitative genetic theory. Through this process, cultivated crop varieties are improved for use in agriculture by linking gene effects to trait phenotypes. The hierarchical structure of crop models, combined with the infinitesimal model, is considered for optimizing selection in breeding programs.