Journal articles: 'Crop Phenotyping'

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Wang, Ya-Hong, and Wen-Hao Su. "Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review." Agronomy 12, no. 11 (October 27, 2022): 2659. http://dx.doi.org/10.3390/agronomy12112659.

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Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture. Therefore, this paper provides a comprehensive review of CNNs in computer vision for grain crop phenotyping. It is meaningful to provide a review as a roadmap for future research in such a thriving research area. The CNN models (e.g., VGG, YOLO, and Faster R-CNN) used CV tasks including image classification, object detection, semantic segmentation, and instance segmentation, and the main results of recent studies on crop phenotype detection are discussed and summarized. Additionally, the challenges and future trends of the phenotyping techniques in grain crops are presented.

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Nguyen, Giao N., and Sally L. Norton. "Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm." Plants 9, no. 7 (June 29, 2020): 817. http://dx.doi.org/10.3390/plants9070817.

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Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.

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Yuan, Huali, Yiming Liu, Minghan Song, Yan Zhu, Weixing Cao, Xiaoping Jiang, and Jun Ni. "Design of the Mechanical Structure of a Field-Based Crop Phenotyping Platform and Tests of the Platform." Agronomy 12, no. 9 (September 11, 2022): 2162. http://dx.doi.org/10.3390/agronomy12092162.

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The field mobile platform is an important tool for high-throughput phenotype monitoring. To overcome problems in existing field-based crop phenotyping platforms, including limited application scope and low stability, a rolling adjustment method for the wheel tread was proposed. A self-propelled three-wheeled field-based crop phenotyping platform with variable wheel tread and height above ground was developed, which enabled phenotypic information of different dry crops in different development stages. A three-dimensional model of the platform was established using Pro/E; ANSYS and ADAMS were used for static and dynamic performance. Results show that when running on flat ground, the platform has a vibration acceleration lower than 0.5 m/s2. When climbing over an obstacle with a height of 100 mm, the vibration amplitude of the platform is 88.7 mm. The climbing angle is not less than 15°. Field tests imply that the normalized difference vegetation index (NDVI) and the ratio vegetation index (RVI) of a canopy measured using crop growth sensors mounted on the above platform show favorable linear correlations with those measured using a handheld analytical spectral device (ASD). Their R2 values are 0.6052 and 0.6093 and root-mean-square errors (RMSEs) are 0.0487 and 0.1521, respectively. The field-based crop phenotyping platform provides a carrier for high-throughput acquisition of crop phenotypic information.

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Anchekov, M. I. "High throughput crop phenotyping systems." News of the Kabardin-Balkar Scientific Center of RAS 5, no. 109 (2022): 19–24. http://dx.doi.org/10.35330/1991-6639-2022-5-109-19-24.

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Jin, Xiuliang, Wanneng Yang, John H. Doonan, and Clement Atzberger. "Crop phenotyping studies with application to crop monitoring." Crop Journal 10, no. 5 (October 2022): 1221–23. http://dx.doi.org/10.1016/j.cj.2022.09.001.

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Stanschewski, Clara S., Elodie Rey, Gabriele Fiene, Evan B. Craine, Gordon Wellman, Vanessa J. Melino, Dilan S. R. Patiranage, et al. "Quinoa Phenotyping Methodologies: An International Consensus." Plants 10, no. 9 (August 24, 2021): 1759. http://dx.doi.org/10.3390/plants10091759.

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Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

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Nobuhara, Hajime. "Aerial Imaging for Field Crop Phenotyping." Journal of the Robotics Society of Japan 34, no. 2 (2016): 123–26. http://dx.doi.org/10.7210/jrsj.34.123.

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Xu, Rui, and Changying Li. "A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots." Plant Phenomics 2022 (June 17, 2022): 1–20. http://dx.doi.org/10.34133/2022/9760269.

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Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient. The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure novel and fine-scale phenotypic traits that were previously unattainable by humans. The main goal of this paper is to review the state-of-the-art of high-throughput field phenotyping systems with a focus on autonomous ground robotic systems. This paper first provides a brief review of nonautonomous ground phenotyping systems including tractors, manually pushed or motorized carts, gantries, and cable-driven systems. Then, a detailed review of autonomous ground phenotyping robots is provided with regard to the robot’s main components, including mobile platforms, sensors, manipulators, computing units, and software. It also reviews the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robots in measuring plant phenotypic traits and collecting phenotyping datasets. At the end of the review, this paper discusses current major challenges and future research directions.

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Watt, Michelle, Fabio Fiorani, Björn Usadel, Uwe Rascher, Onno Muller, and Ulrich Schurr. "Phenotyping: New Windows into the Plant for Breeders." Annual Review of Plant Biology 71, no. 1 (April 29, 2020): 689–712. http://dx.doi.org/10.1146/annurev-arplant-042916-041124.

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Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.

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Ilakiya, T., E. Parameswari, V. Davamani, Dumpala Swetha, and E. Prakash. "High-throughput crop phenotyping in vegetable crops." Pharma Innovation 9, no. 8 (August 1, 2020): 184–91. http://dx.doi.org/10.22271/tpi.2020.v9.i8c.5035.

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Ghanem, Michel Edmond, Hélène Marrou, and Thomas R. Sinclair. "Physiological phenotyping of plants for crop improvement." Trends in Plant Science 20, no. 3 (March 2015): 139–44. http://dx.doi.org/10.1016/j.tplants.2014.11.006.

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Botyanszka, Lenka. "A Review of Imaging and Sensing Technologies for Field Phenotyping." Acta Horticulturae et Regiotecturae 24, s1 (May 1, 2021): 58–69. http://dx.doi.org/10.2478/ahr-2021-0011.

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Abstract Over the past few decades, food production has been sufficient. However, climate change has already affected crop yields around the world. With climate change and population growth, threats to future food production come. Among the solutions to this crisis, breeding is deemed one of the most effective ways. However, traditional phenotyping in breeding is time-consuming as it requires thousands and thousands of individuals. Mechanisms and structures of stress tolerance have a great variability. Today, bigger emphasis is placed on the selection of crops based on genotype information and this still requires phenotypic data. Their use is limited by insufficient phenotypic data, including the information on stress photosynthetic responses. The latest research seeks to bring rapid, non-destructive imaging and sensing technology to agriculture, in order to greatly accelerate the in-field measurements of phenotypes and increase the phenotypic data. This paper presents a review of the imaging and sensing technologies for the field phenotyping to describe its development in the last few years.

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Khak Pour, Majid, Reza Fotouhi, Pierre Hucl, and Qianwei Zhang. "Development of a Mobile Platform for Field-Based High-Throughput Wheat Phenotyping." Remote Sensing 13, no. 8 (April 17, 2021): 1560. http://dx.doi.org/10.3390/rs13081560.

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Designing and implementing an affordable High-Throughput Phenotyping Platform (HTPP) for monitoring crops’ features in different stages of their growth can provide valuable information for crop-breeders to study possible correlation between genotypes and phenotypes. Conducting automatic field measurements can improve crop productions. In this research, we have focused on development of a mechatronic system, hardware and software, for a mobile, field-based HTPP for autonomous crop monitoring for wheat field. The system can measure canopy’s height, temperature, and vegetation indices and is able to take high quality photos of crops. The system includes. developed software for data and image acquisition. The main contribution of this study is autonomous, reliable, and fast data collection for wheat and similar crops.

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Banerjee, Bikram Pratap, German Spangenberg, and Surya Kant. "CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements." Biosensors 12, no. 1 (December 29, 2021): 16. http://dx.doi.org/10.3390/bios12010016.

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The phenotypic characterization of crop genotypes is an essential, yet challenging, aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agricultural research due to the diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

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Jin, Xiuliang, Zhenhai Li, and Clement Atzberger. "Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”." Remote Sensing 12, no. 6 (March 13, 2020): 940. http://dx.doi.org/10.3390/rs12060940.

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High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.

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Wasaya, Allah, Xiying Zhang, Qin Fang, and Zongzheng Yan. "Root Phenotyping for Drought Tolerance: A Review." Agronomy 8, no. 11 (October 31, 2018): 241. http://dx.doi.org/10.3390/agronomy8110241.

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Plant roots play a significant role in plant growth by exploiting soil resources via the uptake of water and nutrients. Root traits such as fine root diameter, specific root length, specific root area, root angle, and root length density are considered useful traits for improving plant productivity under drought conditions. Therefore, understanding interactions between roots and their surrounding soil environment is important, which can be improved through root phenotyping. With the advancement in technologies, many tools have been developed for root phenotyping. Canopy temperature depression (CTD) has been considered a good technique for field phenotyping of crops under drought and is used to estimate crop yield as well as root traits in relation to drought tolerance. Both laboratory and field-based methods for phenotyping root traits have been developed including soil sampling, mini-rhizotron, rhizotrons, thermography and non-soil techniques. Recently, a non-invasive approach of X-ray computed tomography (CT) has provided a break-through to study the root architecture in three dimensions (3-D). This review summarizes methods for root phenotyping. On the basis of this review, it can be concluded that root traits are useful characters to be included in future breeding programs and for selecting better cultivars to increase crop yield under water-limited environments.

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Maier, Chelsea R., Zhong-Hua Chen, Christopher I. Cazzonelli, David T. Tissue, and Oula Ghannoum. "Precise Phenotyping for Improved Crop Quality and Management in Protected Cropping: A Review." Crops 2, no. 4 (September 22, 2022): 336–50. http://dx.doi.org/10.3390/crops2040024.

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Protected cropping produces more food per land area than field-grown crops. Protected cropping includes low-tech polytunnels utilizing protective coverings, medium-tech facilities with some environmental control, and high-tech facilities such as fully automated glasshouses and indoor vertical farms. High crop productivity and quality are maintained by using environmental control systems and advanced precision phenotyping sensor technologies that were first developed for broadacre agricultural and can now be utilized for protected-cropping applications. This paper reviews the state of the global protected-cropping industry and current precision phenotyping methodology and technology that is used or can be used to advance crop productivity and quality in a protected growth environment. This review assesses various sensor technologies that can monitor and maintain microclimate parameters, as well as be used to assess plant productivity and produce quality. The adoption of precision phenotyping technologies is required for sustaining future food security and enhancing nutritional quality.

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Nguyen, Giao N., and Surya Kant. "Improving nitrogen use efficiency in plants: effective phenotyping in conjunction with agronomic and genetic approaches." Functional Plant Biology 45, no. 6 (2018): 606. http://dx.doi.org/10.1071/fp17266.

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For global sustainable food production and environmental benefits, there is an urgent need to improve N use efficiency (NUE) in crop plants. Excessive and inefficient use of N fertiliser results in increased crop production costs and environmental pollution. Therefore, cost-effective strategies such as proper management of the timing and quantity of N fertiliser application, and breeding for better varieties are needed to improve NUE in crops. However, for these efforts to be feasible, high-throughput and reliable phenotyping techniques would be very useful for monitoring N status in planta, as well as to facilitate faster decisions during breeding and selection processes. This review provides an insight into contemporary approaches to phenotyping NUE-related traits and associated challenges. We discuss recent and advanced, sensor- and image-based phenotyping techniques that use a variety of equipment, tools and platforms. The review also elaborates on how high-throughput phenotyping will accelerate efforts for screening large populations of diverse genotypes in controlled environment and field conditions to identify novel genotypes with improved NUE.

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LIU, Zhe, Fan ZHANG, Qin MA, Dong AN, Lin LI, Xiaodong ZHANG, Dehai ZHU, and Shaoming LI. "Advances in crop phenotyping and multi-environment trials." Frontiers of Agricultural Science and Engineering 2, no. 1 (2015): 28. http://dx.doi.org/10.15302/j-fase-2015051.

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Guo, Qinghua, Fangfang Wu, Shuxin Pang, Xiaoqian Zhao, Linhai Chen, Jin Liu, Baolin Xue, et al. "Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping." Science China Life Sciences 61, no. 3 (December 6, 2017): 328–39. http://dx.doi.org/10.1007/s11427-017-9056-0.

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Wang, Yinghua, Songtao Hu, He Ren, Wanneng Yang, and Ruifang Zhai. "3DPhenoMVS: A Low-Cost 3D Tomato Phenotyping Pipeline Using 3D Reconstruction Point Cloud Based on Multiview Images." Agronomy 12, no. 8 (August 8, 2022): 1865. http://dx.doi.org/10.3390/agronomy12081865.

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Manual phenotyping of tomato plants is time consuming and labor intensive. Due to the lack of low-cost and open-access 3D phenotyping tools, the dynamic 3D growth of tomato plants during all growth stages has not been fully explored. In this study, based on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we proposed a 3D phenotyping pipeline, 3DPhenoMVS, to calculate 17 phenotypic traits of tomato plants covering the whole life cycle. Among all the phenotypic traits, six of them were used for accuracy evaluation because the true values can be generated by manual measurements, and the results showed that the R2 values between the phenotypic traits and the manual ones ranged from 0.72 to 0.97. In addition, to investigate the environmental influence on tomato plant growth and yield in the greenhouse, eight tomato plants were chosen and phenotyped during seven growth stages according to different light intensities, temperatures, and humidities. The results showed that stronger light intensity and moderate temperature and humidity contribute to a higher biomass and higher yield. In conclusion, we developed a low-cost and open-access 3D phenotyping pipeline for tomato and other plants, and the generalization test was also complemented on other six species, which demonstrated that the proposed pipeline will benefit plant breeding, cultivation research, and functional genomics in the future.

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Hein, Nathan T., Ignacio A. Ciampitti, and S. V. Krishna Jagadish. "Bottlenecks and opportunities in field-based high-throughput phenotyping for heat and drought stress." Journal of Experimental Botany 72, no. 14 (January 20, 2021): 5102–16. http://dx.doi.org/10.1093/jxb/erab021.

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Abstract Flowering and grain-filling stages are highly sensitive to heat and drought stress exposure, leading to significant loss in crop yields. Therefore, phenotyping to enhance resilience to these abiotic stresses is critical for sustaining genetic gains in crop improvement programs. However, traditional methods for screening traits related to these stresses are slow, laborious, and often expensive. Remote sensing provides opportunities to introduce low-cost, less biased, high-throughput phenotyping methods to capture large genetic diversity to facilitate enhancement of stress resilience in crops. This review focuses on four key physiological traits and processes that are critical in understanding crop responses to drought and heat stress during reproductive and grain-filling periods. Specifically, these traits include: (i) time of day of flowering, to escape these stresses during flowering; (ii) optimizing photosynthetic efficiency; (iii) storage and translocation of water-soluble carbohydrates; and (iv) yield and yield components to provide in-season yield estimates. Moreover, we provide an overview of current advances in remote sensing in capturing these traits, and discuss the limitations with existing technology as well as future direction of research to develop high-throughput phenotyping approaches. In the future, phenotyping these complex traits will require sensor advancement, high-quality imagery combined with machine learning methods, and efforts in transdisciplinary science to foster integration across disciplines.

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Lasky, Jesse R., Hari D. Upadhyaya, Punna Ramu, Santosh Deshpande, C. Tom Hash, Jason Bonnette, Thomas E. Juenger, et al. "Genome-environment associations in sorghum landraces predict adaptive traits." Science Advances 1, no. 6 (July 2015): e1400218. http://dx.doi.org/10.1126/sciadv.1400218.

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Improving environmental adaptation in crops is essential for food security under global change, but phenotyping adaptive traits remains a major bottleneck. If associations between single-nucleotide polymorphism (SNP) alleles and environment of origin in crop landraces reflect adaptation, then these could be used to predict phenotypic variation for adaptive traits. We tested this proposition in the global food cropSorghum bicolor, characterizing 1943 georeferenced landraces at 404,627 SNPs and quantifying allelic associations with bioclimatic and soil gradients. Environment explained a substantial portion of SNP variation, independent of geographical distance, and genic SNPs were enriched for environmental associations. Further, environment-associated SNPs predicted genotype-by-environment interactions under experimental drought stress and aluminum toxicity. Our results suggest that genomic signatures of environmental adaptation may be useful for crop improvement, enhancing germplasm identification and marker-assisted selection. Together, genome-environment associations and phenotypic analyses may reveal the basis of environmental adaptation.

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Petsoulas, Christos, Eleftherios Evangelou, Alexandros Tsitouras, Vassilis Aschonitis, Anastasia Kargiotidou, Ebrahim Khah, Ourania I. Pavli, and Dimitrios N. Vlachostergios. "Spectral Reflectance Indices as a High Throughput Selection Tool in a Sesame Breeding Scheme." Remote Sensing 14, no. 11 (May 31, 2022): 2629. http://dx.doi.org/10.3390/rs14112629.

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On-farm genotype screening is at the core of every breeding scheme, but it comes with a high cost and often high degree of uncertainty. Phenomics is a new approach by plant breeders, who use optical sensors for accurate germplasm phenotyping, selection and enhancement of the genetic gain. The objectives of this study were to: (1) develop a high-throughput phenotyping workflow to estimate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge index (NDRE) at the plot-level through an active crop canopy sensor; (2) test the ability of spectral reflectance indices (SRIs) to distinguish between sesame genotypes throughout the crop growth period; and (3) identify specific stages in the sesame growth cycle that contribute to phenotyping accuracy and functionality and evaluate the efficiency of SRIs as a selection tool. A diversity panel of 24 sesame genotypes was grown at normal and late planting dates in 2020 and 2021. To determine the SRIs the Crop Circle ACS-430 active crop canopy sensor was used from the beginning of the sesame reproductive stage to the end of the ripening stage. NDVI and NDRE reached about the same high accuracy in genotype phenotyping, even under dense biomass conditions where “saturation” problems were expected. NDVI produced higher broad-sense heritability (max 0.928) and NDRE higher phenotypic and genotypic correlation with the yield (max 0.593 and 0.748, respectively). NDRE had the highest relative efficiency (61%) as an indirect selection index to yield direct selection. Both SRIs had optimal results when the monitoring took place at the end of the reproductive stage and the beginning of the ripening stage. Thus, an active canopy sensor as this study demonstrated can assist breeders to differentiate and classify sesame genotypes.

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Susko, Alexander Q., Fletcher Gilbertson, D. Jo Heuschele, Kevin Smith, and Peter Marchetto. "An automatable, field camera track system for phenotyping crop lodging and crop movement." HardwareX 4 (October 2018): e00029. http://dx.doi.org/10.1016/j.ohx.2018.e00029.

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Chenu, Karine, Andrew Fletcher, Behnam Ababaei, Jack Christopher, Alison Kelly, Lee Hickey, Erik Van Oosterom, and Graeme Hammer. "Integrating Crop Modelling, Physiology, Genetics and Breeding to Aid Crop Improvement for Changing Environments in the Australian Wheatbelt." Proceedings 36, no. 1 (December 24, 2019): 4. http://dx.doi.org/10.3390/proceedings2019036004.

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Despite recent progress in genetics, genomics, and phenotyping, trait selection is limited by our ability to predict genotype x environment interactions, and to identify impactful traits for target environments. [...]

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Ninomiya, Seishi. "High-throughput field crop phenotyping: current status and challenges." Breeding Science 72, no. 1 (2022): 3–18. http://dx.doi.org/10.1270/jsbbs.21069.

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Bucksch, A., J. Burridge, L. M. York, A. Das, E. Nord, J. S. Weitz, and J. P. Lynch. "Image-Based High-Throughput Field Phenotyping of Crop Roots." PLANT PHYSIOLOGY 166, no. 2 (September 3, 2014): 470–86. http://dx.doi.org/10.1104/pp.114.243519.

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Araus, José Luis, and Jill E. Cairns. "Field high-throughput phenotyping: the new crop breeding frontier." Trends in Plant Science 19, no. 1 (January 2014): 52–61. http://dx.doi.org/10.1016/j.tplants.2013.09.008.

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Tracy, Saoirse R., Kerstin A. Nagel, Johannes A. Postma, Heike Fassbender, Anton Wasson, and Michelle Watt. "Crop Improvement from Phenotyping Roots: Highlights Reveal Expanding Opportunities." Trends in Plant Science 25, no. 1 (January 2020): 105–18. http://dx.doi.org/10.1016/j.tplants.2019.10.015.

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Zhang, Chongyuan, Honghong Gao, Jianfeng Zhou, Asaph Cousins, Michael O. Pumphrey, and Sindhuja Sankaran. "3D Robotic System Development for High-throughput Crop Phenotyping." IFAC-PapersOnLine 49, no. 16 (2016): 242–47. http://dx.doi.org/10.1016/j.ifacol.2016.10.045.

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Gioia, Tania, Anna Galinski, Henning Lenz, Carmen Müller, Jonas Lentz, Kathrin Heinz, Christoph Briese, et al. "GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system based on germination paper to quantify crop phenotypic diversity and plasticity of root traits under varying nutrient supply." Functional Plant Biology 44, no. 1 (2017): 76. http://dx.doi.org/10.1071/fp16128.

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New techniques and approaches have been developed for root phenotyping recently; however, rapid and repeatable non-invasive root phenotyping remains challenging. Here, we present GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system (4 plants min–1) based on flat germination paper. GrowScreen-PaGe allows the acquisition of time series of the developing root systems of 500 plants, thereby enabling to quantify short-term variations in root system. The choice of germination paper was found to be crucial and paper ☓ root interaction should be considered when comparing data from different studies on germination paper. The system is suitable for phenotyping dicot and monocot plant species. The potential of the system for high-throughput phenotyping was shown by investigating phenotypic diversity of root traits in a collection of 180 rapeseed accessions and of 52 barley genotypes grown under control and nutrient-starved conditions. Most traits showed a large variation linked to both genotype and treatment. In general, root length traits contributed more than shape and branching related traits in separating the genotypes. Overall, results showed that GrowScreen-PaGe will be a powerful resource to investigate root systems and root plasticity of large sets of plants and to explore the molecular and genetic root traits of various species including for crop improvement programs.

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Gibbs, Jonathon A., Michael Pound, Andrew P. French, Darren M. Wells, Erik Murchie, and Tony Pridmore. "Approaches to three-dimensional reconstruction of plant shoot topology and geometry." Functional Plant Biology 44, no. 1 (2017): 62. http://dx.doi.org/10.1071/fp16167.

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There are currently 805 million people classified as chronically undernourished, and yet the World’s population is still increasing. At the same time, global warming is causing more frequent and severe flooding and drought, thus destroying crops and reducing the amount of land available for agriculture. Recent studies show that without crop climate adaption, crop productivity will deteriorate. With access to 3D models of real plants it is possible to acquire detailed morphological and gross developmental data that can be used to study their ecophysiology, leading to an increase in crop yield and stability across hostile and changing environments. Here we review approaches to the reconstruction of 3D models of plant shoots from image data, consider current applications in plant and crop science, and identify remaining challenges. We conclude that although phenotyping is receiving an increasing amount of attention – particularly from computer vision researchers – and numerous vision approaches have been proposed, it still remains a highly interactive process. An automated system capable of producing 3D models of plants would significantly aid phenotyping practice, increasing accuracy and repeatability of measurements.

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Cai, Shuangze, Wenbo Gou, Weiliang Wen, Xianju Lu, Jiangchuan Fan, and Xinyu Guo. "Design and Development of a Low-Cost UGV 3D Phenotyping Platform with Integrated LiDAR and Electric Slide Rail." Plants 12, no. 3 (January 20, 2023): 483. http://dx.doi.org/10.3390/plants12030483.

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Unmanned ground vehicles (UGV) have attracted much attention in crop phenotype monitoring due to their lightweight and flexibility. This paper describes a new UGV equipped with an electric slide rail and point cloud high-throughput acquisition and phenotype extraction system. The designed UGV is equipped with an autopilot system, a small electric slide rail, and Light Detection and Ranging (LiDAR) to achieve high-throughput, high-precision automatic crop point cloud acquisition and map building. The phenotype analysis system realized single plant segmentation and pipeline extraction of plant height and maximum crown width of the crop point cloud using the Random sampling consistency (RANSAC), Euclidean clustering, and k-means clustering algorithm. This phenotyping system was used to collect point cloud data and extract plant height and maximum crown width for 54 greenhouse-potted lettuce plants. The results showed that the correlation coefficient (R2) between the collected data and manual measurements were 0.97996 and 0.90975, respectively, while the root mean square error (RMSE) was 1.51 cm and 4.99 cm, respectively. At less than a tenth of the cost of the PlantEye F500, UGV achieves phenotypic data acquisition with less error and detects morphological trait differences between lettuce types. Thus, it could be suitable for actual 3D phenotypic measurements of greenhouse crops.

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Zenda, Tinashe, Songtao Liu, Anyi Dong, and Huijun Duan. "Advances in Cereal Crop Genomics for Resilience under Climate Change." Life 11, no. 6 (May 29, 2021): 502. http://dx.doi.org/10.3390/life11060502.

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Adapting to climate change, providing sufficient human food and nutritional needs, and securing sufficient energy supplies will call for a radical transformation from the current conventional adaptation approaches to more broad-based and transformative alternatives. This entails diversifying the agricultural system and boosting productivity of major cereal crops through development of climate-resilient cultivars that can sustainably maintain higher yields under climate change conditions, expanding our focus to crop wild relatives, and better exploitation of underutilized crop species. This is facilitated by the recent developments in plant genomics, such as advances in genome sequencing, assembly, and annotation, as well as gene editing technologies, which have increased the availability of high-quality reference genomes for various model and non-model plant species. This has necessitated genomics-assisted breeding of crops, including underutilized species, consequently broadening genetic variation of the available germplasm; improving the discovery of novel alleles controlling important agronomic traits; and enhancing creation of new crop cultivars with improved tolerance to biotic and abiotic stresses and superior nutritive quality. Here, therefore, we summarize these recent developments in plant genomics and their application, with particular reference to cereal crops (including underutilized species). Particularly, we discuss genome sequencing approaches, quantitative trait loci (QTL) mapping and genome-wide association (GWAS) studies, directed mutagenesis, plant non-coding RNAs, precise gene editing technologies such as CRISPR-Cas9, and complementation of crop genotyping by crop phenotyping. We then conclude by providing an outlook that, as we step into the future, high-throughput phenotyping, pan-genomics, transposable elements analysis, and machine learning hold much promise for crop improvements related to climate resilience and nutritional superiority.

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Iqbal, Jawad, Rui Xu, Shangpeng Sun, and Changying Li. "Simulation of an Autonomous Mobile Robot for LiDAR-Based In-Field Phenotyping and Navigation." Robotics 9, no. 2 (June 21, 2020): 46. http://dx.doi.org/10.3390/robotics9020046.

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The agriculture industry is in need of substantially increasing crop yield to meet growing global demand. Selective breeding programs can accelerate crop improvement but collecting phenotyping data is time- and labor-intensive because of the size of the research fields and the frequency of the work required. Automation could be a promising tool to address this phenotyping bottleneck. This paper presents a Robotic Operating System (ROS)-based mobile field robot that simultaneously navigates through occluded crop rows and performs various phenotyping tasks, such as measuring plant volume and canopy height using a 2D LiDAR in a nodding configuration. The efficacy of the proposed 2D LiDAR configuration for phenotyping is assessed in a high-fidelity simulated agricultural environment in the Gazebo simulator with an ROS-based control framework and compared with standard LiDAR configurations used in agriculture. Using the proposed nodding LiDAR configuration, a strategy for navigation through occluded crop rows is presented. The proposed LiDAR configuration achieved an estimation error of 6.6% and 4% for plot volume and canopy height, respectively, which was comparable to the commonly used LiDAR configurations. The hybrid strategy with GPS waypoint following and LiDAR-based navigation was used to navigate the robot through an agricultural crop field successfully with an root mean squared error of 0.0778 m which was 0.2% of the total traveled distance. The presented robot simulation framework in ROS and optimized LiDAR configuration helped to expedite the development of the agricultural robots, which ultimately will aid in overcoming the phenotyping bottleneck.

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Virlet, Nicolas, Kasra Sabermanesh, Pouria Sadeghi-Tehran, and Malcolm J. Hawkesford. "Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring." Functional Plant Biology 44, no. 1 (2017): 143. http://dx.doi.org/10.1071/fp16163.

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Current approaches to field phenotyping are laborious or permit the use of only a few sensors at a time. In an effort to overcome this, a fully automated robotic field phenotyping platform with a dedicated sensor array that may be accurately positioned in three dimensions and mounted on fixed rails has been established, to facilitate continual and high-throughput monitoring of crop performance. Employed sensors comprise of high-resolution visible, chlorophyll fluorescence and thermal infrared cameras, two hyperspectral imagers and dual 3D laser scanners. The sensor array facilitates specific growth measurements and identification of key growth stages with dense temporal and spectral resolution. Together, this platform produces a detailed description of canopy development across the crops entire lifecycle, with a high-degree of accuracy and reproducibility.

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Munaiz, Eduardo D., Susana Martínez, Arun Kumar, Marlon Caicedo, and Bernardo Ordás. "The Senescence (Stay-Green)—An Important Trait to Exploit Crop Residuals for Bioenergy." Energies 13, no. 4 (February 11, 2020): 790. http://dx.doi.org/10.3390/en13040790.

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In this review, we present a comprehensive revisit of past research and advances developed on the stay-green (SG) paradigm. The study aims to provide an application-focused review of the SG phenotypes as crop residuals for bioenergy. Little is known about the SG trait as a germplasm enhancer resource for energy storage as a system for alternative energy. Initially described as a single locus recessive trait, SG was shortly after reported as a quantitative trait governed by complex physiological and metabolic networks including chlorophyll efficiency, nitrogen contents, nutrient remobilization and source-sink balance. Together with the fact that phenotyping efforts have improved rapidly in the last decade, new approaches based on sensing technologies have had an impact in SG identification. Since SG is linked to delayed senescence, we present a review of the term senescence applied to crop residuals and bioenergy. Firstly, we discuss the idiosyncrasy of senescence. Secondly, we present biological processes that determine the fate of senescence. Thirdly, we present the genetics underlying SG for crop-trait improvement in different crops. Further, this review explores the potential uses of senescence for bioenergy crops. Finally, we discuss how high-throughput phenotyping methods assist new technologies such as genomic selection in a cost-efficient manner.

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Sharma, Neelesh, Bikram Pratap Banerjee, Matthew Hayden, and Surya Kant. "An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse." Plants 12, no. 2 (January 9, 2023): 317. http://dx.doi.org/10.3390/plants12020317.

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Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping.

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Hamany Djande, Claude Y., Chanel Pretorius, Fidele Tugizimana, Lizelle A. Piater, and Ian A. Dubery. "Metabolomics: A Tool for Cultivar Phenotyping and Investigation of Grain Crops." Agronomy 10, no. 6 (June 11, 2020): 831. http://dx.doi.org/10.3390/agronomy10060831.

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The quality of plants is often enhanced for diverse purposes such as improved resistance to environmental pressures, better taste, and higher yields. Considering the world’s dependence on plants (nutrition, medicine, or biofuel), developing new cultivars with superior characteristics is of great importance. As part of the ‘omics’ approaches, metabolomics has been employed to investigate the large number of metabolites present in plant systems under well-defined environmental conditions. Recent advances in the metabolomics field have greatly expanded our understanding of plant metabolism, largely driven by potential application to agricultural systems. The current review presents the workflow for plant metabolome analyses, current knowledge, and future directions of such research as determinants of cultivar phenotypes. Furthermore, the value of metabolome analyses in contemporary crop science is illustrated. Here, metabolomics has provided valuable information in research on grain crops and identified significant biomarkers under different conditions and/or stressors. Moreover, the value of metabolomics has been redefined from simple biomarker identification to a tool for discovering active drivers involved in biological processes. We illustrate and conclude that the rapid advances in metabolomics are driving an explosion of information that will advance modern breeding approaches for grain crops and address problems associated with crop productivity and sustainable agriculture.

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Wan, Liang, Jiangpeng Zhu, Xiaoyue Du, Jiafei Zhang, Xiongzhe Han, Weijun Zhou, Xiaopeng Li, et al. "A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles." Journal of Experimental Botany 72, no. 13 (May 8, 2021): 4691–707. http://dx.doi.org/10.1093/jxb/erab194.

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Abstract Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape (Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and cotton (Gossypium hirsutum L.), and a high accuracy of FVC retrieval was obtained, with rRMSEs of 12%, 6%, and 6%, respectively. Our findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which should be a promising tool for precision crop breeding.

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Sarkar, Sayantan, Joseph Oakes, Alexandre-Brice Cazenave, Mark D. Burow, Rebecca S. Bennett, Kelly D. Chamberlin, Ning Wang, et al. "Evaluation of the U.S. Peanut Germplasm Mini-Core Collection in the Virginia-Carolina Region Using Traditional and New High-Throughput Methods." Agronomy 12, no. 8 (August 18, 2022): 1945. http://dx.doi.org/10.3390/agronomy12081945.

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Peanut (Arachis hypogaea L.) is an important food crop for the U.S. and the world. The Virginia-Carolina (VC) region (Virginia, North Carolina, and South Carolina) is an important peanut-growing region of the U.S and is affected by numerous biotic and abiotic stresses. Identification of stress-resistant germplasm, along with improved phenotyping methods, are important steps toward developing improved cultivars. Our objective in 2017 and 2018 was to assess the U.S. mini-core collection for desirable traits, a valuable source for resistant germplasm under limited water conditions. Accessions were evaluated using traditional and high-throughput phenotyping (HTP) techniques, and the suitability of HTP methods as indirect selection tools was assessed. Traditional phenotyping methods included stand count, plant height, lateral branch growth, normalized difference vegetation index (NDVI), canopy temperature depression (CTD), leaf wilting, fungal and viral disease, thrips rating, post-digging in-shell sprouting, and pod yield. The HTP method included 48 aerial vegetation indices (VIs), which were derived using red, blue, green, and near-infrared reflectance; color space indices were collected using an octocopter drone at the same time, with traditional phenotyping. Both phenotypings were done 10 times between 4 and 16 weeks after planting. Accessions had yields comparable to high yielding checks. Correlation coefficients up to 0.8 were identified for several Vis, with yield indicating their suitability for indirect phenotyping. Broad-sense heritability (H2) was further calculated to assess the suitability of particular VIs to enable genetic gains. VIs could be used successfully as surrogates for the physiological and agronomic trait selection in peanuts. Further, this study indicates that UAV-based sensors have potential for measuring physiologic and agronomic characteristics measured for peanut breeding, variable rate input application, real time decision making, and precision agriculture applications.

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Wang, Yongjian, Weiliang Wen, Sheng Wu, Chuanyu Wang, Zetao Yu, Xinyu Guo, and Chunjiang Zhao. "Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates." Remote Sensing 11, no. 1 (December 31, 2018): 63. http://dx.doi.org/10.3390/rs11010063.

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High-throughput phenotyping technologies have become an increasingly important topic of crop science in recent years. Various sensors and data acquisition approaches have been applied to acquire the phenotyping traits. It is quite confusing for crop phenotyping researchers to determine an appropriate way for their application. In this study, three representative three-dimensional (3D) data acquisition approaches, including 3D laser scanning, multi-view stereo (MVS) reconstruction, and 3D digitizing, were evaluated for maize plant phenotyping in multi growth stages. Phenotyping traits accuracy, post-processing difficulty, device cost, data acquisition efficiency, and automation were considered during the evaluation process. 3D scanning provided satisfactory point clouds for medium and high maize plants with acceptable efficiency, while the results were not satisfactory for small maize plants. The equipment used in 3D scanning is expensive, but is highly automatic. MVS reconstruction provided satisfactory point clouds for small and medium plants, and point deviations were observed in upper parts of higher plants. MVS data acquisition, using low-cost cameras, exhibited the highest efficiency among the three evaluated approaches. The one-by-one pipeline data acquisition pattern allows the use of MVS high-throughput in further phenotyping platforms. Undoubtedly, enhancement of point cloud processing technologies is required to improve the extracted phenotyping traits accuracy for both 3D scanning and MVS reconstruction. Finally, 3D digitizing was time-consuming and labor intensive. However, it does not depend on any post-processing algorithms to extract phenotyping parameters and reliable phenotyping traits could be derived. The promising accuracy of 3D digitizing is a better verification choice for other 3D phenotyping approaches. Our study provides clear reference about phenotyping data acquisition of maize plants, especially for the affordable and portable field phenotyping platforms to be developed.

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Mir, Reyazul Rouf, Mathew Reynolds, Francisco Pinto, Mohd Anwar Khan, and Mohd Ashraf Bhat. "High-throughput phenotyping for crop improvement in the genomics era." Plant Science 282 (May 2019): 60–72. http://dx.doi.org/10.1016/j.plantsci.2019.01.007.

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Zhang, Chongyuan, Afef Marzougui, and Sindhuja Sankaran. "High-resolution satellite imagery applications in crop phenotyping: An overview." Computers and Electronics in Agriculture 175 (August 2020): 105584. http://dx.doi.org/10.1016/j.compag.2020.105584.

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Zhang, Jingwei, Liang Gong, Chengliang Liu, Yixiang Huang, Dabing Zhang, and Zheng Yuan. "Field Phenotyping Robot Design and Validation for the Crop Breeding." IFAC-PapersOnLine 49, no. 16 (2016): 281–86. http://dx.doi.org/10.1016/j.ifacol.2016.10.052.

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Suhairi, Tengku Adhwa Syaherah Tengku Mohd, Siti Sarah Mohd Sinin, Eranga M. Wimalasiri, Nur Marahaini Mohd Nizar, Anil Shekar Tharmandran, Ebrahim Jahanshiri, Peter J. Gregory, and Sayed N. Azam-Ali. "Use of Unmanned Aerial Vehicles (UAVs) Imagery in Phenotyping of Bambara Groundnut." Journal of Agricultural Science 12, no. 6 (May 15, 2020): 12. http://dx.doi.org/10.5539/jas.v12n6p12.

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In this experiment, proximal measurements and Unmanned Aerial Vehicle (UAV) imagery was used to determine growth stages for bambara groundnut (Vigna subterranea (L.) Verdc.). The crop is a high potential crop due to its ability to yield in marginal environments, but neglected and underutilised due to lack of information on its growth in different environments. This study evaluated the correlation between Normalised Difference Vegetation Index (NDVI) derived from the ground as well as airborne sensors to test the ability of remotely sensed data to identify growth stages. NDVI and chlorophyll content of bambara groundnut leaves were measured at ground level at 18, 32, 46 and 88 days after planting (DAP) comprising vegetative, flowering, pod formation and maturity growth stages. The UAV imagery for the experimental plots was acquired with 0.2m resolution at maturity. The result showed a significant (p < 0.05) linear relationship between proximal NDVI and chlorophylls content at all growth stages ofgrowth. The R2 varied from 0.57 in the vegetative stage to 0.78 in the flowering stage. Furthermore, NDVI derived from proximal measurements and UAV data showed a significant (p < 0.05) correlation. The observed high correlation between proximal sensors, UAV data and crop parameters suggest that remote sensing technologies can be used for rapid phenotyping to hasten the development of models to assess the performance of underutilised crops for food and nutrition security.

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Huang, Yixiang, Pengcheng Xia, Liang Gong, Binhao Chen, Yanming Li, and Chengliang Liu. "Designing an Interactively Cognitive Humanoid Field-Phenotyping Robot for In-Field Rice Tiller Counting." Agriculture 12, no. 11 (November 21, 2022): 1966. http://dx.doi.org/10.3390/agriculture12111966.

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Field phenotyping is a crucial process in crop breeding, and traditional manual phenotyping is labor-intensive and time-consuming. Therefore, many automatic high-throughput phenotyping platforms (HTPPs) have been studied. However, existing automatic phenotyping methods encounter occlusion problems in fields. This paper presents a new in-field interactive cognition phenotyping paradigm. An active interactive cognition method is proposed to remove occlusion and overlap for better detectable quasi-structured environment construction with a field phenotyping robot. First, a humanoid robot equipped with image acquiring sensory devices is designed to contain an intuitive remote control for field phenotyping manipulations. Second, a bio-inspired solution is introduced to allow the phenotyping robot to mimic the manual phenotyping operations. In this way, automatic high-throughput phenotyping of the full growth period is realized and a large volume of tiller counting data is availed. Third, an attentional residual network (AtResNet) is proposed for rice tiller number recognition. The in-field experiment shows that the proposed method achieves approximately 95% recognition accuracy with the interactive cognition phenotyping platform. This paper opens new possibilities to solve the common technical problems of occlusion and observation pose in field phenotyping.

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Mochida, Keiichi, Ryuei Nishii, and Takashi Hirayama. "Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits." Plant and Cell Physiology 61, no. 8 (May 11, 2020): 1408–18. http://dx.doi.org/10.1093/pcp/pcaa064.

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Abstract To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants’ later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant–environment interactions by elucidating plants’ temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant–environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.

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Lyu, Beichen, Stuart D. Smith, Yexiang Xue, Katy M. Rainey, and Keith Cherkauer. "An Efficient Pipeline for Crop Image Extraction and Vegetation Index Derivation Using Unmanned Aerial Systems." Transactions of the ASABE 63, no. 4 (2020): 1133–46. http://dx.doi.org/10.13031/trans.13661.

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HighlightsThis study addresses two computational challenges in high-throughput phenotyping: scalability and efficiency.Specifically, we focus on extracting crop images and deriving vegetation indices using unmanned aerial systems.To this end, we outline a data processing pipeline, featuring a crop localization algorithm and trie data structure.We demonstrate the efficacy of our approach by computing large-scale and high-precision vegetation indices in a soybean breeding experiment, where we evaluate soybean growth under water inundation and temporal change.Abstract. In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. Keywords: Data processing pipeline, High-throughput phenotyping, Image processing, Soybean breeding, Unmanned aerial systems, Vegetation indices.

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Journal articles on the topic 'Crop Phenotyping'

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