Modern phenomics to empower holistic crop science, agronomy, and breeding research

Ni Jiang; Xin-Guang Zhu

doi:10.1016/j.jgg.2024.04.016

Volume 51 Issue 8

Aug. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Genetics and Genomics > 2024 > 51(8): 790-800

PDF( 1372 KB)

Modern phenomics to empower holistic crop science, agronomy, and breeding research

doi: 10.1016/j.jgg.2024.04.016

Ni Jiang^a,
Xin-Guang Zhu^b

a. Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China;
b. Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China

Funds:

This work is supported by National Research and Development Program of Ministry of Science and Technology of China (2020YFA0907600, 2018YFA0900600, 2019YFA09004600), Strategic Priority Research Program of the Chinese Academy of Sciences (XDB27020105, XDB37020104, XDA24010203, XDA0450202), National Science Foundation of China (31870214), the National Key Research and Development Program of China (2023YFF1000100), and STI2030–Major Projects (2023ZD04076).

Received Date: 2023-12-29
Accepted Date: 2024-04-30
Rev Recd Date: 2024-04-25

Available Online: 2025-06-06

Publish Date: 2024-05-10

Abstract

Abstract

Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.
- Crop phenomics,
- High-throughput phenotyping,
- Traits,
- Crop designer breeding,
- Agronomy

FullText(HTML)

References(101)

References

Al-Tam, F., Adam, H., Anjos, A. dos, Lorieux, M., Larmande, P., Ghesquiere, A., Jouannic, S., Shahbazkia, H.R., 2013. P-TRAP: a panicle trait phenotyping tool. BMC Plant Biol. 13, 122.

Amezquita, E.J., Quigley, M.Y., Ophelders, T., Landis, J.B., Koenig, D., Munch, E., Chitwood, D.H., 2021. Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform. in silico Plants 4, diab033.

Amezquita, E.J., Quigley, M.Y., Ophelders, T., Munch, E., Chitwood, D.H., 2020. The shape of things to come: topological data analysis and biology, from molecules to organisms. Dev. Dynam. 249, 816-833.

Arino-Estrada, G., Mitchell, G.S., Saha, P., Arzani, A., Cherry, S.R., Blumwald, E., Kyme, A.Z., 2019. Imaging salt uptake dynamics in plants using PET. Sci. Rep. 9, 18626.

Borisjuk, L., Rolletschek, H., Neuberger, T., 2012. Surveying the plant's world by magnetic resonance imaging. Plant J. 70, 129-146.

Bucksch, A., Burridge, J., York, L.M., Das, A., Nord, E., Weitz, J.S., Lynch, J.P., 2014. Image-based high-throughput field phenotyping of crop roots. Plant Physiol. 166, 470-486.

Chandra, A.L., Desai, S.V., Guo, W., Balasubramanian, V.N., 2020. Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: a Survey. (arXiv [cs.CV]).

Chang, T.-G., Chang, S., Song, Q.-F., Perveen, S., and Zhu, X.-G., 2019. Systems models, phenomics and genomics: three pillars for developing high-yielding photosynthetically efficient crops. In Silico Plants 1.

Crossa, J., Perez-Rodriguez, P., Cuevas, J., Montesinos-Lopez, O., Jarquin, D., de los Campos, G., Burgueno, J., Gonzalez-Camacho, J. M., Perez-Elizalde, S., Beyene, Y. et al., 2017. Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci. 22, 961-975.

Demidchik, V.V., Shashko, A.Y., Bandarenka, U.Y., Smolikova, G.N., Przhevalskaya, D.A., Charnysh, M.A., Pozhvanov, G.A., Barkosvkyi, A.V., Smolich, I.I., Sokolik, A.I., et al., 2020. Plant phenomics: fundamental bases, software and hardware platforms, and machine learning. Russ. J. Plant Physiol. 67, 397-412.

Du, J., Lu, X., Fan, J., Qin, Y., Yang, X., Guo, X., 2020. Image-based high-throughput detection and phenotype evaluation method for multiple lettuce varieties. Front. Plant Sci. 11, 563386.

Duncan, K.E., Czymmek, K.J., Jiang, N., Thies, A.C., Topp, C.N., 2022. X-ray microscopy enables multiscale high-resolution 3D imaging of plant cells, tissues, and organs. Plant Physiol. 188, 831-845.

Feng, H., Chen, Y., Song, J., Lu, B., Shu, C., Qiao, J., Liao, Y., Yang, W., 2024. Maturity classification of rapeseed using hyperspectral image combined with machine learning. Plant Phenomics 6, 139.

Furbank, R.T., Jimenez-Berni, J.A., George-Jaeggli, B., Potgieter, A.B., n.d. Deery D.M.,2019. Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops. New Phytol 223 (4), 1714–1727.

Furbank, R.T., Tester, M., 2011. Phenomics - technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16, 635-644.

Gimenez-Gallego, J., Gonzalez-Teruel, J.D., Jimenez-Buendia, M., Toledo-Moreo, A.B., Soto-Valles, F., Torres-Sanchez, R., 2019. Segmentation of multiple tree leaves pictures with natural backgrounds using deep learning for image-based agriculture applications. NATO Adv. Sci. Inst. Ser. E Appl. Sci. 10, 202.

Gu, J., Yin, X., Stomph, T.-J., Wang, H., Struik, P.C., 2012. Physiological basis of genetic variation in leaf photosynthesis among rice (Oryza sativa L.) introgression lines under drought and well-watered conditions. J. Exp. Bot. 63, 5137-5153.

Guo, Q., Wu, F., Pang, S., Zhao, X., Chen, L., Liu, J., Xue, B., Xu, G., Li, L., Jing, H., Chu, C., 2018. Crop 3D-a LiDAR based platform for 3D high-throughput crop phenotyping. Sci. China Life Sci. 61, 328-339.

Hao, D., Asrar, G.R., Zeng, Y., Yang, X., Li, X., Xiao, J., Guan, K., Wen, J., Xiao, Q., Berry, J.A., et al., 2021. Potential of hotspot solar-induced chlorophyll fluorescence for better tracking terrestrial photosynthesis. Global Change Biol. 27, 2144-2158.

Hartmann, A., Czauderna, T., Hoffmann, R., Stein, N., Schreiber, F., 2011. HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinf. 12, 148.

Heeraman, D.A., Hopmans, J.W., Clausnitzer, V., 1997. Three dimensional imaging of plant roots in situ with X-ray Computed Tomography. Plant Soil 189, 167-179.

Holman, F.H., Riche, A.B., Michalski, A., Castle, M., Wooster, M.J., Hawkesford, M.J., 2016. High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Rem. Sens. 8, 1031.

Houle, D., Govindaraju, D.R., Omholt, S., 2010. Phenomics: the next challenge. Nat. Rev. Genet. 11, 855-866.

Huang, X., Han, B., 2014. Natural variations and genome-wide association studies in crop plants. Annu. Rev. Plant Biol. 65, 531-551.

Humplik, J.F., Lazar, D., Husickova, A., Spichal, L., 2015. Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses - a review. Plant Methods 11, 1-10.

Jiang, F., Lu, Y., Chen, Y., Cai, D., Li, n.d. G.,2020. Image recognition of four rice leaf diseases based on deep learning and support vector machine. Comput Electron Agric 179, 105824.

Jiang, N., Floro, E., Bray, A.L., Laws, B., Duncan, K.E., Topp, C.N., 2019. Three-dimensional time-lapse analysis reveals multiscale relationships in maize root systems with contrasting architectures. Plant Cell 31, 1708-1722.

Jiang, Z., Tu, H., Bai, B., Yang, C., Zhao, B., Guo, Z., Liu, Q., Zhao, H., Yang, W., Xiong, L., et al., 2021. Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress. New Phytol. 232, 440-455.

Johnson, X., Vandystadt, G., Bujaldon, S., Wollman, F.-A., Dubois, R., Roussel, P., Alric, J., Beal, D., 2009. A new setup for in vivo fluorescence imaging of photosynthetic activity. Photosynth. Res. 102, 85-93.

Keller, K., Kirchgessner, N., Khanna, R., Siegwart, R., Walter, A., Aasen, H., 2018. Soybean leaf coverage estimation with machine learning and thresholding algorithms for field phenotyping. In Proceedings of the British Machine Vision Conference, (Newcastle, UK).

Khan, I.H., Liu, H., Li, W., Cao, A., Wang, X., Liu, H., Cheng, T., Tian, Y., Zhu, Y., Cao, W., et al., 2021. Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral images in wheat. Remote Sensing 13 (18), 3612.

Kim, M.-Y., Lee, K.H., 2022. Electrochemical sensors for sustainable precision agriculture-a review. Front. Chem. 10:848320.

Li, D., Quan, C., Song, Z., Li, X., Yu, G., Li, C., Muhammad, A., 2020. High-throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field. Front. Bioeng. Biotechnol. 8, 623705.

Li, L., Hassan, M.A., Yang, S., Jing, F., Yang, M., Rasheed, A., Wang, J., Xia, X., He, Z., Xiao, Y., 2022. Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies. Crop J. 10, 1303-1311.

Li, M., Frank, M.H., Coneva, V., Mio, W., Chitwood, D.H., Topp, C.N., 2018. The persistent homology mathematical framework provides enhanced genotype-to-phenotype associations for plant morphology. Plant Physiol. 177, 1382-1395.

Li, M., Klein, L.L., Duncan, K.E., Jiang, N., Chitwood, D.H., Londo, J.P., Miller, A.J., Topp, C.N., 2019. Characterizing 3D inflorescence architecture in grapevine using X-ray imaging and advanced morphometrics: implications for understanding cluster density. J. Exp. Bot. 70, 6261-6276.

Lin, Y., 2015. LiDAR: an important tool for next-generation phenotyping technology of high potential for plant phenomics? Comput. Electron. Agric. 119, 61-73.

Liu, F., Song, Q., Zhao, J., Mao, L., Bu, H., Hu, Y., Zhu, X.-G., 2021. Canopy occupation volume as an indicator of canopy photosynthetic capacity. New Phytol. 232, 941-956.

Lobet, G., 2017. Image analysis in plant sciences: publish then perish. Trends Plant Sci. 22, 559-566.

Long, S.P., Taylor, S.H., Burgess, S.J., Carmo-Silva, E., Lawson, T., De Souza, A.P., Leonelli, L., Wang, Y., 2022. Into the shadows and back into sunlight: photosynthesis in fluctuating light. Annu. Rev. Plant Biol. 73: 617-648.

Lu, J., Tan, L., Jiang, H., 2021. Review on Convolutional Neural Network (CNN) applied to plant leaf disease classification. Collect. FAO Agric. 11, 707.

Mairhofer, S., Zappala, S., Tracy, S., Sturrock, C., Bennett, M., Mooney, S., Pridmore, T., 2012. RooTrak: automated recovery of 3D plant root architecture in soil from X-ray Micro Computed Tomography using visual tracking. Plant Physiol. 158, 561-569.

Marsh, J.I., Hu, H., Gill, M., Batley, J., Edwards, D., 2021. Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics. Theor. Appl. Genet. 134, 1677-1690.

Matthews, J.S.A., Vialet-Chabrand, S.R.M., Lawson, T., 2017. Diurnal variation in gas exchange: the balance between carbon fixation and water loss. Plant Physiol. 174: 614-623.

Metzner, R., Eggert, A., van Dusschoten, D., Pflugfelder, D., Gerth, S., Schurr, U., Uhlmann, N., Jahnke, S., 2015. Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: potential and challenges for root trait quantification. Plant Methods 11: 17.

Minervini, M., Scharr, H., Tsaftaris, S.A., 2015. Image analysis: the new bottleneck in plant phenotyping. IEEE Signal Process. Mag. 32: 126-131.

Miyoshi, Y., Nagao, Y., Yamaguchi, M., Suzui, N., Yin, Y.-G., Kawachi, N., Yoshida, E., Takyu, S., Tashima, H., Yamaya, T. et al., 2021. Plant root PET: visualization of photosynthate translocation to roots in rice plant. J. Instrum. 16, C12018.

Mulero, G., Jiang, D., Bonfil, D.J., Helman, D., 2023. Use of thermal imaging and the photochemical reflectance index (PRI) to detect wheat response to elevated CO2 and drought. Plant Cell Environ. 46, 76-92.

Pasala, R., Pandey, B.B., 2020. Plant phenomics: high-throughput technology for accelerating genomics. J. Bio. Sci. 45.

Paulus, S., 2019. Measuring crops in 3D: using geometry for plant phenotyping. Plant Methods 15, 103.

Paulus, S., Mahlein, A.-K., 2020. Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale. GigaScience 9, giaa090.

Perez-Sanz, F., Navarro, P.J., Egea-Cortines, M., 2017. Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms. GigaScience 6, 1-18.

Pflugfelder, D., Metzner, R., van Dusschoten, D., Reichel, R., Jahnke, S., Koller, R., 2017. Non-invasive imaging of plant roots in different soils using magnetic resonance imaging (MRI). Plant Methods 13, 102.

Pieruschka, R., Schurr, U., 2019. Plant phenotyping: past, present, and future. Plant Phenomics 2019, 7507131.

Pineda, M., Baron, M., Perez-Bueno, M.-L., 2020. Thermal imaging for plant stress detection and phenotyping. Rem. Sens. 13, 68.

Prado, S.A., Cabrera-Bosquet, L., Grau, A., Coupel-Ledru, A., Millet, E.J., Welcker, C., Tardieu, F., 2018. Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand. Plant Cell Environ. 41, 314-326.

Reymond, M., Muller, B., Leonardi, A., Charcosset, A., Tardieu, F., 2003. Combining quantitative trait Loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiol. 131, 664-675.

Ronellenfitsch, H., Lasser, J., Daly, D.C., Katifori, E., 2015. Topological phenotypes constitute a new dimension in the phenotypic space of leaf venation networks. PLoS Comput. Biol. 11, e1004680.

Sandhu, J., Zhu, F., Paul, P., Gao, T., Dhatt, B.K., Ge, Y., Staswick, P., Yu, H., Walia, H., 2019. PI-Plat: a high-resolution image-based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits. Plant Methods 15, 162.

Sandhu, K.S., Mihalyov, P.D., Lewien, M.J., Pumphrey, M.O., Carter, A.H., 2021. Combining genomic and phenomic information for predicting grain protein content and grain yield in spring wheat. Front. Plant Sci. 12: 613300.

Saric, R., Nguyen, V.D., Burge, T., Berkowitz, O., Trtilek, M., Whelan, J., Lewsey, M.G., Custovic, E., 2022. Applications of hyperspectral imaging in plant phenotyping. Trends Plant Sci. 27, 301-315.

Schneider, H.M., Postma, J.A., Kochs, J., Pflugfelder, D., Lynch, J.P., van Dusschoten, D., 2020. Spatio-temporal variation in water uptake in seminal and nodal root systems of barley plants grown in soil. Front. Plant Sci. 11, 1247.

Shafiekhani, A., Kadam, S., Fritschi, F.B., DeSouza, G.N., 2017. Vinobot and Vinoculer: two robotic platforms for high-throughput field phenotyping. Sensors 17, 214.

Shi, Z., Chang, T.-G., Chen, G., Song, Q., Wang, Y., Zhou, Z., Wang, M., Qu, M., Wang, B., Zhu, X.-G., 2019. Dissection of mechanisms for high yield in two elite rice cultivars. Field Crops Res. 241, 107563.

Shu, M., Dong, Q., Fei, S., Yang, X., Zhu, J., Meng, L., Li, B., Ma, Y., 2022. Improved estimation of canopy water status in maize using UAV-based digital and hyperspectral images. Comput. Electron. Agric. 197, 106982.

Soltaninejad, M., Sturrock, C.J., Griffiths, M., Pridmore, T.P., Pound, M.P., 2020. Three dimensional root CT segmentation using multi-resolution encoder-decoder networks. IEEE Trans. Image Process. 29, 6667-6679.

Song, Q., Van Rie, J., Den Boer, B., Galle, A., Zhao, H., Chang, T., He, Z., Zhu, X.-G., 2022. Diurnal and seasonal variations of photosynthetic energy conversion efficiency of field grown wheat. Front. Plant Sci. 13, 817654.

Song, Q., Xiao, H., Xiao, X., Zhu, X.-G., 2016. A new canopy photosynthesis and transpiration measurement system (CAPTS) for canopy gas exchange research. Agric. For. Meteorol. 217, 101-107.

Sun, D., Robbins, K., Morales, N., Shu, Q., Cen, H., 2022. Advances in optical phenotyping of cereal crops. Trends Plant Sci. 27, 191-208.

Sun, Y., Gu, L., Wen, J., van Der Tol, C., 2023a. From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I-Harnessing theory. Global Change Biol. 29, 2926-2952.

Sun, Z., Wang, X., Song, Y., Li, Q., Song, J., Cai, J., Zhou, Q., Zhong, Y., Jin, S., Jiang, D., 2023b. StomataTracker: revealing circadian rhythms of wheat stomata with in-situ video and deep learning. Comput. Electron. Agric. 212, 108120.

Taghavi Namin, S., Esmaeilzadeh, M., Najafi, M., Brown, T.B., Borevitz, J.O., 2018. Deep phenotyping: deep learning for temporal phenotype/genotype classification. Plant Methods 14, 66.

Tang, Z., Chen, Z., Gao, Y., Xue, R., Geng, Z., Bu, Q., Wang, Y., Chen, X., Jiang, Y., Chen, F., et al., 2023. A strategy for the acquisition and analysis of image-based phenome in rice during the whole growth period. Plant Phenomics 5, 58.

Tayade, R., Yoon, J., Lay, L., Khan, A.L., Yoon, Y., Kim, Y., 2022. Utilization of spectral indices for high-throughput phenotyping. Plants 11.

Tester, M., Langridge, P., 2010. Breeding technologies to increase crop production in a changing world. Science 327, 818-822.

Ubbens, J., Cieslak, M., Prusinkiewicz, P., Parkin, I., Ebersbach, J., Stavness, I., 2020. Latent space phenotyping: automatic image-based phenotyping for treatment studies. Plant Phenomics 2020, 5801869.

van Bezouw, R.F.H.M., Keurentjes, J.J.B., Harbinson, J., Aarts, M.G.M., 2019. Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency. Plant J. 97, 112-133.

Vialet-Chabrand, S., Lawson, T., 2019. Dynamic leaf energy balance: deriving stomatal conductance from thermal imaging in a dynamic environment. J. Exp. Bot. 70, 2839-2855.

Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P., Hawkesford, M.J., 2016. Field Scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring. Funct. Plant Biol. 44, 143-153.

Walter, A., Liebisch, F., Hund, A., 2015. Plant phenotyping: from bean weighing to image analysis. Plant Methods 11: 14.

Wang, C., Sun, M., Liu, L., Zhu, W., Liu, P., Li, X., 2022. A high-accuracy genotype classification approach using time series imagery. Biosyst. Eng. 220, 172-180.

Wang, H., Qian, X., Zhang, L., Xu, S., Li, H., Xia, X., Dai, L., Xu, L., Yu, J., Liu, X., 2018. A method of high throughput monitoring crop physiology using chlorophyll fluorescence and multispectral imaging. Front. Plant Sci. 9, 407.

Wang, Y., Song, Q., Jaiswal, D., P. de Souza, A., Long, S.P., Zhu, X.-G., 2017. Development of a three-dimensional ray-tracing model of sugarcane canopy photosynthesis and its application in assessing impacts of varied row spacing. Bioenergy Res. 10, 626-634.

Wang, Y.-H., Su, W.-H., 2022. Convolutional neural networks in computer vision for grain crop phenotyping: a review. Agronomy 12, 2659.

Wu, S., Wen, W., Xiao, B., Guo, X., Du, J., Wang, C., Wang, Y., 2019. An accurate skeleton extraction approach from 3D point clouds of maize plants. Front. Plant Sci. 10, 248.

Xiao, Y., Chang, T., Song, Q., Wang, S., Tholen, D., Wang, Y., Xin, C., Zheng, G., Zhao, H., Zhu, X.-G., 2017. ePlant for quantitative and predictive plant science research in the big data era -Lay the foundation for the future model guided crop breeding, engineering and agronomy. Quantitative Biology 5, 260-271.

Xiong, X., Yu, L., Yang, W., Liu, M., Jiang, N., Wu, D., Chen, G., Xiong, L., Liu, K., Liu, Q., 2017. A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage. Plant Methods 13, 7.

Xu, Z., Valdes, C., Clarke, J., 2018. Existing and potential statistical and computational approaches for the analysis of 3D CT images of plant roots. Agronomy 8, 71.

Yang, W., Guo, Z., Huang, C., Duan, L., Chen, G., Jiang, N., Fang, W., Feng, H., Xie, W., Lian, X. et al., 2014. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat. Commun. 5, 5087.

Xue, Y., Chong, K., Han, B., Gui, J., Wang, T., Fu, X., Cheng, Z., 2015. New chapter of designer breeding in China: update on strategic program of molecular module-based designer breeding systems. Bull. Chin. Acad. Sci.

Yang, W., Feng, H., Zhang, X., Zhang, J., Doonan, J.H., Batchelor, W.D., Xiong, L., Yan, J., 2020. Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Mol. Plant 13, 187-214.

Yu, S., Ali, J., Zhou, S., Ren, G., Xie, H., Xu, J., Yu, X., Zhou, F., Peng, S., Ma, L. et al., 2022. From Green Super Rice to green agriculture: reaping the promise of functional genomics research. Mol. Plant 15, 9-26.

Zaman-Allah, M., Vergara, O., Araus, J. L., Tarekegne, A., Magorokosho, C., Zarco-Tejada, P. J., Hornero, A., Alba, A. H., Das, B., Craufurd, P. et al., 2015. Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods 11, 35.

Zeng, D., Li, M., Jiang, N., Ju, Y., Schreiber, H., Chambers, E., Letscher, D., Ju, T., Topp, C.N., 2021. TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging. Plant Methods 17, 127.

Zhang, C., Kong, J., Wu, D., Guan, Z., Ding, B., Chen, F., 2023. Wearable sensor: an emerging data collection tool for plant phenotyping. Plant Phenomics 5: 51.

Zhou, Y., Kusmec, A., Mirnezami, S.V., Attigala, L., Srinivasan, S., Jubery, T.Z., Schnable, J.C., Salas-Fernandez, M.G., Ganapathysubramanian, B., Schnable, P.S., 2021. Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping. Plant Cell 33, 2562-2582.

Zhu, X., Chang, T., Song, Q., Chang, S., Wang, C., Zhang, G., Guo, Y., Zhou, S., 2020. ePlant: scientific connotations, bottlenecks, and development strategies. Synth. Biol. J. 1, 285.

Zhu, X.G., Song, Q., Ort, D.R., 2012. Elements of a dynamic systems model of canopy photosynthesis. Curr. Opin. Plant Biol. 15: 237-244.

Zhu, X., Zhang, G., Tholen, D., Wang, Y., Xin, C., Song, Q., 2011. The next generation models for crops and agro-ecosystems. Sci. China Inf. Sci. 54, 589-597.

Zhu, X.-G., Lynch, J.P., LeBauer, D.S., Millar, A.J., Stitt, M., Long, S.P., 2016. Plants in silico: why, why now and what?-an integrative platform for plant systems biology research. Plant Cell Environ. 39, 1049-1057.

Zhu, X.G., Marcelis, L., 2023. Vertical farming for crop production. Mod. Agric. 1, 13-15.

Relative Articles

Supplements(0)

Cited By

Proportional views