Journal of Integrative Agriculture ›› 2024, Vol. 23 ›› Issue (6): 1787-1802.DOI: 10.1016/j.jia.2023.10.019
• • 下一篇
收稿日期:
2023-07-10
接受日期:
2023-09-14
出版日期:
2024-06-20
发布日期:
2024-05-29
Mansoor
Sheikh1, Farooq Iqra2, Hamadani Ambreen3,
Kumar A Pravin2, Manzoor Ikra4, Yong Suk Chung1#
1 Phenomics Laboratory, Department of Plant Resources and Environment, Jeju National University, Jeju 63234, Republic of Korea
2 Council of Scientific & Industrial Research, Indian Institute of Integrative Medicine, Pulwama, J&K 192301, India
3 Animal and Dairy Science, University of Wisconsin, Madison, WI 530706, United States of America
4 Division of Fruit Science, Faculty of Horticulture, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar 190025, India
Received:
2023-07-10
Accepted:
2023-09-14
Online:
2024-06-20
Published:
2024-05-29
About author:
#Correspondence Yong Suk Chung, E-mail: yschung@jejunu.ac.kr
Supported by:
Mansoor Sheikh, Farooq Iqra, Hamadani Ambreen, Kumar A Pravin, Manzoor Ikra, Yong Suk Chung. [J]. Journal of Integrative Agriculture, 2024, 23(6): 1787-1802.
Mansoor Sheikh, Farooq Iqra, Hamadani Ambreen, Kumar A Pravin, Manzoor Ikra, Yong Suk Chung. Integrating artificial intelligence and high-throughput phenotyping for crop improvement[J]. Journal of Integrative Agriculture, 2024, 23(6): 1787-1802.
Ampatzidis Y, Partel V. 2019. UAV-based high throughput phenotyping in citrus utilizing multispectral imaging and artificial intelligence. Remote Sensing, 11, 410. Araus J L, Kefauver S C, Zaman-Allah M, Olsen M S, Cairns J E. 2018. Translating high-throughput phenotyping into genetic gain. Trends in Plant Science, 23, 451–466. Arend D, Beier S, König P, Lange M, Memon J A, Oppermann M, Scholz U, Weise S. 2022. From genotypes to phenotypes: A plant perspective on current developments in data management and data publication. In: Integrative Bioinformatics: History and Future. Springer Singapore, Singapore. pp. 11–43. Ataş M, Yardimci Y, Temizel A. 2012. A new approach to aflatoxin detection in chili pepper by machine vision. Computers and Electronics in Agriculture, 87, 129–141. Ates A M, Bukowski M. 2022. Amber waves: The economics of food, farming, natural resources, and rural America, 2022. Oil Crops Outlook: September 2022. OCS-22i, USA. Benos L, Tagarakis A C, Dolias G, Berruto R, Kateris D, Bochtis D. 2021. Machine learning in agriculture: A comprehensive updated review. Sensors, 21, 3758. Bolger A M, Poorter H, Dumschott K, Bolger M E, Arend D, Osorio S, Gundlach H, Mayer K F, Lange M, Scholz U, Usadel B. 2019. Computational aspects underlying genome to phenome analysis in plants. The Plant Journal, 97, 182–198. Busby P E, Soman C, Wagner M R, Friesen M L, Kremer J, Bennett A, Morsy M, Eisen J A, Leach J E, Dangl J L. 2017. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biology, 15, e2001793. Bustos-Korts D, Boer M P, Malosetti M, Chapman S, Chenu K, Zheng B, Van Eeuwijk F A. 2019. Combining crop growth modeling and statistical genetic modeling to evaluate phenotyping strategies. Frontiers in Plant Science, 10, 1491. Chawade A, van Ham J, Blomquist H, Bagge O, Alexandersson E, Ortiz R. 2019. High-throughput field-phenotyping tools for plant breeding and precision agriculture. Agronomy, 9, 258 Cobb J N, DeClerck G, Greenberg A, Clark R, McCouch S. 2013. Next-generation phenotyping: Requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement. Theoretical and Applied Genetics, 126, 867–887. Confalonieri R, Paleari L, Foi M, Movedi E, Vesely F M, Thoelke W, Agape C, Borlini G, Ferri I, Massara F, Motta R. 2017. PocketPlant3D: Analysing canopy structure using a smartphone. Biosystems Engineering, 164, 1–12. Coppens F, Wuyts N, Inzé D, Dhondt S. 2017. Unlocking the potential of plant phenotyping data through integration and data-driven approaches. Current Opinion in Systems Biology, 4, 58–63. Corona-Lopez D D, Sommer S, Rolfe S A, Podd F, Grieve B D. 2019. Electrical impedance tomography as a tool for phenotyping plant roots. Plant Methods, 15, 1–15. Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, De Los Campos G, Burgueño J, González-Camacho J M, Pérez-Elizalde S, Beyene Y, Dreisigacker S. 2017. Genomic selection in plant breeding: methods, models, and perspectives. Trends in Plant Science, 22, 961–975. Delgado A, Hays D B, Bruton R K, Ceballos H, Novo A, Boi E, Selvaraj M G. 2017. Ground penetrating radar: A case study for estimating root bulking rate in cassava (Manihot esculenta Crantz). Plant Methods, 13, 1–11. van Dijk A D J, Kootstra G, Kruijer W, de Ridder D. 2021. Machine learning in plant science and plant breeding. Iscience, 24, 101890. Fahlgren N, Gehan M A, Baxter I. 2015. Lights, camera, action: High-throughput plant phenotyping is ready for a close-up. Current Opinion in Plant Biology, 24, 93–99. Fiorani F, Schurr U. 2013. Future scenarios for plant phenotyping. Annual Review of Plant Biology, 64, 267–291. Fukao T, Xiong L. 2013. Genetic mechanisms conferring adaptation to submergence and drought in rice: simple or complex. Current Opinion in Plant Biology, 16, 196–204. Furbank R T, Tester M. 2011. Phenomics–technologies to relieve the phenotyping bottleneck. Trends in Plant Science, 16, 635–644. Ganesan M, Andavar S, Raj R S P. 2021. Prediction of land suitability for crop cultivation using classification techniques. Brazilian Archives of Biology and Technology, 64, e21200483. Ghimire A, Seong-Hoon K, Areum C, Naeun J, Seonhwa A, Mohammad S I, Sheikh M, Yong S C, Yoonha K. 2023. Automatic evaluation of soybean seed traits using RGB image data and a python algorithm. Plants, 12, 3078. Gill T, Gill S K, Saini D K, Chopra Y, de Koff J P, Sandhu K S. 2022. A comprehensive review of high throughput phenotyping and machine learning for plant stress phenotyping. Phenomics, 2, 156–183. Goff S A, Vaughn M, McKay S, Lyons E, Stapleton A E, Gessler D, Matasci N, Wang L, Hanlon M, Lenards A, Muir A. 2011. The iPlant collaborative: Cyberinfrastructure for plant biology. Frontiers in Plant Science, 2, 34. Gowda V D, Prabhu M S, Ramesha M, Kudari J M, Samal A. 2021. Smart agriculture and smart farming using IoT technology. Journal of Physics: Conference Series, 2089, id.012038. 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. Science China Life Sciences, 61, 328–339. Hamadani A, Ganai N A. 2022. Development of a multi-use decision support system for scientific management and breeding of sheep. Scientific Reports, 12, 19360. Hamadani A, Ganai N A, Alam S, Mudasir S, Raja T A, Hussain I, Ali H, Ahmad S M. 2022. Artificial intelligence techniques for the prediction of body weights in sheep. Indian Journal of Animal Research, B–4831, 1–6. Hamadani H, Khan A. 2015. Automation in livestock farming - A technological revolution. International Journal of Advance Research, 3, 1335–1344. Haq M A. 2022. CNN based automated weed detection system using UAV imagery. Computer Systems Science & Engineering, 42, 2. Harfouche A L, Jacobson D A, Kainer D, Romero J C, Harfouche A H, Mugnozza G S, Moshelion M, Tuskan G A, Keurentjes J J, Altman A. 2019. Accelerating climate resilient plant breeding by applying next-generation artificial intelligence. Trends in Biotechnology, 37, 1217–1235. Hassan A R, Olanrewaju R O, Chukwudum Q C, Olanrewaju S A, Fadugba S E. 2022. Comparison study of generative and discriminative models for classification of classifiers. International Journal of Mathematics and Computers in Simulation, 16, 76–87. Hughes A, Askew K, Scotson C P, Williams K, Sauze C, Corke F, Doonan J H, Nibau C. 2017. Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography. Plant Methods, 13, 1–16. Hughes A, Oliveira H R, Fradgley N, Corke F M, Cockram J, Doonan J H, Nibau C. 2019. μCT trait analysis reveals morphometric differences between domesticated temperate small grain cereals and their wild relatives. The Plant Journal, 99, 98–111. Islam M, Shehzad F. 2022. A prediction model optimization critiques through centroid clustering by reducing the sample size, integrating statistical and machine learning techniques for wheat productivity. Scientifica, doi: 10.1155/2022/7271293. Jafari M, Shahsavar A. 2020. The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress. PLoS ONE, 15, e0240427. Jahnke S, Roussel J, Hombach T, Kochs J, Fischbach A, Huber G, Scharr H. 2016. Pheno seeder - A robot system for automated handling and phenotyping of individual seeds. Plant Physiology, 172, 1358–1370. Jannoura R, Brinkmann K, Uteau D, Bruns C, Joergensen R G. 2015. Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter. Biosystems Engineering, 129, 341–351. Jo H, Lee J Y, Cho H, Choi H J, Son C K, Bae J S, Bilyeu K, Song J T, Lee J D. 2021. Genetic diversity of soybeans (Glycine max (L.) merr.) with black seed coats and green cotyledons in Korean germplasm. Agronomy, 11, 581. Jung D H, Kim C Y, Lee T S, Park S H. 2022 Depth image conversion model based on CycleGAN for growing tomato truss identification. Plant Methods, 18, 83. Jung J, Maeda M, Chang A, Bhandari M, Ashapure A, Landivar-Bowles J. 2021. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Current Opinion in Biotechnology, 70, 15–22. Karunathilake E M B M, Tuan Le A, Seong H, Yong S C, Sheikh M. 2023. The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture, 13, 1593. Kim J Y. 2020. Roadmap to high throughput phenotyping for plant breeding. Journal of Biosystems Engineering, 45, 43–55. Kim J, Lee C, Park J E, Mansoor S, Chung Y S, Kim K. 2023. Drought stress restoration frequencies of phenotypic indicators in early vegetative stages of soybean (Glycine max L.). Sustainability, 15, 4852. Koh J C, Spangenberg G, Kant S. 2021. Automated machine learning for high-throughput image-based plant phenotyping. Remote Sensing, 13, 858. Ku K B, Mansoor S, Han G D, Chung Y S, Tuan T T. 2023 Identification of new cold tolerant Zoysia grass species using high-resolution RGB and multi-spectral imaging. Scientific Reports, 13, 13209. Latif N A, Nain F N M , Malim N H A H, Abdullah R, Rahim M F A, Mohamad M N, Fauzi N S M. 2021. Predicting heritability of oil palm breeding using phenotypic traits and machine learning. Sustainability, 13, 12613. Li L, Zhang Q, Huang D. 2014. A review of imaging techniques for plant phenotyping. Sensors, 14, 20078–20111. Li Y, Xiao J, Chen L, Huang X, Cheng Z, Han B, Zhang Q, Wu C. 2018. Rice functional genomics research: past decade and future. Molecular Plant, 11, 359–380. Liu F, Hu P, Zheng B, Duan T, Zhu B, Guo Y. 2021. A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images. Agricultural and Forest Meteorology, 296, 108231. Liu H, Bruning B, Garnett T, Berger B. 2020. Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing. Computers and Electronics in Agriculture, 175, 105621. Maes W H, Steppe K. 2019. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science, 24, 152–164. McMullen M D, Kresovic S, Villeda H S, Bradbury P, Li H, Sun Q, Flint-Garcia S, Thornsberry J, Acharya C, Bottoms C, Brown P. 2009. Genetic properties of the maize nested association mapping population. Science, 325, 737–740. Mentsiev A U, Amirova E F. 2020. IoT and mechanization in agriculture: Problems, solutions, and prospects. In: IOP Conference Series: Earth and Environmental Science. 548, 032035. Merrick L F, Herr A W, Sandhu K S, Lozada D N, Carter A H. 2022. Optimizing plant breeding programs for genomic selection. Agronomy, 12, 714. 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, 1–11. Minervini M, Scharr H, Tsaftaris S A. 2015. Image analysis: The new bottleneck in plant phenotyping. IEEE Signal Processing Magazine, 32, 126–131. Mir R R, Reynolds M, Pinto F, Khan M A, Bhat M A. 2019. High-throughput phenotyping for crop improvement in the genomics era. Plant Science, 282, 60–72. Mishra B, Kumar N, Mukhtar M S. 2019. Systems biology and machine learning in plant–pathogen interactions. Molecular Plant (Microbe Interactions), 32, 45–55. Mochida K, Koda S, Inoue K, Hirayama T, Tanaka S, Nishii R, Melgani F. 2019. Computer vision-based phenotyping for improvement of plant productivity: A machine learning perspective. GigaScience, 8, doi: 10.1093/gigascience/giy153. Montes J M, Melchinger A E, Reif J C. 2007. Novel throughput phenotyping platforms in plant genetic studies. Trends in Plant Science, 12, 433–436. Morisse M, Wells D M, Millet E J, Lillemo M, Fahrner S, Cellini F, Lootens P, Muller O, Herrera J M, Bentley A R, Janni M. 2022. A European perspective on opportunities and demands for field-based crop phenotyping. Field Crops Research, 276, 108371. Nabwire S, Suh H K, Kim M S, Baek I, Cho B K. 2021. Application of artificial intelligence in phenomics. Sensors, 21, 4363. Nagasubramanian K, Singh A, Singh A, Sarkar S, Ganapathysubramanian B. 2022. Plant phenotyping with limited annotation: Doing more with less. The Plant Phenome Journal, 5, e20051. Pabuayon I L B, Sun Y, Guo W, Ritchie G L. 2019. High-throughput phenotyping in cotton: A review. Journal of Cotton Research, 2, 1–9. Parmley K A, Higgins R H, Ganapathysubramanian B, Sarkar S, Singh A K. 2019. Machine learning approach for prescriptive plant breeding. Scientific Reports, 9, 7132. Parry M A, Reynolds M, Salvucci M E, Raines C, Andralojc P J, Zhu X G, Price G D, Condon A G, Furbank R T. 2011. Raising yield potential of wheat. II. Increasing photosynthetic capacity and efficiency. Journal of Experimental Botany, 62, 453–467. Passioura J B. 2012. Phenotyping for drought tolerance in grain crops: when is it useful to breeders. Functional Plant Biology, 39, 851–859. Pieruschka R, Schurr U. 2019. Plant phenotyping: past, present, and future. Plant Phenomics, doi: 10.34133/2019/7507131. Poorter H, Fiorani F, Stitt M, Schurr U, Finck A, Gibon Y, Usadel B, Munns R, Atkin O K, Tardieu F, Pons T L. 2012. The art of growing plants for experimental purposes: A practical guide for the plant biologist. Functional Plant Biology, 39, 821–838. Rai K K. 2022. Integrating speed breeding with artificial intelligence for developing climate-smart crops. Molecular Biology Reports, 49, 11385–11402. Rebetzke G J, Jimenez-Berni J, Fischer R A, Deery D M, Smith D J. 2019. High-throughput phenotyping to enhance the use of crop genetic resources. Plant Science, 282, 40–48. Rouphael Y, Spíchal L, Panzarová K, Casa R, Colla G. 2018. High-throughput plant phenotyping for developing novel biostimulants: From lab to field or from field to lab. Frontiers in Plant Science, 9, 1197. Sadeghi-Tehran P, Sabermanesh K, Virlet N, Hawkesford M J. 2017. Automated method to determine two critical growth stages of wheat: Heading and flowering. Frontiers in Plant Science, 8, 252. Sagan V, Maimaitijiang M, Paheding S, Bhadra S, Gosselin N, Burnette M, Demieville J, Hartling S, LeBauer D, Newcomb M, Pauli D. 2021. Data-driven artificial intelligence for calibration of hyperspectral big data. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–20. Sarić R, NguyenV D, Burge T, Berkowitz O, Trtílek M, Whelan J, Lewsey M G, Čustović E. 2022. Applications of hyperspectral imaging in plant phenotyping. Trends in Plant Science, 27, 301–315. Shakoor N, Lee S, Mockler T C. 2017. High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current Opinion in Plant Biology, 38, 184–192. Singh A, Ganapathysubramanian B, Singh A, Sarkar S. 2016. Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 21, 110–124. Singh P, Singh N, Singh K K, Singh A. 2021. Diagnosing of disease using machine learning. In: Machine Learning and the Internet of Medical Things in Healthcare. Academic Press, India. pp. 89–111. Šulc M, Matas J. 2017. Fine-grained recognition of plants from images. Plant Methods, 13, 1–14. Sun D, Cen H, Weng H, Wan L, Abdalla A, El-Manawy A I, Zhu Y, Zhao N, Fu H, Tang J, Li X. 2019. Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality. Plant Methods, 15, 1–16. Sundari V, Anusree M, Swetha U. 2022. Crop recommendation and yield prediction using machine learning algorithms. World Journal of Advanced Research and Reviews, 14, 452–459. Svane S F, Jensen C S, Thorup-Kristensen K. 2019. Construction of a large-scale semi-field facility to study genotypic differences in deep root growth and resources acquisition. Plant Methods, 15, 1–16. Taranto F, Nicolia A, Pavan S, De Vita P, D’Agostino N. 2018. Biotechnological and digital revolution for climate-smart plant breeding. Agronomy, 8, 277. Tayade R, Yoon J, Lay L, Khan A L, Yoon Y, Kim Y. 2022. Utilization of spectral indices for high-throughput phenotyping. Plants, 11, 1712. Teshome F T, Bayabil H K, Hoogenboom G, Schaffer B, Singh A, Ampatzidis Y. 2023. Unmanned aerial vehicle (UAV) imaging and machine learning applications for plant phenotyping. Computers and Electronics in Agriculture, 212, 108064. Tian Z, Ma W, Yang Q, Duan F. 2022. Application status and challenges of machine vision in plant factory - A review. Information Processing in Agriculture, 9, 195–211. Tripodi P, Nicastro N, Pane C, Cammarano D. 2022. Digital applications and artificial intelligence in agriculture toward next-generation plant phenotyping. Crop and Pasture Science, 74, 597–614 Tsaftaris S A, Minervini M, Scharr H. 2016. Machine learning for plant phenotyping needs image processing. Trends in Plant Science, 21, 989–991. Tsaftaris S A, Scharr H. 2019. Sharing the right data right: A symbiosis with machine learning. Trends in Plant Science, 24, 99–102. Varshney R K. 2021. The plant genome special issue: Advances in genomic selection and application of machine learning in genomic prediction for crop improvement. The Plant Genome, 14, 3. Veeragandham S, Santhi H. 2020. A review on the role of machine learning in agriculture. Scalable Computing: Practice and Experience, 21, 583–589. Wang C, Caragea D, Kodadinne Narayana N, Hein N T, Bheemanahalli R, Somayanda I M, Jagadish S K. 2022. Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature. Plant Methods, 18, 9. Wasson A P, Chiu G S, Zwart A B, Binns T R. 2017. Differentiating wheat genotypes by Bayesian hierarchical nonlinear mixed modeling of wheat root density. Frontiers in Plant Science, 8, 282. Xu R, Li C. 2022. A review of high-throughput field phenotyping systems: Focusing on ground robots. Plant Phenomics, doi: 10.34133/2022/9760269. Xu Y. 2016. Envirotyping for deciphering environmental impacts on crop plants. Theoretical and Applied Genetics, 129, 653–673. Xu Y, Liu X, Fu J, Wang H, Wang J, Huang C, Prasanna B M, Olsen M S, Wang G, Zhang A. 2020. Enhancing genetic gain through genomic selection: From livestock to plants. Plant Communications, 1, doi: 10.1016/j.xplc.2019.100005. Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen M S, Varshney R K, Prasanna B M, Qian Q. 2022. Smart breeding driven by big data, artificial intelligence, and integrated genomic–enviromic prediction. Molecular Plant, 15, 1664–1695. Yang G, Liu J, Zhao C, Li Z, Huang Y, Yu H, Xu B, Yang X, Zhu D, Zhang X, Zhang R. 2017. Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives. Frontiers in plant Science, 8, doi: 10.3389/fpls.2017.01111. 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. Molecular Plant, 13, 187–214. Yang Y, Saand M A, Huang L, Abdelaal W B, Zhang J, Wu Y, Li J, Sirohi M H, Wang F. 2021. Applications of multi-omics technologies for crop improvement. Frontiers in Plant Science, 12, 563953. Yoosefzadeh-Najafabadi M, Hesami M, Eskandari M. 2023. Machine learning-assisted approaches in modernized plant breeding programs. Genes, 14, 777. York L M. 2019. Functional phenomics: An emerging field integrating high-throughput phenotyping, physiology, and bioinformatics. Journal of Experimental Botany, 70, 379–386. Younas M. 2019. Research challenges of big data. Service Oriented Computing and Applications, 13, 105–107. Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J. 2019. Crop phenomics: Current status and perspectives. Frontiers in Plant Science, 10, 714. |
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