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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