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The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource competition. To address this issue, we propose a 3D maize modeling method based on computational intelligence. An initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture. The subsequent model considers the 3D phytomers of maize as intelligent agents. The aim is to maximize the ratio of sunlit leaf area, and by iteratively modifying the azimuth angle of the 3D phytomers, a 3D maize canopy model that maximizes light resource interception can be constructed. Additionally, the method incorporates a reflective approach to optimize the canopy and utilizes a mesh deformation technique for detecting and responding to leaf collisions within the canopy. Six canopy models of 2 varieties plus 3 planting densities was constructed for validation. The average R2 of the difference in azimuth angle between adjacent leaves is 0.71, with a canopy coverage error range of 7% to 17%. Another 3D maize canopy model constructed using 12 distinct density gradients demonstrates the proportion of leaves perpendicular to the row direction increases along with the density. The proportion of these leaves steadily increased after 9 × 104 plants ha−1. This study presents a 3D modeling method for the maize canopy. It is a beneficial exploration of swarm intelligence on crops and generates a new way for exploring efficient resources utilization of crop canopies.
Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J. Crop Phenomics: Current status and perspectives. Front Plant Sci. 2019;10:16.
Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J. Crop Phenomics and high-throughput phenotyping: Past decades, current challenges, and future perspectives. Mol Plant. 2020;13(2):187–214.
Vos J, Evers JB, Buck-Sorlin GH, Andrieu B, Chelle M, de Visser PHB. Functional–structural plant modelling: A new versatile tool in crop science. J Exp Bot. 2010;61(8):2101–2115.
Louarn G, Song Y. Two decades of functional–structural plant modelling: Now addressing fundamental questions in systems biology and predictive ecology. Ann Bot. 2020;126(4):501–509.
Tian J, Wang C, Xia J, Wu L, Xu G, Wu W, Li D, Qin W, Han X, Chen Q, et al. Teosinte ligule allele narrows plant architecture and enhances high-density maize yields. Science. 2019;365(6454):658–664.
Li RF, Zhang GQ, Xie RZ, Hou P, Ming B, Xue J, Wang KR, Li SK. Optimizing row spacing increased radiation use efficiency and yield of maize. Agron J. 2021;113(6):4806–4818.
Zhang XY, Xue J, Tian M, Zhang G, Ming B, Wang K, Hou P, Xie R, Tang Q, Li S. Maize lodging resistance with plastic film removal, increased planting density, and cultivars with different maturity periods. Plants. 2022;11(20):2723.
Slattery RA, Ort DR. Perspectives on improving light distribution and light use efficiency in crop canopies. Plant Physiol. 2021;185(1):34–48.
Wen WL, Zhao C, Guo X, Wang Y, Du J, Yu Z. Construction method of three-dimensional model of maize colony based on t-distribution function. Trans Chinese Soc of Agric Eng. 2018;34(04):192–200.
Okura F. 3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping. Breed Sci. 2022;72(1):31–47.
Zang J, Jin S, Zhang S, Li Q, Mu Y, Li Z, Li S, Wang X, Su Y, Jiang D. Field-measured canopy height may not be as accurate and heritable as believed: Evidence from advanced 3D sensing. Plant Methods. 2023;19(1):39.
Escamilla DM, Huang M, McHale L, Wang DC, Diers B, Xavier A, Rainey KM. Canopy coverage phenotyping and field spatial variability adjustment as an efficient selection tool in soybean breeding. Crop Sci. 2023;63(6):3277–3291.
Li Y, Wen W, Guo X, Yu Z, Gu S, Yan H, Zhao C. High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network. PLoS One. 2021;16(1):e0241528.
Fan J, Li Y, Yu S, Gou W, Guo X, Zhao C. Application of internet of things to agriculture – The LQ-FieldPheno platform: A high-throughput platform for obtaining crop phenotypes in field. Research. 2023;6:0059.
Li Y, Wen W, Fan J, Gou W, Gu S, Lu X, Yu Z, Wang X, Guo X. Multi-source data fusion improves time-series phenotype accuracy in maize under a field high-throughput phenotyping platform. Plant Phenomics. 2023;5:0043.
Xiao S, Ye Y, Fei S, Chen H, Zhang B, Li Q, Cai Z, Che Y, Wang Q, Ghafoor A, et al. High-throughput calculation of organ-scale traits with reconstructed accurate 3D canopy structures using a UAV RGB camera with an advanced cross-circling oblique route. ISPRS J Photogramm Remote Sens. 2023;201:104–122.
Wen W, Guo X, Li B, Wang C, Wang Y, Yu Z, Wu S, Fan J, Gu S, Lu X. Estimating canopy gap fraction and diffuse light interception in 3D maize canopy using hierarchical hemispheres. Agric For Meteorol. 2019;276-277:107594.
Zhu B, Liu F, Xie Z, Guo Y, Li B, Ma Y. Quantification of light interception within image-based 3-D reconstruction of sole and intercropped canopies over the entire growth season. Ann Bot. 2020;126(4):701–712.
Guo Y, Ma Y, Zhan Z, Li B, Dingkuhn M, Luquet D, De Reffye P. Parameter optimization and field validation of the functional-structural model GREENLAB for maize. Ann Bot. 2006;97(2):217–230.
Xiao S, Fei S, Li Q, Zhang B, Chen H, Xu D, Cai Z, Bi K, Guo Y, Li B, et al. The importance of using realistic 3D canopy models to calculate light interception in the field. Plant Phenomics. 2023;5:0082.
Graña M, Wozniak M, Rios S, de Lope J. Computational intelligence in remote sensing: An editorial. Sensors. 2020;20(3):633.
Da Silva AC Jr, Sant'Anna IC, Silva GN, Cruz CD, Nascimento M, Lopes LB, Soares PC. Computational intelligence to study the importance of characteristics in flood-irrigated rice. Acta Sci Agron. 2023;45(1):e57209.
Wen W, Wang Y, Wu S, Liu K, Gu S, Guo X. 3D phytomer-based geometric modelling method for plants–The case of maize. AoB Plants. 2021;13(5):plab055.
Liu X, Gu S, Wen W, Lu X, Jin Y, Zhang Y, Guo X. Disentangling the heterosis in biomass production and radiation use efficiency in maize: A phytomer-based 3D modelling approach. Plant. 2023;12(6):1229.
Wu S, Wen W, Wang Y, Fan J, Wang C, Gou W, Guo X. MVS-Pheno: A portable and low-cost phenotyping platform for maize shoots using multiview stereo 3D reconstruction. Plant Phenomics. 2020;2020:1848437.
Wang Y, Wen W, Wu S, Wang C, Yu Z, Guo X, Zhao C. Maize plant phenotyping: Comparing 3D laser scanning, multi-view stereo reconstruction, and 3D digitizing estimates. Remote Sens. 2019;11(1):63.
Fang X-R, Gao J-F, Xie C-Q, Zhu F-L, Huang L-X, He Y. Review of crop canopy spectral information detection technology and methods. Guang Pu Xue Yu Guang Pu Fen Xi. 2015;35(7):1949–1955.
Tao W, Gao S, Yuan Y. Boundary crossing: An experimental study of individual perceptions toward AIGC. Front Psychol. 2023;14:1185880.
Zhao X, de Reffye P, Xiong FL, Hu BG, Zhan ZG. Dual-scale automaton model for virtual plant development. Chin J Comput. 2001;24(60):608–617.
Vitsas N, Evangelou I, Papaioannou G, Gkaravelis A. Parallel transformation of bounding volume hierarchies into oriented bounding box trees. Comput Graph Forum. 2023;42(2):245–254.
Lysenko M. Fourier collision detection. Int J Robot Res. 2013;32(4):483–503.
Klein J, Zachmann G. Point cloud collision detection. Comput Graph Forum. 2004;23(3):567–576.
Serouart M, Lopez-Lozano R, Daubige G, Baumont M, Escale B, De Solan B, Baret F. Analyzing changes in maize leaves orientation due to GxExM using an automatic method from RGB images. Plant Phenomics. 2023;5:0046.
Qian B, Huang W, Xie D, Ye H, Guo A, Pan Y, Jin Y, Xie Q, Jiao Q, Zhang B, et al. Coupled maize model: A 4D maize growth model based on growing degree days. Comput Electron Agric. 2023;212:108124.
Verger A, Baret F, Weiss M, Filella I, Peñuelas J. GEOCLIM: A global climatology of LAI, FAPAR, and FCOVER from VEGETATION observations for 1999-2010. Remote Sens Environ. 2015;166:126–137.
Baret F, Hagolle O, Geiger B, Bicheron P, Miras B, Huc M, Berthelot B, Nino F, Weiss M, Samain O, et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm. Remote Sens Environ. 2007;110(3):275–286.
Wang F, Xue J, Xie R, Ming B, Wang K, Hou P, Zhang L, Li S. Assessing growth and water productivity for drip-irrigated maize under high plant density in arid to semi-humid climates. Agriculture. 2022;12(1):97.
Cai Z, Jiang S, Dong J, Tang S. An artificial plant community algorithm for the accurate range-free positioning of wireless sensor networks. Sensors. 2023;23(5):2804.
Zlobin Y, Kovalenko I, Klymenko H, Kyrylchuk K, Bondarieva L, Tykhonova O, Zubtsova I. Vitality analysis algorithm in the study of plant individuals and populations. Open Agric J. 2021;15:119–129.
Wang J, Zhang P, Song H, Bei J, Zhang H, Sun W, Sun X. A carnivorous plant algorithm with heuristic decoding method for traveling salesman problem. IEEE Access. 2022;10:97142–97164.
Song Q, Liu F, Bu H, Zhu X-G. Quantifying contributions of different factors to canopy photosynthesis in 2 Maize varieties: Development of a novel 3D canopy modeling pipeline. Plant Phenomics. 2023;5:0075.
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