Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
The sowing pattern has an important impact on light interception efficiency in maize by determining the spatial distribution of leaves within the canopy. Leaves orientation is an important architectural trait determining maize canopies light interception. Previous studies have indicated how maize genotypes may adapt leaves orientation to avoid mutual shading with neighboring plants as a plastic response to intraspecific competition. The goal of the present study is 2-fold: firstly, to propose and validate an automatic algorithm (Automatic Leaf Azimuth Estimation from Midrib detection [ALAEM]) based on leaves midrib detection in vertical red green blue (RGB) images to describe leaves orientation at the canopy level; and secondly, to describe genotypic and environmental differences in leaves orientation in a panel of 5 maize hybrids sowing at 2 densities (6 and 12 plants.m−2) and 2 row spacing (0.4 and 0.8 m) over 2 different sites in southern France. The ALAEM algorithm was validated against in situ annotations of leaves orientation, showing a satisfactory agreement (root mean square [RMSE] error = 0.1, R2 = 0.35) in the proportion of leaves oriented perpendicular to rows direction across sowing patterns, genotypes, and sites. The results from ALAEM permitted to identify significant differences in leaves orientation associated to leaves intraspecific competition. In both experiments, a progressive increase in the proportion of leaves oriented perpendicular to the row is observed when the rectangularity of the sowing pattern increases from 1 (6 plants.m−2, 0.4 m row spacing) towards 8 (12 plants.m−2, 0.8 m row spacing). Significant differences among the 5 cultivars were found, with 2 hybrids exhibiting, systematically, a more plastic behavior with a significantly higher proportion of leaves oriented perpendicularly to avoid overlapping with neighbor plants at high rectangularity. Differences in leaves orientation were also found between experiments in a squared sowing pattern (6 plants.m−2, 0.4 m row spacing), indicating a possible contribution of illumination conditions inducing a preferential orientation toward east-west direction when intraspecific competition is low.
Rizzo G, Monzon JP, Tenorio FA, Howard R, Cassman KG, Grassini P. Climate and agronomy, not genetics, underpin recent maize yield gains in favorable environments. Proc Natl Acad Sci USA. 2022;119(4):e2113629119.
Berzsenyi Z, Tokatlidis IS. Density dependence rather than maturity determines hybrid selection in dryland maize production. Agron J. 2012;104(2):331–336.
Gonzalez VH, Tollenaar M, Bowman A, Good B, Lee EA. Maize yield potential and density tolerance. Crop Sci. 2018;58(2):472–485.
Fasoula VA, Fasoula DA. Principles underlying genetic improvement for high and stable crop yield potential. Field Crop Res. 2002;75(2–3):191–209.
Tetio-Kagho F, Gardner FP. Responses of maize to plant population density. II. Reproductive development, yield, and yield adjustments. Agron J. 1988;80(6):935–940.
Tokatlidis I, Has V, Melidis V, Has I, Mylonas I, Evgenidis G, Copandean A, Ninou E, Fasoula VA. Maize hybrids less dependent on high plant densities improve resource-use efficiency in rainfed and irrigated conditions. Field Crop Res. 2011;120(3):345–351.
Girardin P, Tollenaar M. Leaf azimuth in maize : Origin and effects on canopy patterns. Eur J Agron. 1992;1(4):227–233.
Ford ED, Cocke A, Horton L, Fellner M, Van Volkenburgh E. Estimation, variation and importance of leaf curvature in zea mays hybrids. Agric For Meteorol. 2008;148(10):1598–1610.
He L, Sun W, Chen X, Han L, Li J, Ma Y, Song Y. Modeling maize canopy morphology in response to increased plant density. Front Plant Sci. 2021;11:533514.
Perez RP, Fournier C, Cabrera-Bosquet L, Artzet S, Pradal C, Brichet N, Chen TW, Chapuis R, Welcker C, Tardieu F. Changes in the vertical distribution of leaf area enhanced light interception efficiency in maize over generations of selection. Plant Cell Environ. 2019;42(7):2105–2119.
Kusmec A, de Leon N, Schnable PS. Harnessing phenotypic plasticity to improve maize yields. Front Plant Sci. 2018;9:1377.
Drouet J-L, Moulia B. Spatial re-orientation of maize leaves affected by initial plant orientation and density. Agric For Meteorol. 1997;88(1):85–100.
Drouet J-L, Moulia BB, Bonhomme R. Do changes in the azimuthal distribution of maize leaves over time affect canopy light absorption? Agronomie. 1999;19(3–4):281–294.
Lopez-Lozano R, Frederic B, Chelle M, Rochdi N, España M. Sensitivity of gap fraction to maize architectural characteristics based on 4d model simulations. Agric For Meteorol. 2007;143:217–229.
Maddonni G, Otegui M, Andrieu B, Chelle M, Casal J. Maize leaves turn away from neighbors. Plant Physiol. 2002;130:1181–1189.
Ballaré CL, Sánchez RA, Scopel AL, Casal JJ, Ghersa CM. Early detection of neighbour plants by phytochrome perception of spectral changes in reflected sunlight. Plant Cell Environ. 1987;10(7):551–557.
Araus JL, Cairns JE. Field high-throughput phenotyping: The new crop breeding frontier. Trends Plant Sci. 2014;19(1):52–61.
Fiorani F, Schurr U. Future scenarios for plant phenotyping. Annu Rev Plant Biol. 2013;64(1):267–291.
Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M. Plant phenomics, from sensors to knowledge. Curr Biol. 2017;27(15):R770–R783.
Kitano BT, Mendes CCT, Geus AR, Oliveira HC, Souza JR. Corn plant counting using deep learning and uav images. IEEE Geosci Remote Sens Lett. 2019;1–5.
Velumani K, Lopez-Lozano R, Madec S, Guo W, Gillet J, Comar A, Baret F. Estimates of maize plant density from uav rgb images using faster-rcnn detection model: Impact of the spatial resolution. Plant Phenomics. 2021;2021:9824843.
Liu W-Y, Chang YM, Chen SCC, Lu CH, Wu YH, Lu MYJ, Chen DR, Shih ACC, Sheue CR, Huang HC, et al. Anatomical and transcriptional dynamics of maize embryonic leaves during seed germination. Proc Natl Acad Sci USA. 2013;110(10):3979–3984.
Baret F, Madec S, Irfan K, Lopez J, Comar A, Hemmerlé M, Dutartre D, Praud S, Tixier MH. Leaf-rolling in maize crops: From leaf scoring to canopy-level measurements for phenotyping. J Exp Bot. 2018;69(10):2705–2716.
Li Y, Wen W, Miao T, Wu S, Yu Z, Wang X, Guo X, Zhao C. Automatic organ-level point cloud segmentation of maize shoots by integrating high-throughput data acquisition and deep learning. Comput Electron Agric. 2022;193:106702.
Liu F, Song Q, Zhao J, Mao L, Bu H, Hu Y, Zhu XG. Canopy occupation volume as an indicator of canopy photosynthetic capacity. New Phytol. 2021;232(2):941–956.
Bleasdale JKA, Nelder JA. Plant population and crop yield. Nature. 1960;188:342–342.
Maddonni G, Chelle M, Drouet J-L, Andrieu B. Light interception of contrasting azimuth canopies under square and rectangular plant spatial distributions: Simulations and crop measurements. Field Crop Res. 2001;70(1):1–13.
Liu G, Liu W, Yang Y, Guo X, Zhang G, Li J, Xie R, Ming B, Wang K, Hou P, et al. Marginal superiority of maize: An indicator for density tolerance under high plant density. Sci Rep. 2020;10:15378.
Serouart M, Madec S, David E, Velumani K, Lopez Lozano R, Weiss M, Baret F. Segveg: Segmenting rgb images into green and senescent vegetation by combining deep and shallow methods. Plant Phenomics. 2022;2022.
Freeman H, Shapira R. Determining the minimum-area encasing rectangle for an arbitrary closed curve. Commun ACM. 1975;18(7):409–413.
Annicchiarico P. Additive main effects and multiplicative interaction (ammi) analysis of genotype-location interaction in variety trials repeated over years. Theor Appl Genet. 1997;94:1072–1077.
Valladares F, Sanchez-Gomez D, Zavala MA. Quantitative estimation of phenotypic plasticity: Bridging the gap between the evolutionary concept and its ecological applications. J Ecol. 2006;94(6):1103–1116.
Jimenez-Berni JA, Deery DM, Rozas-Larraondo P, Condon A(T)G, Rebetzke GJ, James RA, Bovill WD, Furbank RT, Sirault XRR. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with lidar. Front Plant Sci. 2018;9:237.
Jin S, Su Y, Gao S, Wu F, Ma Q, Xu K, Ma Q, Hu T, Liu J, Pang S, et al. Separating the structural components of maize for field phenotyping using terrestrial lidar data and deep convolutional neural networks. IEEE Trans Geosci Remote Sens. 2020;58(4):2644–2658.
Sinoquet H, Bonhomme R. Modélisation de l’interception des rayonnements solaires dans une culture en rangs. II: Structure géométrique du couvert et validation du modèle. Agronomie. 1989;9:619–628.
Distributed under a Creative Commons Attribution License (CC BY 4.0).