Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression

被引:45
作者
Adak, Alper [1 ]
Murray, Seth C. [1 ]
Bozinovic, Sofija [2 ]
Lindsey, Regan [1 ]
Nakasagga, Shakirah [1 ]
Chatterjee, Sumantra [1 ]
Anderson, Steven L., II [3 ]
Wilde, Scott [1 ]
机构
[1] Texas A&M Univ, Dept Soil & Crop Sci, College Stn, TX 77843 USA
[2] Maize Res Inst, Belgrade 11185, Serbia
[3] Univ Florida, Mid Florida Res & Educ Ctr, Inst Food & Agr Sci, Dept Environm Hort, Apopka, FL 32703 USA
关键词
high throughput phenotyping; unoccupied aerial system; temporal vegetation indices; nested design; machine learning regression; phenomic prediction and selection; GENOMIC SELECTION; RIDGE-REGRESSION; AUTOMATED CROP; CANOPY; REFLECTANCE; MODELS; IDENTIFICATION; EXTRACTION; SOIL; L;
D O I
10.3390/rs13112141
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = similar to 0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = similar to 0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.
引用
收藏
页数:21
相关论文
共 57 条
[1]  
Adak A., P 63 ANN MAIZ GEN M, P89
[2]   Validation of functional polymorphisms affecting maize plant height by unoccupied aerial systems discovers novel temporal phenotypes [J].
Adak, Alper ;
Conrad, Clarissa ;
Chen, Yuanyuan ;
Wilde, Scott C. ;
Murray, Seth C. ;
Anderson II, Steven L. ;
Subramanian, Nithya K. .
G3-GENES GENOMES GENETICS, 2021, 11 (06)
[3]   Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize [J].
Adak, Alper ;
Murray, Seth C. ;
Anderson, Steven L. ;
Popescu, Sorin C. ;
Malambo, Lonesome ;
Romay, M. Cinta ;
de Leon, Natalia .
PLANT GENOME, 2021, 14 (02)
[4]   Use of Hyperspectral Image Data Outperforms Vegetation Indices in Prediction of Maize Yield [J].
Aguate, Fernando M. ;
Trachsel, Samuel ;
Gonzalez Perez, Lorena ;
Burgueno, Juan ;
Crossa, Jose ;
Balzarini, Monica ;
Gouache, David ;
Bogard, Matthieu ;
de los Campos, Gustavo .
CROP SCIENCE, 2017, 57 (05) :2517-2524
[5]  
Anderson S.L., 2019, Plant phenome j, V2, P1, DOI [DOI 10.2135/TPPJ2019.02.0004, 10.2135/tppj2019.02.0004]
[6]   Unoccupied aerial system enabled functional modeling of maize height reveals dynamic expression of loci [J].
Anderson, Steven L. I. I. I. I. ;
Murray, Seth C. ;
Chen, Yuanyuan ;
Malambo, Lonesome ;
Chang, Anjin ;
Popescu, Sorin ;
Cope, Dale ;
Jung, Jinha .
PLANT DIRECT, 2020, 4 (05)
[7]   R/UAStools::plotshpcreate: Create Multi-Polygon Shapefiles for Extraction of Research Plot Scale Agriculture Remote Sensing Data [J].
Anderson, Steven L., II ;
Murray, Seth C. .
FRONTIERS IN PLANT SCIENCE, 2020, 11
[8]  
[Anonymous], 2018, PLANT PHENOME J, DOI 10.2135/tppj2017.08.0006
[9]  
[Anonymous], 2016, CLOUDCOMPARE POINT C
[10]   Fitting Linear Mixed-Effects Models Using lme4 [J].
Bates, Douglas ;
Maechler, Martin ;
Bolker, Benjamin M. ;
Walker, Steven C. .
JOURNAL OF STATISTICAL SOFTWARE, 2015, 67 (01) :1-48