An algorithm for estimating field wheat canopy light interception based on Digital Plant Phenotyping Platform

doi:10.12133/j.smartag.2020.2.1.202002-SA004

Abstract

Abstract:

The capacity of canopy light interception is a key functional trait to distinguish the phenotypic variation over genotypes. High-throughput phenotyping canopy light interception in the field, therefore, would be of high interests for breeders to increase the efficiency of crop improvement. In this research, the Digital Plant Phenotyping Platform（D3P） was used to conduct in-silico phenotyping experiment with LiDAR scans over a wheat field. In this experiment virtual 3D wheat canopies were generated over 100 wheat genotypes for 5 growth stages, representing wide range of canopy structural variation. Accordingly, the actual value of traits targeted were calculated including GAI (green area index), AIA (average inclination angle) and FIPAR_dif (the fraction of intercepted diffuse photosynthetically activate radiation). Then, virtual LiDAR scanning were accomplished over all the treatments and exported as 3D point cloud. Two types of features were extracted from point cloud, including height quantiles (H) and green fractions (GF). Finally, an artificial neural network was trained to predict the traits targeted from different combinations of LiDAR features. Results show that the prediction accuracy varies with the selection of input features, following the rank as GF + H > H > GF. Regarding the three traits, we achieved satisfactory accuracy for FIPAR_dif (R²=0.95) and GAI (R²=0.98) but not for AIA (R²=0.20). This highlights the importance of H feature with respect to the prediction accuracy. The results achieved here are based on in-silico experiments, further evaluation with field measurement would be necessary. Nontheless, as proof of concept, this work further demonstrates that D3P could greatly facilitate the algorithm development. Morever, it highlights the potential of LiDAR measurement in the high-throuhgput phenopyting of canopy light interpcetion and structural traits in the field.

Key words: canopy light interception, high-throughput phenotyoing, LiDAR, Digital Plant Phenotyping Platform (D3P), wheat canopy

CLC Number:

TP391.4

Liu Shouyang, Jin Shichao, Guo Qinghua, Zhu Yan, Baret Fred. An algorithm for estimating field wheat canopy light interception based on Digital Plant Phenotyping Platform[J]. Smart Agriculture, 2020, 2(1): 87-98.

References

1	Godfray H C, Beddington J R, Crute I R, et al. Food security: The challenge of feeding 9 billion people[J]. Science, 2010, 327(5967): 812-818.
2	Liu B, Asseng S, Müller C, et al. Similar estimates of temperature impacts on global wheat yield by three independent methods[J]. Nature Climate Change, 2016, 6(12): 1130-1136.
3	Araus J L, Cairns J E. Field high-throughput phenotyping: the new crop breeding frontier[J]. Trends in Plant Science, 2014, 19(1): 52-61.
4	Furbank R T, Tester M. Phenomics-technologies to relieve the phenotyping bottleneck[J]. Trends in Plant Science, 2011, 16(12): 635-644.
5	Chapman S C, Zheng B, Potgieter A, et al. Visible, infrarednear, and thermal spectral radiance on-board UAVs for high-throughput phenotyping of plant breeding trials[M]. Thenkabail P S, Lyon J G, Huete A. Biophysical and biochemical characterization and plant species studies. CRC Press, 2018: 275-298.
6	Martre P, Quilot-Turion B, Luquet B, et al. Model-assisted phenotyping and ideotype design[M]. Victor O S, Calderini D F. Crop Physiology. Academic Press, 2015: 349-373.
7	Tardieu F, Cabrera-Bosquet L, Pridmore T, et al. Plant phenomics, from sensors to knowledge[J]. Current Biology, 2017, 27(15): 770-783.
8	Monteith J L. Climate and the efficiency of crop production in Britain[J]. Philosophical Transactions of the Royal Society of London Series B： Biological Sciences, 1977, 281(980): 277-294.
9	Monteith J L. Validity of the correlation between intercepted radiation and biomass[J]. Agricultural and Forest Meteorology, 1994, 68(3-4): 213-220.
10	Parry M A, Reynolds M, Salvucci M E, et al. Raising yield potential of wheat II. increasing photosynthetic capacity and efficiency[J]. Journal of Experimental Botany, 2010, 62(2): 453-467.
11	Furbank R T, Jimenez-Berni J A, George-Jaeggli B, et al. Field crop phenomics: Enabling breeding for radiation use efficiency and biomass in cereal crops[J]. New Phytologist, 2019, 223(4): 1714-1727.
12	Su Y, Wu F, Zurui A, et al. Evaluating maize phenotype dynamics under drought stress using terrestrial lidar[J]. Plant Methods, 2019, 15(1): 11-26.
13	Cabrera-Bosquet L, Fournier C, Brichet N, et al. High throughput estimation of incident light, light interception and radiation use efficiency of thousands of plants in a phenotyping platform[J]. New Phytologist, 2016, 212(1): 269-281.
14	Weiss M, Baret F. fAPAR (fraction of Absorbed Photosynthetically Active Radiation) estimates at various scale[C]// 34th International Symposium on Remote Sensing of Environment, 2011.
15	Baret F, Madec S, Irfan K, et al. Leaf-rolling in maize crops: From leaf scoring to canopy-level measurements for phenotyping[J]. Journal of Experimental Botany, 2018, 69(10): 2705-2716.
16	Liu S, Baret F, Abichou M, et al. Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model[J]. Agricultural and Forest Meteorology, 2017, 247: 12-20.
17	Jimenez-Berni J A, Deery D M, PabloRozas-Larraondo, et al. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR[J]. Frontiers in Plant Science, 2018, 9: no. 237.
18	Jin S, Su Y, Gao S， et al. Deep learning: Individual maize segmentation from terrestrial lidar data using faster R-CNN and regional growth algorithms[J]. Frontiers in Plant Science, 2018, 9: no. 866.
19	Jin S, Su Y, Wu F, et al. Stem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(3): 1336-1346.
20	Lin Y. LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics?[J]. Computers and Electronics in Agriculture, 2015, 119: 61-73.
21	Campbell G S, Norman J M. An Introduction to Environmental Biophysics[M]. New York: Springer, 1998.
22	Zhao K, García M, Liu S, et al. Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution[J]. Agricultural and Forest Meteorology, 2015, 209-210: 100-113.
23	Liu S, Martre P, Buis S, et al. Estimation of plant and canopy architectural traits using the D3P Digital Plant Phenotyping Platform[J]. Plant Physiology, 2019, 181(3): 881-890.
24	Godin C, Sinoquet H. Functional-structural plant modelling[J]. New phytologist, 2005, 166(3): 705-708.
25	Vos J, Evers B, Buck-Sorlin H, et al. Functional-structural plant modelling: A new versatile tool in crop science[J]. Journal of Experimental Botany, 2010, 61(8): 2101-2115.
26	Pradal C, Dufour-Kowalski S, Boudon F, et al. OpenAlea: A visual programming and component-based software platform for plant modelling[J]. Functional Plant Biology, 2008, 35(10): 751-760.
27	Fournier C, Andrieu B, Ljutovac S, et al. ADEL-Wheat: A 3D architectural model of wheat development[C]// International Symposium on Plant Growth Modeling, Simulation, Visualization and their Applications (PMA03). 2003: 54-63.
28	Chelle M, Andrieu B. The nested radiosity model for the distribution of light within plant canopies[J]. Ecological Modelling, 1998, 111(1): 75-91.
29	Andrieu B, Baret F. Indirect methods of estimating crop structure from optical measurements, crop structure and light microclimate[M]. Crop Structure and Light Microclimate: Characterization and Application. Paris: INRA, 1993: 285-322.
30	Chen J M, Black T Defining leaf area index for non-flat leaves[J]. Plant, Cell & Environment, 1992, 15(4): 421-429.
31	Lang A R G. Application of some of Cauchy's theorems to estimation of surface areas of leaves, needles and branches of plants, and light transmittance[J]. Agricultural and Forest Meteorology, 1991, 55(3): 191-212.
32	Baret F, Buis S. Estimating canopy characteristics from remote sensing observations: Review of methods and associated problems[M]. Liang S. Advances in Land Remote Sensing. Dordrecht: Springer, 2008: 173-201.
33	Zhao K, Popescu S, Meng X, et al. Characterizing forest canopy structure with lidar composite metrics and machine learning[J]. Remote Sensing of Environment, 2011, 115(8): 1978-1996.
34	Abichou M, de Solan B, Andrieu B. Architectural Response of wheat cultivars to row spacing reveals altered perception of plant density[J]. Frontiers in Plant Science, 2019, 10: 999.
35	Baret F, Hagolle O, Geiger B, et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm[J]. Remote Sensing of Environment, 2007, 110(3): 275-286.
36	Bacour C, Baret F, Béal D, et al. Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data: Principles and validation[J]. Remote Sensing of Environment, 2006, 105(4): 313-325.
37	Martre P, Dambreville A. A model of leaf coordination to scale-up leaf expansion from the organ to the canopy[J]. Plant Physiology, 2018, 176(1): 704-716.
38	Liu S, Baret F, Andrieu B, et al. Modeling the spatial distribution of plants on the row for wheat crops: Consequences on the green fraction at the canopy level[J]. Computers and Electronics in Agriculture, 2017, 136: 147-156.
39	Sinclair R, Hone T. Leaf nitrogen, photosynthesis, and crop radiation use efficiency: A review[J]. Crop Science, 1989, 29: 90-98.
40	Baret F, de Solan B, Lopez-Lozano R, et al. GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops[J]. Agricultural and Forest Meteorology, 2010, 150(11): 1393-1401.
41	Hartmann A, Czauderna T, Hoffmann R, et al. HTPheno: An image analysis pipeline for high-throughput plant phenotyping[J]. BMC bioinformatics, 2011, 12(1): 148.
42	Donald C M. The breeding of crop ideotypes[J]. Euphytica, 1967, 17(3): 385-403.
43	Jin X, Madec S, Dutartre D, et al. High-throughput measurements of stem characteristics to estimate ear density and above-ground biomass[J]. Plant Phenomics, 2019： no. 4820305.
44	Madec S, Jin X, Lu H, et al. Ear density estimation from high resolution RGB imagery using deep learning technique[J]. Agricultural and Forest Meteorology, 2019， 264: 225-234.
45	Pound M P, Atkinson J A, Townsend A J, et al. Deep machine learning provides state-of-the-art performance in image-based plant phenotyping[J]. Gigascience, 2017, 6(10): 1-10.
46	Taghavi Namin S, Esmaeilzadeh M, Najafi M, et al. Deep phenotyping: Deep learning for temporal phenotype/genotype classification[J]. Plant Methods, 2018, 14(1): no. 66.
47	Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]// International Conference on Neural Information Processing Systems, 2014: 2672-2680.
48	Qi C, Yi L, Su H, et al. PointNet++: Deep hierarchical feature learning on point sets in a metric space[M]. Bach F R, Jordan M. Advances in neural information processing systems, 2017: 5099-5108.
49	Jin S, Su Y, Gao S, et al. Separating the structural components of maize for field phenotyping using terrestrial lidar data and deep convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4): 2644-2658.
50	Han J, Jentzen A, Weinan E. Solving high-dimensional partial differential equations using deep learning[J]. Proceedings of the National Academy of Sciences, 2018, 115(34): 8505-8510.

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