Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Materials and Methods
Experimental Site
Results
Discussion
Implications for High-Throughput Phenotyping
Conclusion
References
All Topics
Psychology

Color as a Language in Pre-school Facilities

Profile image of Aleksandar KekovicAleksandar Kekovic

2019, Architecture. Construction. Education

https://doi.org/10.18503/2309-7434-2019-1(13)-26-31
visibility

…

description

13 pages

link

1 file

Abstract

Fu et al. Ensemble Approach to Phenotyping Photosynthesis with increases in R 2 of 0.1 (0.08) and decreases in RMSE by 4.1 (6.6) µmol m −2 s −1 , equal to 8% (15%) reduction in RMSE. Better predictive performance of the regression stacking is likely attributed to the varying coefficients (or weights) in the level-2 model (the LASSO model) and the diverse ability of each individual regression technique to utilize spectral information for the best modeling performance. Further refinements can be made to apply this stacked regression technique to other plant phenotypic traits.

Related papers

Machine Learning Techniques for Predicting Crop Photosynthetic Capacity from Leaf Reflectance Spectra
sarthak agarwal

Molecular plant, 2017

Harnessing natural variation in photosynthetic capacity is a promising route toward yield increases, but physiological phenotyping is still too laborious for large-scale genetic screens. Here, we evaluate the potential of leaf reflectance spectroscopy to predict parameters of photosynthetic capacity in Brassica oleracea and Zea mays, a C3 and a C4 crop, respectively. To this end, we systematically evaluated properties of reflectance spectra and found that they are surprisingly similar over a wide range of species. We assessed the performance of a wide range of machine learning methods and selected recursive feature elimination on untransformed spectra followed by partial least squares regression as the preferred algorithm that yielded the highest predictive power. Learning curves of this algorithm suggest optimal species-specific sample sizes. Using the Brassica relative Moricandia, we evaluated the model transferability between species and found that cross-species performance canno...

View PDFchevron_right
Plant Phenomics: Fundamental Bases, Software and Hardware Platforms, and Machine Learning
Galina Smolikova

Russian Journal of Plant Physiology, 2020

In recent years, a new branch of plant physiology, plant phenomics, which focuses on identifying patterns of organization and changes in plant Phenomes, i.e., physical and biochemical characteristics, considered as a set of phenotypes of a plant organism, has emerged. Phenomics is a postgenomic discipline that actively uses the achievements of the genomic era and bioinformatics. It supplements them with standardized and statistically significant factual material on phenotypes with a high degree of detail. The technique of obtaining and analyzing information about phenotypes in phenomics is called phenotyping. High-performance phenotyping, providing digital automated analysis of large data samples, has become widespread. Recent progress in high-performance phenotyping has been associated with the development of image registration systems in various spectral regions, approaches to cultivating plant objects under standardized conditions, sensory technologies, robotics, and methods for data processing and analysis, such as computer vision and machine learning (artificial neural network). Phenomics technologies have a high information content analysis, surpassing human capabilities, performing measurements in the hyperspectral range using X-ray tomography and ultra-precise "thermal" images, and have a number of other low-invasive and precision approaches. Arrays of data obtained using phenomics technologies are recorded and processed automatically and are free from the problems of subjective assessment and inadequate statistical processing. It is assumed that phenotyping will allow for the creation of digital models of the vital activity processes and the "formation" of plant productivity at the organism level in connection with the dynamics of transcriptomes, proteomes, and metabolomes. Phenomics helps researchers transform a large amount of information received from modern sensors into new knowledge using computer data processing and modeling, reducing the distance from basic science to the practical application of results in crop production and breeding. Phenotyping is actively developing both in laboratory and in greenhouse conditions as well as on open agricultural sites, forests, and in real natural phytocenoses. The review analyzes the current state of plant phenomics with a focus on technical aspects, in particular, the design of hardware-software phenotyping complexes, i.e., phenomics platforms, as well as the use of neural networks in phenotyping of plant organisms.

View PDFchevron_right
Phenotypic selection for photosynthetic capacity and efficiency
Robert Furbank
View PDFchevron_right
Trait correlation networks: a whole-plant perspective on the recently criticized leaf economic spectrum
Hendrik Poorter

New Phytologist, 2014

View PDFchevron_right
Gradient analysis, the next generation: towards more plant-relevant explanatory variables
Todd Lookingbill

Canadian Journal of Forest Research, 2005

The long history of gradient analysis is anchored in the observation that species turnover can be described along elevation gradients. This model is unsatisfying in that elevation is not directly relevant to plants and the ubiquitous "elevation gradient" is composed of multiple intertwined environmental factors. We offer an approach to landscapescale vegetation analysis that disentangles the elevation gradient into its constituent parts through focused field sampling and statistical analysis. We illustrate the approach for an old-growth watershed in the Oregon Western Cascades. Our initial model of this system supports the common observation that forest community types are highly associated with specific elevation bands. By replacing elevation and other crude environmental proxy variables with estimates of more direct and resource gradients (radiation, temperature, and soil moisture), we create a vegetative model with stronger explanatory power than the proxy model in both cross-validation analysis and validation using an independent data set. The resulting model is also more biologically interpretable, which provides more meaningful insight into potential forest response to environmental change (e.g., global climate change scenarios). Acquiring a better mechanistic understanding of the relationship between plant communities and environmental predictor variables presents the next great challenge to community ecologists conducting gradient studies at landscape scales.

View PDFchevron_right
Predictive Models of Chlorophyll Content in Sugarcane Seedlings Using Spectral Images
Bárbara Teruel

Engenharia Agrícola, 2021

Chlorophyll content is a widely used parameter for nutritional status diagnosis in sugarcane. This study aimed to develop a predictive model of chlorophyll content in sugarcane seedlings using spectral imagery analysis within the electromagnetic spectrum visible range. The experiment was carried out in a split-plot design, with two fertilization rates and three sugarcane cultivars. For chlorophyll analysis, 144 leaves were collected from seedlings. Chlorophyll contents were extracted and measured by SPAD-502 meter. Spectral images within the range of 480 to 710 nm were analyzed using reflectance, absorbance (white source), and fluorescence (source at 405 and 470 nm) responses. Predictive models were developed using multivariate regression methods such as Principal Component Regression and Partial Least Squares Regression. We chose the best model through absorbance response using variable selection and the PLSR method (R2P = 0.718 and RMSEP = 7.665). The wavelengths of 480, 490, 500, 600, 630, and 640 nm were identified as the best for total chlorophyll content determination. The spectral image processing-based method can provide a chlorophyll measurement equivalent to SPAD, with the advantage of having a higher spatial coverage over the entire leaf area. Besides, it can also support automation of the chlorophyll measurement in greenhouses.

View PDFchevron_right
Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency
Jeremy Harbinson

The Plant Journal, 2018

In recent years developments in plant phenomic approaches and facilities have gradually caught up with genomic approaches. An opportunity lies ahead to dissect complex, quantitative traits when both genotype and phenotype can be assessed at a high level of detail. This is especially true for the study of natural variation in photosynthetic efficiency, for which forward genetics studies have yielded only a little progress in our understanding of the genetic layout of the trait. High-throughput phenotyping, primarily from chlorophyll fluorescence imaging, should help to dissect the genetics of photosynthesis at the different levels of both plant physiology and development. Specific emphasis should be directed towards understanding the acclimation of the photosynthetic machinery in fluctuating environments, which may be crucial for the identification of genetic variation for relevant traits in food crops. Facilities should preferably be designed to accommodate phenotyping of photosynthesis-related traits in such environments. The use of forward genetics to study the genetic architecture of photosynthesis is likely to lead to the discovery of novel traits and/or genes that may be targeted in breeding or bio-engineering approaches to improve crop photosynthetic efficiency. In the near future, big data approaches will play a pivotal role in data processing and streamlining the phenotype-to-gene identification pipeline.

View PDFchevron_right
Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data
Eric Sanderson

Remote Sensing of Environment, 1996

This study examined the use of stepwise multiple linear regression to quantify leaf carbon, nitrogen, lignin, cellulose, dry weight, and water compositions from leaf level reflectance (R). Two fresh leaf and one dry leaf datasets containing a broad range of native and cultivated plant species were examined using unconstrained stepwise multiple linear regression and constrained regression with wavelengths reported from other leaf level studies and wavelengths derived from chemical spectroscopy. Although stepwise multiple linear regression explained large amounts of the variation in the chemical data, the bands selected were not related to known absorption bands, varied among datasets and expression bases for the chemical [concentration (g g-1) or content (g m-2)], did not correspond to bands selected in other studies, and were sensitive to the samples entered into the regression. Stepwise multiple regression using artificially constructed datasets that randomized the association between nitrogen concentration and reflectance spectra produced coefficients of determination (R2"s) between 0.41 and 0.82 for first and second derivative log(1/R) spectra. The R2"s for correctly-paired nitrogen data and first and second derivative log(1/R) only exceeded the average randomized R2"s by O. 02-0.42. Replication of this randomization experiment on a larger dry ground leaf data set from the Harvard Forest showed the same trends but lower R2"s.

View PDFchevron_right
GiNA, an Efficient and High-Throughput Software for Horticultural Phenotyping
GIOVANNY E COVARRUBIAS PAZARAN

Traditional methods for trait phenotyping have been a bottleneck for research in many crop species due to their intensive labor, high cost, complex implementation, lack of reproducibil-ity and propensity to subjective bias. Recently, multiple high-throughput phenotyping platforms have been developed, but most of them are expensive, species-dependent, complex to use, and available only for major crops. To overcome such limitations, we present the open-source software GiNA, which is a simple and free tool for measuring horticultural traits such as shape-and color-related parameters of fruits, vegetables, and seeds. GiNA is multi-platform software available in both R and MATLAB 1 programming languages and uses conventional images from digital cameras with minimal requirements. It can process up to 11 different horticultural morphological traits such as length, width, two-dimensional area, volume , projected skin, surface area, RGB color, among other parameters. Different validation tests produced highly consistent results under different lighting conditions and camera setups making GiNA a very reliable platform for high-throughput phenotyping. In addition, five-fold cross validation between manually generated and GiNA measurements for length and width in cranberry fruits were 0.97 and 0.92. In addition, the same strategy yielded prediction accuracies above 0.83 for color estimates produced from images of cranberries analyzed with GiNA compared to total anthocyanin content (TAcy) of the same fruits measured with the standard methodology of the industry. Our platform provides a scalable, easy-to-use and affordable tool for massive acquisition of phenotypic data of fruits, seeds, and vegetables.

View PDFchevron_right
Fitting net photosynthetic light-response curves with Microsoft Excel — a critical look at the models
George Vourlitis

Photosynthetica, 2013

In this study, we presented the most commonly employed net photosynthetic light-response curves (P N /I curves) fitted by the Solver function of Microsoft Excel. Excel is attractive not only due to its wide availability as a part of the Microsoft Office suite but also due to the increased level of familiarity of undergraduate students with this tool as opposed to other statistical packages. In this study, we explored the use of Excel as a didactic tool which was built upon a previously published paper presenting an Excel Solver tool for calculation of a net photosynthetic/chloroplastic CO 2-response curve. Using the Excel spreadsheets accompanying this paper, researchers and students can quickly and easily choose the best fitted P N /I curve, selecting it by the minimal value of the sum of the squares of the errors. We also criticized the misuse of the asymptotic estimate of the maximum gross photosynthetic rate, the light saturation point estimated at a specific percentile of maximum net photosynthetic rate, and the quantum yield at zero photosynthetic photon flux density and we proposed the replacement of these variables by others more directly linked to plant ecophysiology.

View PDFchevron_right

References (83)

  1. Ainsworth, E. A., Serbin, S. P., Skoneczka, J. A., and Townsend, P. A. (2014). Using leaf optical properties to detect ozone effects on foliar biochemistry. Photosynth. Res. 119, 65-76. doi: 10.1007/s11120-013-9837-y
  2. Ali, I., Cawkwell, F., Dwyer, E., Barrett, B., and Green, S. (2016). Satellite remote sensing of grasslands: from observation to management. J. Plant Ecol. 9, 649-671. doi: 10.1093/jpe/rtw005
  3. Behmann, J., Steinrücken, J., and Plümer, L. (2014). Detection of early plant stress responses in hyperspectral images. ISPRS J. Photogramm. Remote Sens. 93, 98-111. doi: 10.1016/j.isprsjprs.2014.03.016
  4. Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2006). Greedy layer-wise training of deep networks. Proceedings of the 19th International Conference on Neural Information Processing Systems, Canada.
  5. Bernacchi, C. J., Pimentel, C., and Long, S. P. (2003). In vivo temperature response functions of parameters required to model RuBP-limited photosynthesis. Plant Cell Environ. 26, 1419-1430. doi: 10.1046/j.0016-8025.2003.01050.x
  6. Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Portis, A. R. Jr., and Long, S. P. (2001). Improved temperature response functions for models of rubisco-limited photosynthesis. Plant Cell Environ. 24, 253-259. doi: 10.1111/j.1365-3040.2001.00668.x
  7. Boyd, S., Parikh, N., Chu, E., Peleato, B., and Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends R Mach. Learn. 3, 1-122. doi: 10.1561/2200000016
  8. Breiman, L. (1996). Stacked regressions. Mach. Learn. 24, 49-64. doi: 10.1007/ BF00117832
  9. Breiman, L. (2001). Random forests. Mach. Learn. 45, 5-32. doi: 10.1023/A: 1010933404324
  10. Brereton, R. G., and Lloyd, G. R. (2010). Support vector machines for classification and regression. Analyst 135, 230-267. doi: 10.1039/B918972F
  11. Cabrera-Bosquet, L., Crossa, J., von Zitzewitz, J., Serret María, D., and Luis Araus, J. (2012). High-throughput phenotyping and genomic selection: the frontiers of crop breeding convergef. J. Integr. Plant Biol. 54, 312-320. doi: 10.1111/j.1744- 7909.2012.01116.x
  12. Clinton, N., Yu, L., and Gong, P. (2015). Geographic stacking: decision fusion to increase global land cover map accuracy. ISPRS J. Photogramm. Remote Sens. 103, 57-65. doi: 10.1016/j.isprsjprs.2015.02.010
  13. Cortes, C., and Vapnik, V. (1995). Support-vector networks. Mach. Learn. 20, 273-297. doi: 10.1007/BF00994018
  14. Deery, D., Jimenez-Berni, J., Jones, H., Sirault, X., and Furbank, R. (2014). Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4, 349-379. doi: 10.3390/agronomy4030349
  15. Donoho, D. L. (2006). For most large underdetermined systems of linear equations the minimal. Commun. Pure Appl. Math. 59, 797-829. doi: 10.1002/cpa.20132
  16. Ducat, D. C., and Silver, P. A. (2012). Improving carbon fixation pathways. Curr. Opin. Chem. Biol. 16, 337-344. doi: 10.1016/j.cbpa.2012.05.002
  17. Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004). Least angle regression. Ann. Stat. 32, 407-499. doi: 10.1214/009053604000000067
  18. Ehsani, M. R., Upadhyaya, S. K., Slaughter, D., Shafii, S., and Pelletier, M. (1999). A nir technique for rapid determination of soil mineral nitrogen. Precis. Agric. 1, 219-236. doi: 10.1023/A:1009916108990
  19. Esbensen, K. H., Guyot, D., Westad, F., and Houmoller, L. P. (2002). Multivariate Data Analysis: in Practice: An Introduction to Multivariate Data Analysis and Experimental Design. Esbjerg: Multivariate Data Analysis.
  20. Evans, J. R., and Von Caemmerer, S. (2012). Temperature response of carbon isotope discrimination and mesophyll conductance in tobacco. Plant Cell Environ. 36, 745-756. doi: 10.1111/j.1365-3040.2012.02591.x
  21. Farquhar, G. D., von Caemmerer, S., and Berry, J. A. (1980). A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78-90. doi: 10.1007/BF00386231
  22. Finkel, E. (2009). With 'phenomics, ' plant scientists hope to shift breeding into overdrive. Science 325:380. doi: 10.1126/science.325_380
  23. Fiorani, F., Rascher, U., Jahnke, S., and Schurr, U. (2012). Imaging plants dynamics in heterogenic environments. Curr. Opin. Biotechnol. 23, 227-235. doi: 10.1016/ j.copbio.2011.12.010
  24. Flood, P. J., Harbinson, J., and Aarts, M. G. M. (2011). Natural genetic variation in plant photosynthesis. Trends Plant Sci. 16, 327-335. doi: 10.1016/j.tplants.2011. 02.005
  25. Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1-22.
  26. Fu, P., and Weng, Q. (2016). Consistent land surface temperature data generation from irregularly spaced landsat imagery. Remote Sens. Environ. 184, 175-187. doi: 10.1016/j.rse.2016.06.019
  27. Furbank, R. T., and Tester, M. (2011). Phenomics -technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16, 635-644. doi: 10.1016/j.tplants. 2011.09.005
  28. Geladi, P., and Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Anal. Chim. Acta 185, 1-17. doi: 10.1016/0003-2670(86)80028-9
  29. Grossmann, K., Frankenberg, C., Magney, T. S., Hurlock, S. C., Seibt, U., and Stutz, J. (2018). PhotoSpec: a new instrument to measure spatially distributed red and far-red solar-induced chlorophyll fluorescence. Remote Sens. Environ. 216, 311-327. doi: 10.1016/j.rse.2018.07.002
  30. Großkinsky, D. K., Svensgaard, J., Christensen, S., and Roitsch, T. (2015). Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. J. Exp. Bot. 66, 5429-5440. doi: 10.1093/jxb/erv345
  31. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, 2nd Edn. New York, NY: Springer.
  32. Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., et al. (2018). Mapping forest change using stacked generalization: an ensemble approach. Remote Sens. Environ. 204, 717-728. doi: 10.1016/j.rse. 2017.09.029
  33. Heckmann, D., Schlüter, U., and Weber, A. P. M. (2017). Machine learning techniques for predicting crop photosynthetic capacity from leaf reflectance spectra. Mol. Plant 10, 878-890. doi: 10.1016/j.molp.2017.04.009
  34. Hong, Y., Hsu, K.-L., Sorooshian, S., and Gao, X. (2004). Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteorol. 43, 1834-1853. doi: 10.1175/JAM2173.1
  35. Hudson, G. S., Evans, J. R., von Caemmerer, S., Arvidsson, Y. B. C., and Andrews, T. J. (1992). Reduction of ribulose-1,5-bisphosphate carboxylase/oxygenase content by antisense RNA reduces photosynthesis in transgenic tobacco plants. Plant Physiol. 98:294. doi: 10.1104/pp.98.1.294
  36. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Vol. 112. New York, NY: Springer.
  37. Kim, T.-H. (2010). "Pattern recognition using artificial neural network: a review, " in Proceedings of the International Conference on Information Security and Assurance, (Berlin: Springer), 138-148.
  38. Kim, Y., Glenn, D. M., Park, J., Ngugi, H. K., and Lehman, B. L. (2011). Hyperspectral image analysis for water stress detection of apple trees. Comput. Electron. Agric. 77, 155-160. doi: 10.1016/j.compag.2011.04.008
  39. Kimes, D. S., Nelson, R. F., Manry, M. T., and Fung, A. K. (1998). Review article: attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements. Int. J. Remote Sens. 19, 2639-2663. doi: 10.1080/014311698214433
  40. Knyazikhin, Y., Schull, M. A., Stenberg, P., Mõttus, M., Rautiainen, M., Yang, Y., et al. (2013). Hyperspectral remote sensing of foliar nitrogen content. Proc. Natl. Acad. Sci. U.S.A. 110:811.
  41. Lawson, T., Kramer, D. M., and Raines, C. A. (2012). Improving yield by exploiting mechanisms underlying natural variation of photosynthesis. Curr. Opin. Biotechnol. 23, 215-220. doi: 10.1016/j.copbio.2011.12.012
  42. Li, L., Zhang, Q., and Huang, D. (2014). A review of imaging techniques for plant phenotyping. Sensors 14, 20078-20111. doi: 10.3390/s141120078
  43. Long, S. P., and Bernacchi, C. J. (2003). Gas exchange measurements, what can they tell us about the underlying limitations to photosynthesis? Procedures and sources of error. J. Exp. Bot. 54, 2393-2401. doi: 10.1093/jxb/erg262
  44. Long, S. P., Zhu, X. G., Naidu Shawna, L., and Ort Donald, R. (2006). Can improvement in photosynthesis increase crop yields? Plant Cell Environ. 29, 315-330. doi: 10.1111/j.1365-3040.2005.01493.x
  45. Mahlein, A.-K., Oerke, E.-C., Steiner, U., and Dehne, H.-W. (2012). Recent advances in sensing plant diseases for precision crop protection. Eur. J. Plant Pathol. 133, 197-209. doi: 10.1007/s10658-011-9878-z
  46. Matsuda, O., Tanaka, A., Fujita, T., and Iba, K. (2012). Hyperspectral imaging techniques for rapid identification of Arabidopsis mutants with altered leaf pigment status. Plant Cell Physiol. 53, 1154-1170. doi: 10.1093/pcp/pcs043
  47. Meacham-Hensold, K., Montes, C., Wu, J., Guan, K., Fu, P., Ainsworth, E., et al. (2019). High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity. Remote Sens. Environ. (in press). doi: 10.1016/j.rse. 2019.04.029
  48. Moghadassi, A. R., Nikkholgh, M. R., Parvizian, F., and Hosseini, S. M. (2010). Estimation of thermophysical properties of dimethyl ether as a commercial refrigerant based on artificial neural networks. Expert Syst. Appl. 37, 7755-7761. doi: 10.1016/j.eswa.2010.04.065
  49. Montes, J. M., Melchinger, A. E., and Reif, J. C. (2007). Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci. 12, 433-436. doi: 10.1016/j.tplants.2007.08.006
  50. Mountrakis, G., Im, J., and Ogole, C. (2011). Support vector machines in remote sensing: a review. ISPRS J. Photogramm. Remote Sens. 66, 247-259. doi: 10.1016/j.isprsjprs.2010.11.001
  51. Mutka, A. M., and Bart, R. S. (2014). Image-based phenotyping of plant disease symptoms. Front. Plant Sci. 5:734. doi: 10.3389/fpls.2014.00734
  52. Neto, A. B., Bonini, C. D. S. B., Bisi, B. S., Reis, A. R. D., and Coletta, L. F. S. (2017). Artificial neural network for classification and analysis of degraded soils. IEEE Lat. Am. Trans. 15, 503-509. doi: 10.1109/TLA.2017.7867601
  53. Ort, D. R., Merchant, S. S., Alric, J., Barkan, A., Blankenship, R. E., Bock, R., et al. (2015). Redesigning photosynthesis to sustainably meet global food and bioenergy demand. Proc. Natl. Acad. Sci. U.S.A. 112:8529. doi: 10.1073/pnas. 1424031112
  54. Parry, M. A. J., Reynolds, M., Salvucci, M. E., Raines, C., Andralojc, P. J., Zhu, X.- G., et al. (2011). Raising yield potential of wheat. II. Increasing photosynthetic capacity and efficiency. J. Exp. Bot. 62, 453-467. doi: 10.1093/jxb/erq304
  55. Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., and Plümer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput. Electron. Agric. 74, 91-99. doi: 10.1016/j.compag.2010.06.009
  56. Samarov, D. V., Hwang, J., and Litorja, M. (2015). The spatial LASSO with applications to unmixing hyperspectral biomedical images. Technometrics 57, 503-513. doi: 10.1080/00401706.2014.979950
  57. Sara, B., Howard, E., Marcel, B., Donald, W., and Heather, L. (2017). Relationships between hyperspectral data and components of vegetation biomass in low arctic tundra communities at ivotuk, alaska. Environ. Res. Lett. 12:025003. doi: 10. 1088/1748-9326/aa572e
  58. Sayer, J., Sunderland, T., Ghazoul, J., Pfund, J.-L., Sheil, D., Meijaard, E., et al. (2013). Ten principles for a landscape approach to reconciling agriculture, conservation, and other competing land uses. Proc. Natl. Acad. Sci. U.S.A. 110:8349. doi: 10.1073/pnas.1210595110
  59. Serbin, S. P., Dillaway, D. N., Kruger, E. L., and Townsend, P. A. (2012). Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature. J. Exp. Bot. 63, 489-502. doi: 10.1093/jxb/err294
  60. Serbin, S. P., Singh, A., Desai, A. R., Dubois, S. G., Jablonski, A. D., Kingdon, C. C., et al. (2015). Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. Remote Sens. Environ. 167, 78-87. doi: 10.1016/j.rse.2015.05.024
  61. Sesmero, M. P., Ledezma Agapito, I., and Sanchis, A. (2015). Generating ensembles of heterogeneous classifiers using stacked generalization. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 5, 21-34. doi: 10.1002/widm.1143
  62. Sharkey, T. D. (2016). What gas exchange data can tell us about photosynthesis. Plant Cell Environ. 39, 1161-1163. doi: 10.1111/pce.12641
  63. Sharkey, T. D., Bernacchi, C. J., Farquhar, G. D., and Singsaas, E. L. (2007). Fitting photosynthetic carbon dioxide response curves for C3 leaves. Plant Cell Environ. 30, 1035-1040. doi: 10.1111/j.1365-3040.2007.01710.x
  64. Silva-Perez, V., Molero, G., Serbin, S. P., Condon, A. G., Reynolds, M. P., Furbank, R. T., et al. (2018). Hyperspectral reflectance as a tool to measure biochemical and physiological traits in wheat. J. Exp. Bot. 69, 483-496. doi: 10.1093/jxb/ erx421
  65. Simkin, A. J., McAusland, L., Headland, L. R., Lawson, T., and Raines, C. A. (2015). Multigene manipulation of photosynthetic carbon assimilation increases CO2 fixation and biomass yield in tobacco. J. Exp. Bot. 66, 4075-4090. doi: 10.1093/ jxb/erv204
  66. Specht, D. F. (1991). A general regression neural network. IEEE Trans. Neural Netw. 2, 568-576. doi: 10.1109/72.97934
  67. Suykens, J. A., Van Gestel, T., and De Brabanter, J. (2002). Least Squares Support Vector Machines. Singapore: World Scientific.
  68. Sytar, O., Brestic, M., Zivcak, M., Olsovska, K., Kovar, M., Shao, H., et al. (2017). Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance. Sci. Total Environ. 578, 90-99. doi: 10.1016/j. scitotenv.2016.08.014
  69. Tester, M., and Langridge, P. (2010). Breeding technologies to increase crop production in a changing world. Science 327:818. doi: 10.1126/science.1183700
  70. Thomson, M. J. (2014). High-throughput SNP genotyping to accelerate crop improvement. Plant Breed. Biotechnol. 2, 195-212. doi: 10.9787/PBB.2014.2. 3.195
  71. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B Stat. Methodol. 58, 267-288. doi: 10.1111/j.2517-6161.1996. tb02080.x
  72. Ustin, S. L. (2013). Remote sensing of canopy chemistry. Proc. Natl. Acad. Sci. U.S.A. 110:804.
  73. Verrelst, J., Muñoz, J., Alonso, L., Delegido, J., Rivera, J. P., Camps-Valls, G., et al. (2012). Machine learning regression algorithms for biophysical parameter retrieval: opportunities for sentinel-2 and -3. Remote Sens. Environ. 118, 127- 139. doi: 10.1016/j.rse.2011.11.002
  74. Verrelst, J., Rivera, J. P., Moreno, J., and Camps-Valls, G. (2013). Gaussian processes uncertainty estimates in experimental sentinel-2 LAI and leaf chlorophyll content retrieval. ISPRS J. Photogramm. Remote Sens. 86, 157-167. doi: 10.1016/j.isprsjprs.2013.09.012
  75. Williams, C. K., and Rasmussen, C. E. (2006). Gaussian Processes for Machine Learning. Cambridge, CA: MIT Press.
  76. Wold, S., Sjöström, M., and Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometr. Intell. Lab. Syst. 58, 109-130. doi: 10.1016/s0169- 7439(01)00155-1
  77. Wolpert, D. H. (1992). Stacked generalization. Neural Netw. 5, 241-259. doi: 10.1016/s0893-6080(05)80023-1
  78. Yang, D., and Bao, W. (2017). Group lasso-based band selection for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 14, 2438-2442. doi: 10.1109/lgrs.2017.2768074
  79. Yang, X., Shi, H., Stovall, A., Guan, K., Miao, G., Zhang, Y., et al. (2018). FluoSpec 2-an automated field spectroscopy system to monitor canopy solar-induced fluorescence. Sensors 18:2063. doi: 10.3390/s18072063
  80. Yendrek, C. R., Tomaz, T., Montes, C. M., Cao, Y., Morse, A. M., Brown, P. J., et al. (2017). High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance. Plant Physiol. 173:614. doi: 10.1104/pp.16.01447
  81. Yokota, A., and Shigeoka, S. (2008). Engineering photosynthetic pathways. Adv. Plant Biochem. Mol. Biol. 1, 81-105. doi: 10.1016/s1755-0408(07)01004-1
  82. Zain, A. M., Haron, H., Qasem, S. N., and Sharif, S. (2012). Regression and ANN models for estimating minimum value of machining performance. Appl. Math. Model. 36, 1477-1492. doi: 10.1016/j.apm.2011.09.035
  83. Zhu, X.-G., Long, S. P., and Ort, D. R. (2008). What is the maximum efficiency with which photosynthesis can convert solar energy into biomass? Curr. Opin. Biotechnol. 19, 153-159. doi: 10.1016/j.copbio.2008.02.004

Related papers

High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity
Christine Raines

Remote Sensing of Environment, 2019

Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco (V c,max), maximum electron transport rate (J max) and percentage leaf nitrogen ([N]). The model was developed for wild type and genetically modified plants that represent a wide range of photosynthetic capacities. Results show that hyperspectral reflectance accurately predicted V c,max , J max and [N] for all plants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictive ability relative to gas exchange measurements for V c,max , but not for J max , and not for genotypes unique to 2017. Building a new model including data collected in 2017 resulted in more robust predictions, with R 2 increases of 17% for V c,max. and 13% J max. Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lower V c,max. The PLSR model was able to accurately predict both lower V c,max and higher leaf [N] for this genotype suggesting that the spectral based estimates of V c,max and leaf nitrogen [N] are independent. These results suggest that the PLSR model can be applied across years, but only to genotypes used to build the model and that the actual mechanism measured with the PLSR technique is not directly related to leaf [N]. The success of the leaf-scale analysis suggests that similar approaches may be successful at the canopy and ecosystem scales but to use these methods across years and between genotypes at any scale, application of accurately populated physical based models based on radiative transfer principles may be required.

View PDFchevron_right
The Effect of Differential Growth Rates across Plants on Spectral Predictions of Physiological Parameters
Arnon Karnieli

PLoS ONE, 2014

Leaves of various ages and positions in a plant's canopy can present distinct physiological, morphological and anatomical characteristics, leading to complexities in selecting a single leaf for spectral representation of an entire plant. A fortiori, as growth rates between canopies differ, spectral-based comparisons across multiple plants -often based on leaves' position but not age -becomes an even more challenging mission. This study explores the effect of differential growth rates on the reflectance variability between leaves of different canopies, and its implication on physiological predictions made by widelyused spectral indices. Two distinct irrigation treatments were applied for one month, in order to trigger the formation of different growth rates between two groups of grapevines. Throughout the experiment, the plants were physiologically and morphologically monitored, while leaves from every part of their canopies were spectrally and histologically sampled. As the control vines were constantly developing new leaves, the water deficit plants were experiencing growth inhibition, resulting in leaves of different age at similar nodal position across the treatments. This modification of the age-position correlation was characterized by a near infrared reflectance difference between younger and older leaves, which was found to be exponentially correlated (R 2 = 0.98) to the age-dependent area of intercellular air spaces within the spongy parenchyma. Overall, the foliage of the control plant became more spectrally variable, creating complications for intra-and intertreatment leaf-based comparisons. Of the derived indices, the Structure-Insensitive Pigment Index (SIPI) was found indifferent to the age-position effect, allowing the treatments to be compared at any nodal position, while a Normalized Difference Vegetation Index (NDVI)-based stomatal conductance prediction was substantially affected by differential growth rates. As various biotic and abiotic factors may form distinctions in growth, future precision agriculture studies should consider its spectral effect on physiological predictions.

View PDFchevron_right
Predicting leaf traits across functional groups using reflectance spectroscopy
Juliana Martin Pardo

SummaryPlant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems.We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra.Within the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2>0.85; %RMSE<10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2=0.55-0.85; %RMSE=12.7-19.1). Micronutrients such as ...

View PDFchevron_right
Modelling the effects of environmental conditions on apparent photosynthesis of Stipa bromoides by machine learning tools
Boris Kompare

Ecological Modelling, 2000

Apparent leaf photosynthesis of the grass Stipa bromoides was measured in the field in two sites of Northern Greece. For predicting apparent photosynthesis from irradiance, temperature and relative air humidity data, we applied and compared two modelling approaches: ordinary statistical modelling and automatic model construction based on machine learning procedures. Ordinary statistical models were constructed based on background knowledge concerning the response of photosynthesis to irradiance. A Michaelis -Menten type light saturation curve was selected among six candidate models and was extended to include air temperature effects. A bell-shaped function of temperature was substituted for the parameter describing the asymptotic maximum photosynthesis. The final model accounted for 67.3% of data variation and was further improved by splitting the data set by experimental site. Site-specific differences were detected regarding the half saturation constant for light and the optimal temperature for photosynthesis. Automatic model construction produced a number of regression trees that enabled a detailed but simple description of the way irradiance, temperature and relative humidity affect photosynthesis. Photosynthesis increases with increasing irradiance, temperature affects photosynthesis when irradiance is close to saturation levels and relative humidity has an effect when both irradiance and temperature are high. There is a threshold value of relative humidity (at about 35%), below which photosynthesis is independent of irradiance within the observed range, decreasing with increasing temperature when temperature is high ( \25°C) and increasing with increasing relative humidity when temperature is low ( B25°C). The machine learning tools we used provide a very powerful modelling alternative to ordinary curve fitting methods. Their major advantages are the flexibility to select between accuracy and generality and their robustness against outliers and mixtures of differential responses. The models are transparent and easily interpreted. They seem to be able to handle quite complex dependencies among attributes, not requiring prior expert knowledge.

View PDFchevron_right
PlantServation: time-series phenotyping using machine learning revealed seasonal pigment fluctuation in diploid and polyploidArabidopsis
Toshiaki Tameshige

Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers, but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software to analyze field images, where the plant shape, color, and background vary over months. We estimated the anthocyanin contents of small individuals of fourArabidopsisspecies using color information and verified the results experimentally. We obtained >4 million plant images over three field seasons to study anthocyanin fluctuations. We found significant effects of past radiation, coldness, and precipitation on the anthocyanin content in the field. The synthetic allopolyploidA. kamchaticarecapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit an...

View PDFchevron_right

Related topics

  • Psychology
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved