Incorporating statistical strategy into image analysis to estimate effects of steam and allyl isocyanate on weed control


Autoři: Dong Sub Kim aff001;  Steven B. Kim aff002;  Steven A. Fennimore aff001
Působiště autorů: Department of Plant Sciences, University of California Davis, Salinas, California, United States of America aff001;  Mathematics and Statistics Department, California State University, Monterey Bay, Seaside, California, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222695

Souhrn

Weeds are the major limitation to efficient crop production, and effective weed management is necessary to prevent yield losses due to crop-weed competition. Assessments of the relative efficacy of weed control treatments by traditional counting methods is labor intensive and expensive. More efficient methods are needed for weed control assessments. There is extensive literature on advanced techniques of image analysis for weed recognition, identification, classification, and leaf area, but there is limited information on statistical methods for hypothesis testing when data are obtained by image analysis (RGB decimal code). A traditional multiple comparison test, such as the Dunnett-Tukey-Kramer (DTK) test, is not an optimal statistical strategy for the image analysis because it does not fully utilize information contained in RGB decimal code. In this article, a bootstrap method and a Poisson model are considered to incorporate RGB decimal codes and pixels for comparing multiple treatments on weed control. These statistical methods can also estimate interpretable parameters such as the relative proportion of weed coverage and weed densities. The simulation studies showed that the bootstrap method and the Poisson model are more powerful than the DTK test for a fixed significance level. Using these statistical methods, three soil disinfestation treatments, steam, allyl-isothiocyanate (AITC), and control, were compared. Steam was found to be significantly more effective than AITC, a difference which could not be detected by the DTK test. Our study demonstrates that an appropriate statistical method can leverage statistical power even with a simple RGB index.

Klíčová slova:

Cell phones – Image analysis – Research errors – Simulation and modeling – Statistical data – Statistical methods – Weeds – Digital cameras


Zdroje

1. Peña JM, Torres-Sȧnchez J, Serrano-Pėrez A, de Castro AI, Lȯpez-Granados F. Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Transactions of the ASAE. 2015;15(3):5609–5626.

2. Mahlein AK. Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease. 2016;100(2):241–251. doi: 10.1094/PDIS-03-15-0340-FE 30694129

3. Sen F, Meyvaci KB, Turanli F, Aksoy U. Effects of short-term controlled atmosphere treatment at elevated temperature on dried fig fruit.. Journal of Stored Products Research. 2010;46(1):28–33. doi: 10.1016/j.jspr.2009.07.005

4. Samtani JB, Ajwa HA, Weber JB, Browne GT, Klose S, Hunzie J, et al. Evaluation of non-fumigant alternatives to methyl bromide for weed control and crop yield in California strawberries (Fragaria ananassa L.). Crop Protection. 2011;30(1):45–51. doi: 10.1016/j.cropro.2010.08.023

5. Bangarwa SK, Norsworthy JK, Gbur EE. Allyl isothiocyanate as a methyl bromide alternative for weed management in polyethylene-mulched tomato. Weed Technology. 2012;26(3):449–454. doi: 10.1614/WT-D-11-00152.1

6. Downie HF, Adu MO, Schmidt S, Otten W, Dupuy LX, White PJ, et al. Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis. Plant, Cell and Environment. 2015;38(7):1213–1232. doi: 10.1111/pce.12448 25211059

7. Woebbecke DM, Meyer GE, Von Bargen K, Mortensen DA. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE. 1995;38(1):259–270. doi: 10.13031/2013.27838

8. Golzarian MR, Lee MK, Desbiolles MA. Evaluation of color indices for improved segmentation of plant images. Transactions of the ASAE. 2012;55(1):261–273. doi: 10.13031/2013.41236

9. Longchamps L, Panneton B, Simard MJ, Leroux GD. Could weed sensing in corn interrows result in efficient weed control? Weed Technology. 2012;26(4):649–656.

10. Meyer GE, Mehta T, Kocher MF, Mortensen DA, Samal A. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Transactions of the ASAE. 1998;41(4):1189–1197. doi: 10.13031/2013.17244

11. Yang W, Wang S, Zhao X, Zhang J, Feng J. Greenness identification based on HSV decision tree. Information Processing in Agriculture. 2015;2:149–160. doi: 10.1016/j.inpa.2015.07.003

12. Dunnett CW. Pairwise multiple comparisons in the unequal variance case. Journal of the American Statistical Association. 1980;75(372):796–800. doi: 10.1080/01621459.1980.10477552

13. Efron B. Bootstrap methods: Another look at the jackknife. The Annals of Statistics. 1979;7(1):1–26. doi: 10.1214/aos/1176344552

14. Efron B, Tibshirani R. An introduction to the bootstrap. Boca Raton, FL: Chapman & Hall/CRC; 1993.

15. Efron B. Better bootstrap confidence intervals. Journal of the American Statistical Association. 1987;82(397):171–185. doi: 10.2307/2289153

16. Haukoos JS, Lewis RJ. Advanced statistics: bootstrapping confidence intervals for statistics with “difficult” distributions. Academic Emergency Medicine. 2005;12(4):360–365. doi: 10.1197/j.aem.2004.11.018

17. Canty A, Ripley B. boot: Bootstrap R (S-Plus) functions. 2017; R package version 1.3-20.

18. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2017. URL https://www.R-project.org/.

19. Ebel RL. Etimation of the reliability of ratings. Psychometrika. 1951;16:407–424. doi: 10.1007/BF02288803

20. Bartko JJ. The intraclass correlation coefficient as a measure of reliability. Psychological Reports. 1966;19:3–11. doi: 10.2466/pr0.1966.19.1.3 5942109

21. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine. 2016;15(2):155–163. doi: 10.1016/j.jcm.2016.02.012 27330520

22. Tukey J. Comparing individual means in the analysis of variance. Biometrics. 1949;5(2):99–114. doi: 10.2307/3001913 18151955

23. Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association. 1952;47(260):583–621. doi: 10.1080/01621459.1952.10483441

24. Hollander M, Wolfe DA. Nonparametric statistical methods. New York: John Wiley & Sons; 1973.

25. Kutner M, Nachtsheim C, Neter J, Li W. Applied linear statistical models (5th edition). New York, NY: McGraw-Hill/Irwin; 2004.

26. Hecke TV. Power study of anova versus Kruskal-Wallis test. Journal of Statistics and Management Systems. 2012;15(2–3):241–247. doi: 10.1080/09720510.2012.10701623

27. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B. 1995;57(1):289–300.

28. Wyatt A. Determining the RGB Value of a Color. Tips.Net. 19. Mar 2016. Available from: https://excelribbon.tips.net/T010180_Determining_the_RGB_Value_of_a_Color.html Cited 8 April 2019.


Článek vyšel v časopise

PLOS One


2019 Číslo 9

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Léčba bolesti v ordinaci praktického lékaře
nový kurz
Autoři: MUDr. PhDr. Zdeňka Nováková, Ph.D.

Revmatoidní artritida: včas a k cíli
Autoři: MUDr. Heřman Mann

Jistoty a nástrahy antikoagulační léčby aneb kardiolog - neurolog - farmakolog - nefrolog - právník diskutují
Autoři: doc. MUDr. Štěpán Havránek, Ph.D., prof. MUDr. Roman Herzig, Ph.D., doc. MUDr. Karel Urbánek, Ph.D., prim. MUDr. Jan Vachek, MUDr. et Mgr. Jolana Těšínová, Ph.D.

Léčba akutní pooperační bolesti
Autoři: doc. MUDr. Jiří Málek, CSc.

Nové antipsychotikum kariprazin v léčbě schizofrenie
Autoři: prof. MUDr. Cyril Höschl, DrSc., FRCPsych.

Všechny kurzy
Kurzy Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Nemáte účet?  Registrujte se

Zapomenuté heslo

Zadejte e-mailovou adresu se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se