**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**32722

##### Model-Driven and Data-Driven Approaches for Crop Yield Prediction: Analysis and Comparison

**Authors:**
Xiangtuo Chen,
Paul-Henry Cournéde

**Abstract:**

**Keywords:**
Crop yield prediction,
crop model,
sensitivity analysis,
paramater estimation,
particle swarm optimization,
random forest.

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1316506

**References:**

[1] Drummond S T, Sudduth K A, Joshi A, et al. Statistical and neural methods for site-specific yield prediction(J). Transactions-American Society of Agricultural Engineers, 2003, 46(1): 5-16.

[2] Liu J, Goering C E, Tian L. A neural network for setting target corn yields(J). Transactions-American Society of Agricultural Engineers, 2001, 44(3): 705-714.

[3] Kang F. Mod`eles de croissance de plantes et m´ethodologies adaptees `a leur parametrisation pour l’analyse des ph´enotypes(D)0.5em minus 0.4emChatenay-Malabry, Ecole centrale de Paris, 2013.

[4] Cournede P H, Chen Y, Wu Q, Baey C, Bayol Development and evaluation of plant growth models: Methodology and implementation in the PYGMALION platform, 0.5em minus 0.4emMathematical Modelling of Natural Phenomena, 2013, 8(4): 112-130.

[5] Cournede P H, Letort V, Mathieu A, et al. Some parameter estimation issues in functional-structural plant modelling(J). Mathematical Modelling of Natural Phenomena, 2011, 6(2): 133-159.

[6] Goodwin G C, Payne R L. Dynamic system identification: experiment design and data analysis(J). 1977.

[7] Wallach D, Goffinet B. Mean squared error of prediction in models for studying ecological and agronomic systems(J). Biometrics, 1987: 561-573.

[8] Wallach D. Evaluating crop models(J). Working with Dynamic Crop Models Evaluation, Analysis, Parameterization, and Applications, Elsevier, Amsterdam, 2006: 11-54.

[9] Mess´ean A, Bernard H, de Turckheim ´ E. Concevoir et construire la d´ecision: D´emarches en agriculture, agroalimentaire et espace rural(M). Editions Quae, 2009.

[10] Lecoeur J, Poir´e-Lassus R, Christophe A, et al. Quantifying physiological determinants of genetic variation for yield potential in sunflower. SUNFLO: a model-based analysis(J). Functional plant biology, 2011, 38(3): 246-259.

[11] Brun F, Wallach D, Makowski D, et al. Working with dynamic crop models: Evaluation, analysis, parameterization, and applications(M). Elsevier, 2006.

[12] Saltelli A, Tarantola S, Campolongo F, et al. Sensitivity analysis in practice: a guide to assessing scientific models(M). John Wiley and Sons, 2004.

[13] Saltelli A, Chan K, and Scott EM, eds. Sensitivity analysis. Vol. 1. New York: Wiley, 2000.

[14] Wu, QL, Courn`ede PH and Mathieu, A An efficient computational method for global sensitivity analysis and its application to tree growth modelling(J). Reliability Engineering & System Safety, 2012, 107: 35-43.

[15] Courn`ede PH, Chen Y, Wu QL, Baey C, Bayol B Development and evaluation of plant growth models: Methodology and implementation in the pygmalion platform(J). Mathematical Modelling of Natural Phenomena, 2013, 8: 112-130.

[16] Eberhart R, Kennedy J. A new optimizer using particle swarm theory(C) Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on. IEEE, 1995: 39-43.

[17] Shi Y. Particle swarm optimization: developments, applications and resources(C) Evolutionary computation, 2001. Proceedings of the 2001 Congress on. IEEE, 2001, 1: 81-86.

[18] Shi Y, Eberhart R. Parameter selection in particle swarm optimization(C) Evolutionary programming VII. Springer Berlin/Heidelberg, 1998: 591-600.

[19] Kennedy J. Particle swarm optimization(M) Encyclopedia of machine learning. Springer US, 2011: 760-766.

[20] Kennedy J, Mendes R. Population structure and particle swarm performance(C) Evolutionary Computation, 2002. CEC’02. Proceedings of the 2002 Congress on. IEEE, 2002, 2: 1671-1676.

[21] Clerc M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization(C) Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on. IEEE, 1999, 3: 1951-1957.

[22] Shi Y, Eberhart R. A modified particle swarm optimizer(C) Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on. IEEE, 1998: 69-73.

[23] Eberhart R C, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization(C) Evolutionary Computation, 2000. Proceedings of the 2000 Congress on. IEEE, 2000, 1: 84-88.

[24] Schutte J F, Reinbolt J A, Fregly B J, et al. Parallel global optimization with the particle swarm algorithm(J). International journal for numerical methods in engineering, 2004, 61(13): 2296.

[25] Clarke F H. Optimization and nonsmooth analysis(M). Society for Industrial and Applied Mathematics, 1990.

[26] Singh A, Ganapathysubramanian B, Singh A K, et al. Machine learning for high-throughput stress phenotyping in plants(J). Trends in plant science, 2016, 21(2): 110-124.

[27] Von Storch H. Misuses of statistical analysis in climate research(M) Analysis of Climate Variability. Springer Berlin Heidelberg, 1999: 11-26.

[28] Belsley D A. Conditioning diagnostics(M). John Wiley & Sons, Inc., 1991.

[29] Cline A K, Moler C B, Stewart G W, et al. An estimate for the condition number of a matrix(J). SIAM Journal on Numerical Analysis, 1979, 16(2): 368-375.

[30] Yin S, Ding S X, Haghani A, et al. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process(J). Journal of Process Control, 2012, 22(9): 1567-1581.

[31] Shin M Y. The use of ridge regression for yield prediction models with multicollinearity problems(J).. Journal of Korean Forestry Society, 1990, 79(3): 260-268.

[32] Hassan S S, Farhan M, Mangayil R, et al. Bioprocess data mining using regularized regression and random forests(J). BMC systems biology, 2013, 7(1): S5.

[33] Chang J, Clay D E, Dalsted K, et al. Corn (L.) yield prediction using multispectral and multidate reflectance(J). Agronomy journal, 2003, 95(6): 1447-1453.

[34] Abdel-Rahman E M, Mutanga O, Odindi J, et al. A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of Swiss chard grown under different irrigation water sources using hyperspectral data(J). Computers and Electronics in Agriculture, 2014, 106: 11-19.

[35] Hall M A. Correlation-based feature selection of discrete and numeric class machine learning(J). 2000.

[36] Ru G. Data mining of agricultural yield data: A comparison of regression models(C) Industrial Conference on Data Mining. Springer Berlin Heidelberg, 2009: 24-37.

[37] Albuquerque M C F, de Carvalho N M. Effect of the type of environmental stress on the emergence of sunflower (Helianthus annus L.), soybean (Glycine max (L.) Merril) and maize (Zea mays L.) seeds with different levels of vigor(J). Seed Science and Technology (Switzerland), 2003, 31(2): 465-479.

[38] Midmore E K, McCartan S A, Jinks R L, et al. Using thermal time models to predict germination of five provenances of silver birch (Betula pendula Roth) in southern England(J). Silva Fennica, 2015, 49(2).

[39] Atwell B J, Kriedemann P E, Turnbull C G N. Plants in action: adaptation in nature, performance in cultivation(M). Macmillan Education AU, 1999.

[40] Williams M M. Agronomics and economics of plant population density on processing sweet corn(J). Field Crops Research, 2012, 128: 55-61.

[41] Monteith J L, Moss C J. Climate and the efficiency of crop production in Britain (and discussion)(J). Philosophical Transactions of the Royal Society of London B: Biological Sciences, 1977, 281(980): 277-294.