Irrigation Zone Delineation by Coupling Neural Networks with Spatial Statistics

Heaton, Matthew J.; Teuscher, David; Hansen, Neil C.

doi:10.51387/24-NEJSDS71

The New England Journal of Statistics in Data Science

Irrigation Zone Delineation by Coupling Neural Networks with Spatial Statistics

Matthew J. Heaton

David Teuscher Neil C. Hansen

https://doi.org/10.51387/24-NEJSDS71

Pub. online: 31 October 2024 Type: Spatial And Environmental Statistics

Open Access

Accepted
21 August 2024

Published
31 October 2024

Abstract

Variable rate irrigation (VRI) seeks to increase the efficiency of irrigation by spatially adjusting water output within an agricultural field. Central to the success of VRI technology is establishing homogeneous irrigation zones. In this research, we propose a fusion of statistical modeling and deep learning by using artificial neural networks to map irrigation zones from simple-to-measure predictors. We further couple our neural network model with spatial correlation to capture smooth variations in the irrigation zones. We demonstrate the effectiveness of our model to define irrigation zones for a farm of winter wheat crop in Rexburg, Idaho.

References

[1]

Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A. and Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon 4(11) 00938.

[2]

Albert, J. H. and Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association 88(422) 669–679. MR1224394

[3]

Bahat, I., Netzer, Y., Ben-Gal, A., Grünzweig, J., Peeters, A. and Cohen, Y. (2019). Comparison of water potential and yield parameters under uniform and variable rate drip irrigation in a cabernet sauvignon vineyard. In Precision Agriculture’19 574–577 Wageningen Academic Publishers.

[4]

Berrett, C. and Calder, C. A. (2016). Bayesian spatial binary classification. Spatial Statistics 16 72–102. https://doi.org/10.1016/j.spasta.2016.01.004. MR3493089

[5]

Bhakta, I., Phadikar, S. and Majumder, K. (2019). State-of-the-art technologies in precision agriculture: a systematic review. Journal of the Science of Food and Agriculture 99(11) 4878–4888.

[6]

Bornn, L., Shaddick, G. and Zidek, J. V. (2012). Modeling nonstationary processes through dimension expansion. Journal of the American Statistical Association 107(497) 281–289. https://doi.org/10.1080/01621459.2011.646919. MR2949359

[7]

Chen, W., Li, Y., Reich, B. J. and Sun, Y. (2024). Deepkriging: Spatially dependent deep neural networks for spatial prediction. Statistica Sinica 34 291–311. https://doi.org/10.5705/ss.202021.0277. MR4683573

[8]

Cisternas, I., Velásquez, I., Caro, A. and Rodríguez, A. (2020). Systematic literature review of implementations of precision agriculture. Computers and Electronics in Agriculture 176 105626.

[9]

Cohen, Y., Gogumalla, P., Bahat, I., Netzer, Y., Ben-Gal, A., Lenski, I., Michael, Y. and Helman, D. (2019). Can time series of multispectral satellite images be used to estimate stem water potential in vineyards? In Precision Agriculture’19 1–5 Wageningen Academic Publishers.

[10]

D’Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D. et al. (2020). Underspecification presents challenges for credibility in modern machine learning. arXiv preprint arXiv:2011.03395.

[11]

Dixon, M. F., Polson, N. G. and Sokolov, V. O. (2019). Deep learning for spatio-temporal modeling: Dynamic traffic flows and high frequency trading. Applied Stochastic Models in Business and Industry 35(3) 788–807. https://doi.org/10.1002/asmb.2399. MR3974250

[12]

Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S. and Dehmer, M. (2020). An introductory review of deep learning for prediction models with big data. Frontiers in Artificial Intelligence 3 4.

[13]

Fontanet, M., Scudiero, E., Skaggs, T. H., Fernàndez-Garcia, D., Ferrer, F., Rodrigo, G. and Bellvert, J. (2020). Dynamic management zones for irrigation scheduling. Agricultural Water Management 238 106207.

[14]

Friedman, J., Hastie, T., Tibshirani, R. et al. (2001) The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York. https://doi.org/10.1007/978-0-387-21606-5. MR1851606

[15]

Gal, Y. and Ghahramani, Z. (2016). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In International Conference on Machine Learning 1050–1059. PMLR.

[16]

Ghosh, M., Maiti, T., Kim, D., Chakraborty, S. and Tewari, A. (2004). Hierarchical Bayesian neural networks: an application to a prostate cancer study. Journal of the American Statistical Association 99(467) 601–608. https://doi.org/10.1198/016214504000000665. MR2086386

[17]

Haario, H., Saksman, E. and Tamminen, J. (2001). An adaptive Metropolis algorithm. Bernoulli 7(2) 223–242. https://doi.org/10.2307/3318737. MR1828504

[18]

Haghverdi, A., Leib, B. G., Washington-Allen, R. A., Ayers, P. D. and Buschermohle, M. J. (2015). Perspectives on delineating management zones for variable rate irrigation. Computers and Electronics in Agriculture 117 154–167.

[19]

Heaton, M. J., Christensen, W. F. and Terres, M. A. (2017). Nonstationary Gaussian process models using spatial hierarchical clustering from finite differences. Technometrics 59(1) 93–101. https://doi.org/10.1080/00401706.2015.1102763. MR3604192

[20]

Hedley, C. B. and Yule, I. J. (2009). Soil water status mapping and two variable-rate irrigation scenarios. Precision Agriculture 10(4) 342–355.

[21]

Helman, D., Bahat, I., Netzer, Y., Ben-Gal, A., Alchanatis, V., Peeters, A. and Cohen, Y. (2018). Using time series of high-resolution planet satellite images to monitor grapevine stem water potential in commercial vineyards. Remote Sensing 10(10) 1615.

[22]

Higgs, M. D. and Hoeting, J. A. (2010). A clipped latent variable model for spatially correlated ordered categorical data. Computational Statistics & Data Analysis 54(8) 1999–2011. https://doi.org/10.1016/j.csda.2010.02.024. MR2640303

[23]

Hughes, J. and Haran, M. (2013). Dimension reduction and alleviation of confounding for spatial generalized linear mixed models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 75(1) 139–159. https://doi.org/10.1111/j.1467-9868.2012.01041.x. MR3008275

[24]

Jung, J., Maeda, M., Chang, A., Bhandari, M., Ashapure, A. and Landivar-Bowles, J. (2021). The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Current Opinion in Biotechnology 70 15–22.

[25]

Katz, L., Naor, A., Litaor, M., Ben-Gal, A., Alchanatis, V., Peres, M., Peeters, A. and Cohen, Y. (2021). Methodology for comparison between uniform and variable rate application in a drip-irrigated peach orchard. In Precision Agriculture’21 823–842 Wageningen Academic Publishers.

[26]

Khanal, S., Fulton, J. and Shearer, S. (2017). An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture 139 22–32.

[27]

Klute, A. (1986). Water retention: laboratory methods. In Methods of Soil Analysis, Part 1: Physical and Mineralogical Methods 5 635–662.

[28]

Lee, J., Bahri, Y., Novak, R., Schoenholz, S. S., Pennington, J. and Sohl-Dickstein, J. (2017). Deep neural networks as gaussian processes. arXiv preprint arXiv:1711.00165.

[29]

Linardatos, P., Papastefanopoulos, V. and Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy 23(1) 18.

[30]

Lindblom, J., Lundström, C., Ljung, M. and Jonsson, A. (2017). Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies. Precision Agriculture 18(3) 309–331.

[31]

Lo, T. H., Heeren, D. M., Mateos, L., Luck, J. D., Martin, D. L., Miller, K. A., Barker, J. B. and Shaver, T. M. (2017). Field characterization of field capacity and root zone available water capacity for variable rate irrigation. Applied Engineering in Agriculture 33(4) 559–572.

[32]

Loures, L., Chamizo, A., Ferreira, P., Loures, A., Castanho, R. and Panagopoulos, T. (2020). Assessing the effectiveness of precision agriculture management systems in Mediterranean small farms. Sustainability 12(9) 3765.

[33]

Matthews, A. G. d. G., Rowland, M., Hron, J., Turner, R. E. and Ghahramani, Z. (2018). Gaussian process behaviour in wide deep neural networks. arXiv preprint arXiv:1804.11271.

[34]

McDermott, P. L. and Wikle, C. K. (2019). Bayesian recurrent neural network models for forecasting and quantifying uncertainty in spatial-temporal data. Entropy 21(2) 184. https://doi.org/10.3390/e21020184. MR3923929

[35]

Neal, R. M. (2012) Bayesian Learning for Neural Networks 118. Springer Science & Business Media.

[36]

Nwankpa, C., Ijomah, W., Gachagan, A. and Marshall, S. (2018). Activation functions: Comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378.

[37]

Ohana-Levi, N., Ben-Gal, A., Peeters, A., Termin, D., Linker, R., Baram, S., Raveh, E. and Paz-Kagan, T. (2021). A comparison between spatial clustering models for determining N-fertilization management zones in orchards. Precision Agriculture 22(1) 99–123.

[38]

Ohana-Levi, N., Bahat, I., Peeters, A., Shtein, A., Netzer, Y., Cohen, Y. and Ben-Gal, A. (2019). A weighted multivariate spatial clustering model to determine irrigation management zones. Computers and Electronics in Agriculture 162 719–731.

[39]

O’Shaughnessy, S. A., Evett, S. R., Andrade, A., Workneh, F., Price, J. A. and Rush, C. M. (2015). Site-specific variable rate irrigation as a means to enhance water use efficiency. In 2015 ASABE/IA Irrigation Symposium: Emerging Technologies for Sustainable Irrigation – A Tribute to the Career of Terry Howell, Sr. Conference Proceedings 1–21. American Society of Agricultural and Biological Engineers.

[40]

O’Shaughnessy, S. A., Evett, S. R., Colaizzi, P. D., Andrade, M. A., Marek, T. H., Heeren, D. M., Lamm, F. R. and LaRue, J. L. (2019). Identifying advantages and disadvantages of variable rate irrigation: An updated review. Applied Engineering in Agriculture 35(6) 837–852.

[41]

Pang, G., Shen, C., Cao, L. and Hengel, A. V. D. (2021). Deep learning for anomaly detection: A review. ACM Computing Surveys (CSUR) 54(2) 1–38.

[42]

Perea, R. G., Daccache, A., Díaz, J. R., Poyato, E. C. and Knox, J. W. (2018). Modelling impacts of precision irrigation on crop yield and in-field water management. Precision Agriculture 19(3) 497–512.

[43]

Polson, N. G. and Sokolov, V. (2019). Bayesian regularization: From Tikhonov to horseshoe. Wiley Interdisciplinary Reviews: Computational Statistics 11(4) 1463. https://doi.org/10.1002/wics.1463. MR3999526

[44]

Ramachandran, P., Zoph, B. and Le, Q. V. (2017). Searching for activation functions. arXiv preprint arXiv:1710.05941.

[45]

Saha, A., Basu, S. and Datta, A. (2023). Random forests for spatially dependent data. Journal of the American Statistical Association 118(541) 665–683. https://doi.org/10.1080/01621459.2021.1950003. MR4571149

[46]

Sauer, A., Gramacy, R. B. and Higdon, D. (2020). Active learning for deep Gaussian process surrogates. arXiv preprint arXiv:2012.08015. https://doi.org/10.1080/00401706.2021.2008505. MR4543056

[47]

Shand, L. and Li, B. (2017). Modeling nonstationarity in space and time. Biometrics 73(3) 759–768. https://doi.org/10.1111/biom.12656. MR3713110

[48]

Sigrist, F. (2022). Gaussian process boosting. The Journal of Machine Learning Research 23(1) 10565–10610. MR4577671

[49]

Sit, M., Demiray, B. Z., Xiang, Z., Ewing, G. J., Sermet, Y. and Demir, I. (2020). A comprehensive review of deep learning applications in hydrology and water resources. Water Science and Technology 82(12) 2635–2670.

[50]

Steven, M. and Clark, J. A. (2013) Applications of Remote Sensing in Agriculture. Elsevier.

[51]

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58(1) 267–288. MR1379242

[52]

Tran, M. -N., Nguyen, N., Nott, D. and Kohn, R. (2020). Bayesian deep net GLM and GLMM. Journal of Computational and Graphical Statistics 29(1) 97–113. https://doi.org/10.1080/10618600.2019.1637747. MR4085867

[53]

Wani, M. A., Bhat, F. A., Afzal, S. and Khan, A. I. (2020) Advances in Deep Learning. Springer. https://doi.org/10.1007/978-981-13-6794-6. MR3966423

[54]

Wikle, C. K. (2019). Comparison of deep neural networks and deep hierarchical models for spatio-temporal data. Journal of Agricultural, Biological and Environmental Statistics 24(2) 175–203. https://doi.org/10.1007/s13253-019-00361-7. MR3945276

[55]

Williams, C. K. (1997). Computing with infinite networks. In Advances in Neural Information Processing Systems 295–301.

[56]

Wójtowicz, M., Wójtowicz, A., Piekarczyk, J. et al. (2016). Application of remote sensing methods in agriculture. Communications in Biometry and Crop Science 11(1) 31–50.

[57]

Yari, A., Madramootoo, C. A., Woods, S. A., Adamchuk, V. I. and Huang, H. -H. (2017). Assessment of field spatial and temporal variabilities to delineate site-specific management zones for variable-rate irrigation. Journal of Irrigation and Drainage Engineering 143(9) 04017037.

[58]

Zammit-Mangion, A., Kaminski, M. D., Tran, B. -H., Filippone, M. and Cressie, N. (2023). Spatial Bayesian neural networks. arXiv preprint arXiv:2311.09491. https://doi.org/10.1016/j.spasta.2024.100825. MR4731204

[59]

Zhan, W. and Datta, A. (2023). Neural networks for geospatial data. arXiv preprint arXiv:2304.09157.

[60]

Zhang, J., Guan, K., Peng, B., Jiang, C., Zhou, W., Yang, Y., Pan, M., Franz, T. E., Heeren, D. M., Rudnick, D. R. et al. (2021). Challenges and opportunities in precision irrigation decision-support systems for center pivots. Environmental Research Letters 16(5) 053003.

[61]

Zhao, W., Li, J., Yang, R. and Li, Y. (2017). Crop yield and water productivity responses in management zones for variable-rate irrigation based on available soil water holding capacity. Transactions of the ASABE 60(5) 1659–1667.

Full article

Open access article under the CC BY license.

Keywords

Ordered multinomial Precision agriculture Bayesian

Funding

This work was partially funded by the US-Israel Binational Agricultural Research and Development Fund grant IS-5218-19.

Metrics

since December 2021

96 Article info views	33 Full article views
23 PDF downloads	10 XML downloads

RSS

Authors

Abstract

References

Export citation

Copy and paste formatted citation

Download citation in file