An Assessment of Crop-Specific Land Cover Predictions Using High-Order Markov Chains and Deep Neural Networks✩
Volume 21, Issue 2 (2023): Special Issue: Symposium Data Science and Statistics 2022, pp. 333–353
Pub. online: 31 March 2023
Type: Computing In Data Science
Open Access
Open Access
✩
The findings and conclusions in this presentation are those of the authors and should not be construed to represent any official USDA or US Government determination or policy. This research was supported in part by the intramural research program of the US Department of Agriculture, National Agriculture Statistics Service.
Received
29 July 2022
29 July 2022
Accepted
23 March 2023
23 March 2023
Published
31 March 2023
31 March 2023
Abstract
High-Order Markov Chains (HOMC) are conventional models, based on transition probabilities, that are used by the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) to study crop-rotation patterns over time. However, HOMCs routinely suffer from sparsity and identifiability issues because the categorical data are represented as indicator (or dummy) variables. In fact, the dimension of the parametric space increases exponentially with the order of HOMCs required for analysis. While parsimonious representations reduce the number of parameters, as has been shown in the literature, they often result in less accurate predictions. Most parsimonious models are trained on big data structures, which can be compressed and efficiently processed using alternative algorithms. Consequently, a thorough evaluation and comparison of the prediction results obtain using a new HOMC algorithm and different types of Deep Neural Networks (DNN) across a range of agricultural conditions is warranted to determine which model is most appropriate for operational crop specific land cover prediction of United States (US) agriculture. In this paper, six neural network models are applied to crop rotation data between 2011 and 2021 from six agriculturally intensive counties, which reflect the range of major crops grown and a variety of crop rotation patterns in the Midwest and southern US. The six counties include: Renville, North Dakota; Perkins, Nebraska; Hale, Texas; Livingston, Illinois; McLean, Illinois; and Shelby, Ohio. Results show the DNN models achieve higher overall prediction accuracy for all counties in 2021. The proposed DNN models allow for the ingestion of long time series data, and robustly achieve higher accuracy values than a new HOMC algorithm considered for predicting crop specific land cover in the US.
Supplementary material
Supplementary MaterialSupplementary materials of the paper entitled “An Assessment of Crop-Specific Land Cover Predictions Using High-Order Markov Chains and Deep Neural Networks”
References
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org.
Bernstein E, Vazirani U (1997). Quantum complexity theory. SIAM Journal on Computing, 26(5): 1411–1473. https://doi.org/10.1137/S0097539796300921
Berthiaume A, Brassard G (1994). Oracle quantum computing. Journal of Modern Optics, 41(12): 2521–2535. https://doi.org/10.1080/09500349414552351
Boryan C, Yang Z, Mueller R, Craig M (2011). Monitoring us agriculture: The US Department of Agriculture, national agricultural statistics service, cropland data layer program. Geocarto International, 26(5): 341–358. https://doi.org/10.1080/10106049.2011.562309
Breiman L (2001). Random forests. Machine Learning, 45(1): 5–32. https://doi.org/10.1023/A:1010933404324
Broughton M, Verdon G, McCourt T, Martinez AJ, Yoo JH, Isakov SV, et al. (2020). Tensorflow quantum: A software framework for quantum machine learning. arXiv preprint: https://arxiv.org/abs/2003.02989.
Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint: https://arxiv.org/abs/1409.1259.
Chollet F, et al. (2015). Keras. https://keras.io
Cirq Developers (2022). Cirq. See full list of authors on Github: https://github.com/quantumlib/Cirq/graphs/contributors.
Dagum L, Menon R (1998). OpenMP: An industry standard API for shared-memory programming. IEEE Computational Science & Engineering, 5(1): 46–55. https://doi.org/10.1109/99.660313
Fokianos K, Kedem B (2003). Regression theory for categorical time series. Statistical Science, 18: 357–376. https://doi.org/10.1214/ss/1076102425
Gibney E (2017). D-wave upgrade: How scientists are using the world’s most controversial quantum computer. Nature, 541(7638): 447–448. https://doi.org/10.1038/541447b
Gupta MM, Qi J (1991). Theory of t-norms and fuzzy inference methods. Fuzzy Sets and Systems, 40(3): 431–450. https://doi.org/10.1016/0165-0114(91)90171-L
Halmy MWA, Gessler PE, Hicke JA, Salem BB (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63: 101–112. https://doi.org/10.1016/j.apgeog.2015.06.015
Heald J (2002). Usda establishes a common land unit. ArcUser Online. https://www.esri.com/news/arcuser/0402/usda.html
Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint: https://arxiv.org/abs/1207.0580.
Hochreiter S, Schmidhuber J (1997). Long short-term memory. Neural Computation, 9(8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hopfield JJ (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences of the United States of America, 79(8): 2554–2558. https://doi.org/10.1073/pnas.79.8.2554
Jacobs PA, Lewis PA (1983). Stationary discrete autoregressive-moving average time series generated by mixtures. Journal of Time Series Analysis, 4(1): 19–36. https://doi.org/10.1111/j.1467-9892.1983.tb00354.x
Jeswal S, Chakraverty S (2019). Recent developments and applications in quantum neural network: A review. Archives of Computational Methods in Engineering, 26(4): 793–807. https://doi.org/10.1007/s11831-018-9269-0
Johnson DM, Mueller R (2021). Pre-and within-season crop type classification trained with archival land cover information. Remote Sensing of Environment, 264: 112576. https://doi.org/10.1016/j.rse.2021.112576
Kingma DP, Ba J (2014). Adam: A method for stochastic optimization. arXiv preprint: https://arxiv.org/abs/1412.6980.
Latour A (1998). Existence and stochastic structure of a non-negative integer-valued autoregressive process. Journal of Time Series Analysis, 19(4): 439–455. https://doi.org/10.1111/1467-9892.00102
Logan JA (1981). A structural model of the higher-order Markov process incorporating reversion effects. The Journal of Mathematical Sociology, 8(1): 75–89. https://doi.org/10.1080/0022250X.1981.9989916
McCulloch WS, Pitts W (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4): 115–133. https://doi.org/10.1007/BF02478259
Miyashita D, Lee EH, Murmann B (2016). Convolutional neural networks using logarithmic data representation. arXiv preprint: https://arxiv.org/abs/1603.01025.
Osman J, Inglada J, Dejoux JF (2015). Assessment of a Markov logic model of crop rotations for early crop mapping. Computers and Electronics in Agriculture, 113: 234–243. https://doi.org/10.1016/j.compag.2015.02.015
Panchi L, Shiyong L (2008). Learning algorithm and application of quantum BP neural networks based on universal quantum gates. Journal of Systems Engineering and Electronics, 19(1): 167–174. https://doi.org/10.1016/S1004-4132(08)60063-8
Parker DC, Manson SM, Janssen MA, Hoffmann MJ, Deadman P (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93(2): 314–337. https://doi.org/10.1111/1467-8306.9302004
Pegram G (1980). An autoregressive model for multilag Markov chains. Journal of Applied Probability, 17(2): 350–362. https://doi.org/10.2307/3213025
Santos ES (1969). Probabilistic Turing machines and computability. Proceedings of the American Mathematical Society, 22(3): 704–710. https://doi.org/10.1090/S0002-9939-1969-0249221-4
Schuld M, Sinayskiy I, Petruccione F (2014). The quest for a quantum neural network. Quantum Information Processing, 13(11): 2567–2586. https://doi.org/10.1007/s11128-014-0809-8
Shannon CE (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3): 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Tong H (1975). Determination of the order of a Markov chain by Akaike’s information criterion. Journal of Applied Probability, 12(3): 488–497. https://doi.org/10.2307/3212863
USDA, FSA (2017). Farm Service Agency (FSA) Common Land Unit (CLU) information worksheet. https://www.fsa.usda.gov/Assets/USDA-FSA-Public/usdafiles/APFO/support-documents/pdfs/clu_infosheet_2017_Final.pdf
Wolfram S (1984). Universality and complexity in cellular automata. Physica D, 10(1–2): 1–35. https://doi.org/10.1016/0167-2789(84)90245-8
Yaramasu R, Bandaru V, Pnvr K (2020). Pre-season crop type mapping using deep neural networks. Computers and Electronics in Agriculture, 176: 105664. https://doi.org/10.1016/j.compag.2020.105664
Yuen C (1975). A note on base-2 arithmetic logic. IEEE Transactions on Computers, 100(3): 325–329. https://doi.org/10.1109/T-C.1975.224216
Zadeh LA (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man and Cybernetics, 3: 28–44. https://doi.org/10.1109/TSMC.1973.5408575
Zhang C, Di L, Lin L, Guo L (2019). Machine-learned prediction of annual crop planting in the US Corn Belt based on historical crop planting maps. Computers and Electronics in Agriculture, 166: 104989. https://doi.org/10.1016/j.compag.2019.104989
Zhao R, Wang S (2021). A review of quantum neural networks: Methods, models, dilemma. arXiv preprint: https://arxiv.org/abs/2109.01840.
