Improving Soil Characteristics Using Machine Learning in Different Environment
Keywords:
Soil types, Organic matter content, Naïve bayes, KNN, SVM, Random Forest.Abstract
Given that soil composition influences nutrient cycling, biodiversity support, and water regulation, it is critical to the ecosystem's health. It reduces greenhouse gas emissions by acting as a carbon sink and storing organic carbon. Maintaining arable land, preventing sedimentation in water bodies, and managing erosion are all significantly influenced by the texture and structure of the soil. Furthermore, because some soil constituents enhance the quality of the soil and water by absorbing and digesting pollutants, soil composition has an impact on pollution remediation. It is imperative to acknowledge the significance of soil content in order to sustain ecosystems and apply sustainable land management techniques. Research is mainly focused on evaluating precision, recall, true positive rate, and F-measure in order to forecast the Organic Matter Content in soil, such as that found in homes, farms, and forests. Machine learning methods like Naive Bayes, KNN, SVM, and Random Forest are employed in this study. The outcomes demonstrate that Random Forest outperforms other algorithms in the prediction of soil organic matter content..
Downloads
References
Bisgaard, Søren, and Murat Kulahci. 2011. Time Series Analysis and Forecasting by Example. John Wiley & Sons.
Box, George E. P., and Norman R. Draper. 2007. Response Surfaces, Mixtures, and Ridge Analyses. John Wiley & Sons.
Forkuo, Gloria S., Amanda N. Nieman, Revathi Kodali, Nicolas M. Zahn, Guanguan Li, M. S. Rashid Roni, Michael Rajesh Stephen, et al. 2018. “A Novel Orally Available Asthma Drug Candidate That Reduces Smooth Muscle Constriction and Inflammation by Targeting GABA Receptors in the Lung.” Molecular Pharmaceutics 15 (5): 1766–77.
Goos, Peter, and Bradley Jones. 2011. Optimal Design of Experiments: A Case Study Approach. John Wiley & Sons.
Li, Dan-Ping, Si-Jie Cheng, Peng-Fei Cheng, Jian-Qiang Wang, and Hong-Yu Zhang. n.d. A Novel Financial Risk Assessment Model for Companies Based on Heterogeneous Information and Aggregated Historical Data. Infinite Study.
Liu, Jiangang, Zhenjiang Zhou, and Bo Li. 2024. Remote Sensing for Field-Based Crop Phenotyping. Frontiers Media SA.
Lookman, Turab, Francis J. Alexander, and Krishna Rajan. 2015. Information Science for Materials Discovery and Design. Springer.
Mitran, Tarik, Ram Swaroop Meena, and Abhishek Chakraborty. 2020. Geospatial Technologies for Crops and Soils. Springer Nature.
Myers, Raymond H., Douglas C. Montgomery, and Christine M. Anderson-Cook. 2016. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. John Wiley & Sons.
Reichert, P., Dietrich Borchardt, Mogens Henze, Wolfgang Rauch, P. Shanahan, Laszlo Somlyody, and Peter A. Vanrolleghem. 2001. River Water Quality Model. IWA Publishing.
“Sci-Hub.” n.d. Accessed February 18, 2024. https://sci-hub.se/10.1109/CESYS.2018.8723956.
Thenkabail, Prasad. 2018. Remote Sensing Handbook - Three Volume Set. CRC Press.
“Website.” n.d.
Yada, Rickey Y. 2015. Improving and Tailoring Enzymes for Food Quality and Functionality. Elsevier.
R. Jahan, ‘‘Applying naive Bayes classification technique for classification of improved agricultural land soils,’’ Int. J. Res. Appl. Sci. Eng. Technol., vol. 6, no. 5, pp. 189–193, May 2018.
B. B. Sawicka and B. Krochmal-Marczak, ‘‘Biotic components influencing the yield and quality of potato tubers,’’ Herbalism, vol. 1, no. 3, pp. 125–136, 2017.
B. Sawicka, A. H. Noaema, and A. Gáowacka, ‘‘The predicting the size of the potato acreage as a raw material for bioethanol production,’’ in Alternative Energy Sources, B. Zdunek, M. Olszáwka, Eds. Lublin, Poland: Wydawnictwo Naukowe TYGIEL, 2016, pp. 158–172.
B. Sawicka, A. H. Noaema, T. S. Hameed, and B. Krochmal-Marczak, ‘‘Biotic and abiotic factors influencing on the environment and growth of plants,’’ (in Polish), in Proc. Bioróżnorodność Środowiska Znaczenie, Problemy, Wyzwania. Materiały Konferencyjne, Puławy, May 2017. [Online]. Available: https://bookcrossing.pl/ksiazka/321192
R. H. Myers, D. C. Montgomery, G. G. Vining, C. M. Borror, and S. M. Kowalski, ‘‘Response surface methodology: A retrospective and literature survey,’’ J. Qual. Technol., vol. 36, no. 1, pp. 53–77, Jan. 2004.
D. K. Muriithi, ‘‘Application of response surface methodology for optimization of potato tuber yield,’’ Amer. J. Theor. Appl. Statist., vol. 4, no. 4, pp. 300–304, 2015, doi: 10.11648/j.ajtas.20150404.20.
M. Marenych, O. Verevska, A. Kalinichenko, and M. Dacko, ‘‘Assessment of the impact of weather conditions on the yield of winter wheat in Ukraine in terms of regional,’’ Assoc. Agricult. Agribusiness Econ. Ann. Sci., vol. 16, no. 2, pp. 183–188, 2014.
J. R. Olędzki, ‘‘The report on the state of remotesensing in Poland in 2011–2014,’’ (in Polish), Remote Sens. Environ., vol. 53, no. 2, pp. 113–174, 2015.
K. Grabowska, A. Dymerska, K. Poáarska, and J. Grabowski, ‘‘Predicting of blue lupine yields based on the selected climate change scenarios,’’ Acta Agroph., vol. 23, no. 3, pp. 363–380, 2016.
D. Li, Y. Miao, S. K. Gupta, C. J. Rosen, F. Yuan, C. Wang, L. Wang, and Y. Huang, ‘‘Improving potato yield prediction by combining cultivar information and UAV remote sensing data using machine learning,’’ Remote Sens., vol. 13, no. 16, p. 3322, Aug. 2021, doi: 10.3390/rs13163322.
N. Chanamarn, K. Tamee, and P. Sittidech, ‘‘Stacking technique for academic achievement prediction,’’ in Proc. Int. Workshop Smart Info-Media Syst., 2016, pp. 14–17.
W. Paja, K. Pancerz, and P. Grochowalski, ‘‘Generational feature elimination and some other ranking feature selection methods,’’ in Advances in Feature Selection for Data and Pattern Recognition, vol. 138. Cham, Switzerland: Springer, 2018, pp. 97–112.
D. C. Duro, S. E. Franklin, and M. G. Dubé, ‘‘A comparison of pixelbased and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery,’’ Remote Sens. Environ., vol. 118, pp. 259–272, Mar. 2012.
S. K. Honawad, S. S. Chinchali, K. Pawar, and P. Deshpande, ‘‘Soil classification and suitable crop prediction,’’ in Proc. Nat. Conf. Comput. Biol., Commun., Data Anal. 2017, pp. 25–29.
J. You, X. Li, M. Low, D. Lobell, and S. Ermon, ‘‘Deep Gaussian process for crop yield prediction based on remote sensing data,’’ in Proc. AAAI Conf. Artif. Intell., 2017, vol. 31, no. 1, pp. 4559–4565.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
