Optimizing Soil Fertility through Machine Learning: Enhancing Agricultural Productivity and Sustainability

Öz

Nowadays, the sustainability of agriculture and food security have an increasing importance on soil fertility. Soil fertility is defined as the capacity of a land to grow crops and its potential crop productivity. However, factors such as increasing population, climate change, land use changes and environmental pollution threaten soil fertility. These threats can result in problems such as erosion, soil salinisation and organic matter depletion. Soil fertility is critical for the long-term health of agriculture and food security. Artificial intelligence techniques used to determine and manage soil fertility analyse the minerals present in the soil as well as other factors. These analyses assess the amount of minerals present in the soil, the availability of nutrients and important parameters such as pH. This information guides farmers in selecting the most appropriate crops. Furthermore, the integration of Internet of Things (IoT) technologies allows real-time monitoring of minerals and nutrients in the soil and optimising irrigation and fertilisation processes based on this data. These developments have the potential to improve soil fertility management and increase agricultural productivity.

Anahtar Kelimeler

• Soil Fertility
• Machine Learning
• Precision Agriculture
• Artificial Intelligence
• Sustainable Agriculture

Kaynakça

1. Acar, H., Özerdem, M. S., Acar, E. (2020). Soil moisture inversion via semiempirical and machine learning methods with full-polarization radarsat-2 and polarimetric target decomposition data: A comparative study. IEEE Access, 8, 197896–197907. https://doi.org/10.1109/ACCESS.2020.3035235
2. Aghaabbasi, M., Ali, M., Jasiński, M., Leonowicz, Z., Novák, T. (2023). On hyperparameter optimization of machine learning methods using a Bayesian optimization algorithm to predict work travel mode choice. IEEE Access, 11, 19762-19774.
3. Ahmadi, S. A., Vivar, G., Navab, N., Möhwald, K., Maier, A., Hadzhikolev, H., Brandt, T., Grill, E., Dieterich, M., Jahn, K., Zwergal, A. (2020). Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders. Journal of Neurology, 267(1), 143–152. https://doi.org/10.1007/S00415-020-09931-Z/TABLES/1
4. Ali, N. M., Besar, R., Aziz, N. A. A. (2023). A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation. Bulletin of Electrical Engineering and Informatics, 12(2), 1047–1054. https://doi.org/10.11591/EEI.V12I2.4838
5. Baby, D., Devaraj, S. J., Hemanth, J., Anishin Raj, M. M. (2021). Leukocyte classification based on feature selection using extra trees classifier: atransfer learning approach. Turkish Journal of Electrical Engineering and Computer Sciences, 29(8), 2742–2757. https://doi.org/10.3906/elk-2104-183
6. Bastos, L. M., Rice, C. W., Tomlinson, P. J., Mengel, D. (2021). Untangling soil-weather drivers of daily N2O emissions and fertilizer management mitigation strategies in no-till corn. Soil Science Society of America Journal, 85(5), 1437–1447. https://doi.org/10.1002/SAJ2.20292
7. Bondre, D. A., Santosh Mahagaonkar, M. (2019). Prediction Of Crop Yield And Fertilizer Recommendation Using Machine Learning Algorithms. International Journal of Engineering Applied Sciences and Technology, 4, 371–376. http://www.ijeast.com
8. Breiman, L. (2001). Random forests. Machine Learning, 45(1),5–32. https://doi.org/10.1023/A:1010933404324/METRICS

9. Chen, C., Wang, X., Wu, C., Mafarja, M., Turabieh, H., & Chen, H. (2021). Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine. Electronics 2021, Vol. 10, Page 2115, 10(17), 2115. https://doi.org/10.3390/ELECTRONICS10172115
10. Chu, Z., Yu, J., Hamdulla, A. (2021). Throughput prediction based on extratree for stream processing tasks. Computer Science and Information Systems, 18(1), 1-22.
11. Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., Lawler, J. J. (2007). Random Forests For Classifıcat.on In Ecology. Ecology, 88(11), 2783–2792. https://doi.org/10.1890/07-0539.1
12. D N, V., Choudhary, D. S. (2021). An AI solution for Soil Fertility and Crop Friendliness Detection and Monitoring. International Journal of Engineering and Advanced Technology, 10(3), 172–175. https://doi.org/10.35940/IJEAT.C2270.0210321
13. Ekinci, E. (2022). Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM. Sakarya University Journal of Computer and Information Sciences, 5(1), 121–133. https://doi.org/10.35377/SAUCIS...1070822
14. Escorcia-Gutierrez, J., Gamarra, M., Soto-Diaz, R., Pérez, M., Madera, N., Mansour, R. F. (2022). Intelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques. Agriculture 2022, Vol. 12, Page 977, 12(7), 977. https://doi.org/10.3390/AGRICULTURE12070977
15. Geurts, P., Ernst, D., Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. https://doi.org/10.1007/S10994-006-6226-1/METRICS
16. Hengl, T., Heuvelink, G. B. M., Kempen, B., Leenaars, J. G. B., Walsh, M. G., Shepherd, K. D., Sila, A., MacMillan, R. A., De Jesus, J. M., Tamene, L., Tondoh, J. E. (2015). Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLOS ONE, 10(6), e0125814. https://doi.org/10.1371/JOURNAL.PONE.0125814 Henis, Y. (1986). Soil microorganisms, soil organic matter and soil fertility. The Role of Organic Matter in Modern Agriculture, 159–168. https://doi.org/10.1007/978-94-009-4426-8_7
17. Hota, S., Mishra, V., Mourya, K. K., Saikia, U. S., Ray, S. K. (2022). Fertility capability classification (FCC) of soils of a lower Brahmaputra valley area of Assam, India. Environment Conservation Journal, 23(3), 192–201. https://doi.org/10.36953/ECJ.10462244
18. Jin, H., Hao, X., Yang, Y., Liu, Y., Sun, P. (2023). Classification of soil by laser-induced breakdown spectroscopy combined with PCA-RF. Https://Doi.Org/10.1117/12.2651371, 12558, 48–53. https://doi.org/10.1117/12.2651371
19. Kalyani, N. L., Prakash, K. B. (2020). Soil Synthesis and Identification of Nitrogen percentage in Soil using Machine learning algorithms and Augmented Reality -- A Typical review. Int. J. Emerg. Trends Eng. Res., 8(9), 5501–5505.
20. Khan, M. Y., Qayoom, A., Nizami, M. S., Siddiqui, M. S., Wasi, S., Raazi, S. M. K. U. R. (2021). Automated Prediction of Good Dictionary EXamples (GDEX): A Comprehensive Experiment with Distant Supervision, Machine Learning, and Word Embedding‐Based Deep Learning Techniques. Complexity, 2021(1), 2553199.
21. Koren, M., Koren, O., Peretz, O. (2024). Weighted distance classification method based on data intelligence. Expert Systems, 41(2), e13486. https://doi.org/10.1111/EXSY.13486
22. Li, L., Zhang, Y., Zhao, Y. (2008). K-Nearest Neighbors for automated classification of celestial objects. Science in China, Series G: Physics, Mechanics and Astronomy, 51(7), 916–922. https://doi.org/10.1007/S11433-008-0088-4/METRICS
23. Majstorović, H., Garalejić, B., Sudimac, M., Pavlović, M., Čolović, V. (2022). Parametri Plodnost Zemljišta U Funkciji Tipa Zemljišta Na Teritoriji Grada Pančeva 2022зборник Биодиверзитет. 395–400. https://doi.org/10.46793/SBT27.395M
24. Nadarajah, K. K. (2022). Soil Fertility and Sustainable Agriculture. Advances in Agricultural and Industrial Microbiology: Volume 1: Microbial Diversity and Application in Agroindustry, 1–16. https://doi.org/10.1007/978-981-16-8918-5_1/FIGURES/1
25. Nyakuri, J. P., Bizimana, J., Bigirabagabo, A., Kalisa, J. B., Gafirita, J., Munyaneza, M. A., Nzemerimana, J. P. (2022). IoT and AI Based Smart Soil Quality Assessment for Data-Driven Irrigation and Fertilization. American Journal of Computing and Engineering, 5(2), 1–14. https://doi.org/10.47672/AJCE.1232
26. Parent, L. E., Jamaly, R., Atucha, A., JeanneParent, E., Workmaster, B. A., Ziadi, N., Parent, S. É. (2021). Current and next-year cranberry yields predicted from local features and carryover effects. PLOS ONE, 16(5), e0250575. https://doi.org/10.1371/JOURNAL.PONE.0250575
27. Patil, P. (2022). Soil Health Prediction Using Supervised Machine Learning Technique. International Journal for Research in Applied Science and Engineering Technology, 10(1), 1493–1499. https://doi.org/10.22214/IJRASET.2022.40081
28. Patzel, N., Sticher, H., Karlen, D. L. (2000). Soil Fertility — Phenomenon and Concept. Journal of Plant Nutrition and Soil Science, 163(2), 129–142. https://doi.org/https://doi.org/10.1002/(SICI)1522-2624(200004)163:2<129::AID-JPLN129>3.0.CO;2-D
29. Peng Chunjian. (2018). Soil fertility detection method - Patent.
30. Radočaj, D., Jurišić, M., Gašparović, M. (2022). The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture. Remote Sensing 2022, Vol. 14, Page 778, 14(3), 778. https://doi.org/10.3390/RS14030778
31. Raikwal, J. S., Saxena, K. (2012). Performance Evaluation of SVM and K-Nearest Neighbor Algorithm over Medical Data set. International Journal of Computer Applications, 50(14), 975–8887.
32. Rajamanickam, J., Mani, S. D. (2021). Kullback chi square and Gustafson Kessel probabilistic neural network based soil fertility prediction. Concurrency and Computation: Practice and Experience, 33(24), e6460. https://doi.org/10.1002/CPE.6460
33. Raman, P., Chelliah, B. J. (2023). Enhanced reptile search optimization with convolutional autoencoder for soil nutrient classification model. PeerJ, 11, e15147. https://doi.org/10.7717/PEERJ.15147/SUPP-1
34. Rauter, S., Tschuchnigg, F., Jaksa, M., Liu, Z. (2021). CPT Data Interpretation Employing Different Machine Learning Techniques. Geosciences 2021, Vol. 11, Page 265, 11(7), 265. https://doi.org/10.3390/GEOSCIENCES11070265
35. Sarkar, U., Banerjee, G., Ghosh, I. (2022). A Machine Learning Model for Estimation of Village Level Soil Nutrient Index. Indian Journal of Science and Technology, 15(36), 1815–1822. https://doi.org/10.17485/IJST/V15I36.851
36. Shakeel, N., Baig, F., Saddiq, M. A. (2019). Modeling Commuter’s Sociodemographic Characteristics to Predict Public Transport Usage Frequency by Applying Supervised Machine Learning Method. Transport Technic and Technology, 15(2), 1–7. https://doi.org/10.2478/TTT-2019-0005
37. Solomon, B. D. (2023). Soil fertility. In Dictionary of Ecological Economics: Terms for the New Millennium (p. 498). John Wiley & Sons, Ltd. https://doi.org/10.4337/9781788974912.S.46
38. Sunori, S. K., Negi, P. B., Garia, P., Arora, S., Lohani, M. C., Mittal, A., Juneja, P. (2022). Design of ANN Based Classifiers for Soil Fertility of Uttarakhand. 2022 3rd International Conference for Emerging Technology, INCET 2022. https://doi.org/10.1109/INCET54531.2022.9825273
39. Suruliandi, A., Mariammal, G., Raja, S. P. (2021). Crop prediction based on soil and environmental characteristics using feature selection techniques. Mathematical and Computer Modelling of Dynamical Systems, 27(1), 117–140. https://doi.org/10.1080/13873954.2021.1882505
40. Swetha, A. J., Kalyani, G., Kirananjali, B. (2023). Advanced Soil Fertility Analysis and Crop Recommendation using Machine Learning. 7th International Conference on Trends in Electronics and Informatics, ICOEI 2023 - Proceedings, 1035–1039. https://doi.org/10.1109/ICOEI56765.2023.10125748
41. Talasila, V., Madhubabu, K., Mahadasyam, M. C., Atchala, N. J., Kande, L. S. (2020). The Prediction of Diseases Using Rough Set Theory with Recurrent Neural Network in Big Data Analytics. International Journal of Intelligent Engineering and Systems, 13(5). https://doi.org/10.22266/ijies2020.1031.02
42. Trontelj Ml, J., Chambers, O. (2021). Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method. Sensors 2021, Vol. 21, Page 4208, 21(12), 4208. https://doi.org/10.3390/S21124208
43. Varshitha, D. N., Choudhary, S. (2022). An artificial intelligence solution for crop recommendation. Indonesian Journal of Electrical Engineering and Computer Science, 25(3), 1688–1695. https://doi.org/10.11591/ijeecs.v25.i3.pp1688-1695
44. Zhang, M., Shi, W., Xu, Z. (2020). Systematic comparison of five machine-learning models in classification and interpolation of soil particle size fractions using different transformed data. Hydrology and Earth System Sciences, 24(5), 2505–2526. https://doi.org/10.5194/HESS-24-2505-2020
45. Zhaorong, L., Rong, Z., Jie, S. (2018). Soil fertility detecting method, system, electronic device and storage medium.