MACHINE LEARNING APPLICATIONS IN AGRICULTURE 4.0 –A RENAISSANCE IN THE FIELD OF AGRICULTURE, IMPACT ON THE PRECISION AGRICULTURE AND REVOLUTION IN CROP MANAGEMENT
DOI:
https://doi.org/10.48165/iitmjbs.2024.SI.16Keywords:
IoT, precision agriculture, machine learning, artificial intelligence, traditional farming, crop disease detection, weed detection, yield prediction, crop recognition, soil managementAbstract
The paper focuses on the growth of the agricultural sector from the past to current trends and its growth in smart farming. The agriculture sector in India has successfully met the production targets set by the government and has also set new production records in almost all commodities. The paper is a bibliometric analysis of the systematic literature review of precision agriculture, including technical terms like IoT, precision agriculture, machine learning, artificial intelligence, and traditional farming. The paper discusses the various phases in the agricultural management system, including advancements from the past centuries to current trends.
References
1. Singh, A. (2022). Precision Agriculture in India – Opportunities and Challenges. Indian Journal of Fertilisers, 18(4), 308-331.
2. McQueen, R. J., Garner, S. R., Nevill-Manning, C. G., & Witten, I. H. (1995). Applying machine learning to agricultural data. Computers and Electronics in Agriculture, 12(4), 275-293. doi:10.1016/0168-1699(95)98601-9
3. Ghosh, S., & Koley, S. (Year). Machine Learning for Soil Fertility and Plant Nutrient Management using Back Propagation Neural Networks. International Journal on Recent and Innovation Trends in Computing and Communication, 2(2), 292-297. ISSN: 2321-8169
4. Kumar, R., Singh, M. P., Kumar, P., & Singh, J. P. (2015, May). Crop Selection Method to maximize crop yield rate using machine learning technique. 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM). Presented at the 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Avadi, Chennai, India. doi:10.1109/icstm.2015.72254035
5. Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2021). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access: Practical Innovations, Open Solutions, 9, 4843-4873. doi:10.1109/
access.2020.3048415
6. Zhang, P., Yin, Z.-Y., & Jin, Y.-F. (2022). Machine learning-based modelling of soil
properties for geotechnical design: Review, tool development and comparison. Archives of Computational Methods in Engineering. State of the Art Reviews, 29(2), 1229-1245. doi:10.1007/s11831-021-09615-5
7. 7. Retrieved from https://www.financialexpress. com/policy/economy-income-deficiency-68- of-marginal-farmers-also-engaged-in-non farming-activities-3056332/
8. Anastasiou, E., Fountas, S., Voulgaraki, M., Psiroukis, V., Koutsiaras, M., Kriezi, O., ... Gómez-Barbero, M. (2023). Precision farming technologies for crop protection: A meta-analysis. SmartAgriculturalTechnology, 5(100323), 100323. doi:10.1016/j.atech.2023.100323
9. Travlos, I., Mikroulis, A., Anastasiou, E., Fountas, S., Bilalis, D., Tsiropoulos, Z., & Balafoutis, A. (2017). The use of RGB cameras in defining crop development in legumes. Advances in Animal Biosciences, 8(2), 224-
228. doi:10.1017/s2040470017000498 10. Khan, S., Tufail, M., Khan, M. T., Khan, Z. A., Iqbal, J., & Alam, M. (2021). A novel semi-supervised framework for UAV based crop/weed classification. PLOS ONE, 16(5), e0251008. doi:10.1371/journal. pone.0251008
11. Schrader, M. J., Smytheman, P., Beers, E. H., & Khot, L. R. (2022). An open-source low cost imaging system plug-in for pheromone traps aiding remote insect pest population monitoring in fruit crops. Machines, 10(1), 52. doi:10.3390/machines10010052.
12. Gonzalez-Huitron, V., León-Borges, J. A., Rodriguez-Mata, A. E., Amabilis-Sosa, L. E., Ramírez-Pereda, B., & Rodriguez, H. (2021). Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4. Computers and Electronics in Agriculture, 181(105951), 105951. doi:10.1016/j.compag.2020.105951.
13. Conesa-Muñoz, J., Valente, J., Del Cerro, J., Barrientos, A., & Ribeiro, A. (2016). A multi robot sense-act approach to lead to a proper acting in environmental incidents. Sensors (Basel, Switzerland), 16(8), 1269. doi:10.3390/ s16081269.
14. Alam, M., Alam, M. S., Roman, M., Tufail, M., Khan, M. U., & Khan, M. T. (2020, April). Real-time machine-learning based crop/weed detection and classification for variable-rate spraying in precision agriculture. 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE). Presented at the 2020 7th International Conference on Electrical and Electronics Engineering (ICEEE), Antalya, Turkey. doi:10.1109/ iceee49618.2020.9102505.
15. Kunz, C., Weber, J., & Gerhards, R. (2015). Benefits of precision farming technologies for mechanical weed control in soybean and sugar beet—comparison of precision hoeing with conventional mechanical weed control. Agronomy (Basel, Switzerland), 5(2), 130–142. doi:10.3390/agronomy5020130.
16. Zbiciak, A., & Markiewicz, T. (2023). A new extraordinary means of appeal in the Polish criminal procedure: the basic principles of a fair trial and a complaint against a cassatory judgment. Https://Doi.Org/10.33327/AJEE
18-6.2, 6(2), 1–18. doi:10.33327/ajee-18- 6.2-a000209.
17. Utstumo, T., Urdal, F., Brevik, A., Dørum, J., Netland, J., Overskeid, Gravdahl, J. T. (2018). Robotic in-row weed control in vegetables. Computers and Electronics in Agriculture, 154, 36–45. doi:10.1016/j.compag.2018.08.043.
18. Prey, L., von Bloh, M., & Schmidhalter, U. (2018). Evaluating RGB imaging and multispectral active and hyperspectral passive sensing for assessing early plant vigor in winter wheat. Sensors (Basel, Switzerland), 18(9), 2931. doi:10.3390/s18092931.
19. Liu, B., Li, R., Li, H., You, G., Yan, S., & Tong, Q. (2019). Crop/weed discrimination using a field imaging spectrometer system. Sensors (Basel, Switzerland), 19(23), 5154. doi:10.3390/s19235154.
20. Scherrer, B., Sheppard, J., Jha, P., & Shaw, J. A. (2019). Hyperspectral imaging and neural networks to classify herbicide-resistant weeds. Journal of Applied Remote Sensing, 13(4), 1. doi:10.1117/1.jrs.13.044516.
21. Pineda, M., Pérez-Bueno, M. L., & Barón, M. (2022). Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris. Frontiers in Plant Science, 13, 790268. doi:10.3389/
fpls.2022.790268.
22. Zhang, Y., Staab, E. S., Slaughter, D. C., Giles, D. K., & Downey, D. (2012). Automated weed control in organic row crops using hyperspectral species identification and thermal micro-dosing. Crop Protection (Guildford, Surrey), 41, 96–105. doi:10.1016/j.
cropro.2012.05.007.
23. Gokool, S., Mahomed, M., Kunz, R., Clulow, A., Sibanda, M., Naiken, V., & Mabhaudhi, T. (2023). Crop monitoring in smallholder farms using unmanned aerial vehicles to facilitate precision agriculture practices: A scoping review and bibliometric analysis. Sustainability, 15(4), 3557. doi:10.3390/su15043557
24. Radocaj, D., Siljeg, A., Marinović, R., & Jurišić, M. (2023). State of major vegetation indices in precision agriculture studies indexed in Web of Science: A review. Agriculture, 13(3), 707. doi:10.3390/
agriculture13030707
25. Radocaj, D., Plascak, I., & Jurisić, M. (2023). Global Navigation Satellite Systems as state of-the-art solutions in precision agriculture: A review of studies indexed in the Web of Science. Agriculture, 13, 1417. doi:10.3390/ agriculture13071417
26. Kumar, R., Singh, M. P., Kumar, P., & Singh, J. P. (2015). Crop selection method to maximize crop yield rate using machine learning technique. In 5th International Conference on Smart Technologies and Management for Computing, Communication, Controls.
27. Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2021). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access: Practical Innovations, Open Solutions, 9, 4843–4873. doi:10.1109/
access.2020.3048415.