Advanced Study of Accuracy Farming Procedures with Artificial Intelligence
DOI:
https://doi.org/10.12723/mjs.sp2.6Keywords:
Artificial Intelligence, Precision Agriculture, Agriculture, Farming, Information, Machine LearningAbstract
The idea of smart farming is being revolutionized worldwide by precision agriculture. The secret to generating the highest crop production is smart and precision agriculture. Globally, themajority ofthe agrarian society is illiterate and ignorant about intelligent farming. Our study serves as a link between computer scientists and researchers in the agriculture field. This study focuses on crop suggestions that take into account chemical and climatic factors. The nutrients that should remain additional to the soil to progress its quality are recommended by AI to farmers. Numerous sectors have been
changed by AI and machine learning (ML), and the agronomy manufacturing is alsoseeing the same trend. To make it simpler to monitor farmers' crop and soil health, businesses are creating a number of technologies.
References
T. Campbell, K. Dixon, K. Dods, P. Fearns and R. Handcock, "Machine Learning Regression Model for Predicting Honey Harvests", Agriculture, vol. 10, no. 4, p. 118, 2020.
S. Kresova and S. Hess, "Identifying the Determinants of Regional Raw Milk Prices in Russia Using Machine Learning", Agriculture, vol. 12, no. 7, p. 1006, 2022.
M. Niazian and G. Niedbała, "Machine Learning for Plant Breeding and Biotechnology", Agriculture, vol. 10, no. 10, p. 436, 2020
G. Oluwatosin, O. Adeyolanu, O. Idowu and J. Adediran, “Planning sustainable soil management under intensified
crop production system in Nigeria: an ecosystem approach”, Global Journal of Agricultural Sciences, vol. 4, no. 1, 2005.
V. Pham, D. Weindorf and T. Dang, “Soil profile analysis using interactive visualizations, machine learning, and deep learning”, Computers and Electronics in Agriculture, vol. 191, p. 106539, 2021.
P. Psirofonia, V. Samaritakis, P. Eliopoulos and I. Potamitis, “Use of Unmanned Aerial Vehicles for Agricultural Applications with Emphasis on Crop Protection: Three Novel Case - studies”, International Journal of Agricultural
Science and Technology, vol. 5, no. 1, pp. 30-39, 2017.
E. Ropelewska, “The Application of Machine Learning for Cultivar Discrimination of Sweet Cherry Endocarp”, Agriculture, vol. 11, no. 1, p. 6, 2020.
C. Wang, Y. Gao, A. Aziz and G. Ogunmola, “Agricultural Disaster Risk Management and Capability Assessment Using Big Data Analytics”, Big Data, vol. 10, no. 3, pp. 246-261, 2022.
M. Yang and S. Cho, “High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning”, Agriculture, vol. 11, no. 10, p. 1010, 2021.
M. Abu-hashim, E. Mohamed and A. Belal, “Identification of potential soil water retention using hydric numerical model at arid regions by landuse changes”, International Soil and Water Conservation Research, vol. 3, no. 4, pp.
-315, 2015.
M. Ahmed, “Report on Smart Irrigation System using Fuzzy Logic”, Global Sci-Tech, vol. 11, no. 1, p. 31, 2019.
M. Aitkenhead, A. McDonald, J. Dawson, G. Couper, R. Smart, M. Billett, D. Hope and S. Palmer, “A novel method for training neural networks for time-series prediction in environmental systems”, Ecological Modelling, vol. 162, no. 1-2, pp. 87-95, 2003.
M. Aitkenhead, A. McDonald, J. Dawson, G. Couper, R. Smart, M. Billett, D. Hope and S. Palmer, “A novel method
for training neural networks for time-series prediction in environmental systems”, Ecological Modelling, vol. 162, no. 1-2, pp. 87-95, 2003.
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