Abstract
The concept of precision agriculture mainly focuses on collecting, processing, and analyzing data of inter- and intra-field variables in a farm so that in minimum resources optimum returns can be obtained from agriculture. This paper proposes a model for precision agriculture that automates data collection of intra-field variables such as soil moisture, sunlight exposure, and relative humidity across farms through a network of sensors. The proposed model provides actionable information to the farmers for their crops through which they can optimize irrigation and nutrient management, hence saving costs on agricultural inputs. This also ensures an environmentally sustainable approach to agriculture by being resourceful. The hardware-level implementation of the proposed model is also suggested so that the model can be implemented and deployed easily. At last, the paper also explores various challenges in the adoption of the proposed model along with improvements that can be made in the future as the model scales.
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Tomar, J.S., Mishra, P., Gupta, A., Meena, K.B., Tyagi, V. (2022). A Proposed Model for Precision Agriculture. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_35
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DOI: https://doi.org/10.1007/978-3-031-12641-3_35
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