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Monitoring and control of environmental parameters to pbkp_redict growth in citrus crops using Machine Learning

Abstract:

Currently, in the citrus sector around the world, there are challenges such as the need to increase the efficiency, yield, and quality of the harvest; For this reason, technological alternatives are investigated that allow a reduction in production costs to be achieved. This article presents the development of a system based on artificial neural networks, that uses data captured through sensors, offering recommendations to farmers to improve decision-making. This allows automating in run-Time the monitoring and control of processes in the citrus sector, through a mobile platform and a pbkp_redictive system, to achieve higher production performance. With the system, it is possible to pbkp_redict the growth of a plant, after a certain time, and perceive when the plant reaches maturity. Together with the monitoring system, it is possible to guide the growth of the plant in a suitable direction according to the characteristics of the plant and the climatic expectations of the seasons.