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An Application with Jetson Nano for Plant Stress Detection and On-field Spray Decision

Increasing field productivity is not just a financial need, but also a social issue. Several technologies converge to promote food production and, in this context, the fog computing paradigm can support the development of solutions for precision agriculture. This paper proposes an application of the Jetson Nano device, embedded in an agricultural spraying implement. This device supports the decision on irrigation activity, based on data collected by sensors distributed in the field. The sensors read information about the plant’s stress level from electrical signals and the Jetson Nano enables real-time analysis, through machine learning algorithms, to manage the product spray rate, according to the condition of the crop. Initial studies validated the proposed solution on an experimental basis, showing that the device can be an alternative for this purpose, since it can be used efficiently in machine learning tasks from data collected by the sensors. The experiment also highlighted some limitations of the proposed solution, such as the importance of observing the conditions of the system as a whole, its context and environment, in order to improve performance in spraying process.