QuinuaSmartApp: A Real-Time Agriculture Precision IoT Cloud Platform to Crops Monitoring

Abstract

IoT networks, cloud-based applications and the use of artificial intelligence models in precision agriculture present an important opportunity to increase production and optimize the use of water resources, which will allow the development of sustainable and responsible agriculture in the face of global food security. In order to provide real-time remote monitoring of quinoa crops, this article proposes and implements an integrated architecture based on sensor networks, drones with multispectral and Lidar cameras and cloud computing-based applications. The system has hardware and software applications that enable Quinoa crop monitoring during the different stages of its growth. Additionally, it comprises weather stations providing real-time data which permits actualising the predictive models that can be used for local climate change projections. The monitoring of the level of humidity in the crop field through soil stations feeds the training database based on machine learning that allows generating the projection of water demand, which allows more efficient and better-planned use of crop water. Additionally, it implements a service of warning messages, attended by experts who are connected to the system in order to provide technical recommendations to help deal with this issue in order to lessen the impact of pests and diseases in the field.