Performance Modelling of IoT in Smart Agriculture

Nowadays, IoT is being used in several applications, such as smart cities, health care and innovating agriculture and other applications. Moreover, the evolution of IoT technologies such as LoRaWAN, SIGFOX, ZigBee, and others drives the industry to convert their traditional systems to IoT systems due to the need to achieve better performance. One of the most challenging applications in an IoT system is smart agriculture due to the need for high energy and water consumption to reach the desired crop yield. Several related works in different countries tried to solve this challenge for a better water consumption in irrigation process to get the maximum crop yield production. In this paper, we used artificial intelligence and deep learning to predict the crop yield and the needed parameters of the soil to achieve the best performance with minimum power consumption. Also, we used convolutional neural networks to detect the crop deficit needed for real-time monitoring. The performed simulation has shown that the proposed model outperforms other related works and will positively increase both saving resources and the performance.