An Agricultural Monitoring System Based on Wireless Sensor and Depth Learning Algorithm

Abstract

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: SimSun; mso-ansi-language: EN-GB; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-GB">The rise and development of the Internet of Things (IoT) have given birth to the frontier technology of the agricultural IoT, which marks the future trend in agriculture and the IoT. The agricultural IoT can be combined with Zigbee, a short-range wireless network technology for monitoring systems, to solve the excessively large planting area and other defects in agricultural production. Meanwhile, the modernization of planting and harvesting has set the stage for deep learning. Unlike the artificial neural network, the deep learning is an important intelligent algorithm, capable of solving many real-world problems. Therefore, this paper probes into the problems of modern automatic agriculture. First, the routing allocation technology and transmission mode were optimized to solve the energy consumption problem. Second, the classification model based on deep learning algorithm was put forward according to the application of the Wireless Sensor Network (WSN) in continuous monitoring of soil temperature and humidity. Despite the lack of upper soil humidity sensor in agriculture, the model can still classify the soil moisture of the nodes, and derive the main soil moisture content. Finally, a solution was presented based on agricultural ZigBee WSN technology. In addition to cheap cost and low power consumption, the solution has the functions of reminding and recognition due to the adoption of artificial intelligence algorithm. Suffice it to say that the solution is a successful attempt to integrate artificial intelligence and sensor technology into agricultural modernization.</span>