Research on water and fertilizer irrigation system of tea plantation

• Main Authors: Xuetao Jia, Ying Huang, Yanhua Wang, Daozong Sun
• Format: Article
• Language: English
• Published: SAGE Publishing 2019-03-01
• Series: International Journal of Distributed Sensor Networks
• Online Access: https://doi.org/10.1177/1550147719840182

In this article, water and fertilizer irrigation system of tea plantation was developed to ensure stable yield, quality of tea, and appropriate irrigation and fertilization. Wireless sensor network node and gateway node are designed and deployed in the tea plantation for collecting soil moisture in real time. Each of the sensor nodes was composed of a STM32F103ZET6 microprocessor and a CC2420 transceiver module. Microcontroller S3C2410X is the kernel of hardware platform in the gateway nodes. All data were transmitted by wireless sensor network. The images of the tea leaves are collected by the high-speed camera loaded onto the unmanned aerial vehicle for analysis of the tea deficiency. The monitoring system is mainly used to display the humidity information about each node, the switch status of the water pump, the battery valve, and so on. It is convenient for user to manage the tea plantation. System configuration and parameter modification can be configured through the monitoring system. The dynamic time warping algorithm is used to judge the abnormal situation of the system. Diamond deployment is used in the network, and the networking experiments were conducted comparing with random deployment. Result showed that both time delay and congestion of network increased as the network scale varied from 5, 10, and 15 to 20 nodes. The topology stabilization time gets prolonged simultaneously. Packet loss rate decreased while data transmission interval varied from 10, 20, and 30 to 40 s. Packet loss rate values in diamond deployment are lower than those in random deployment. In order to improve the accuracy of tea deficiency detection, data fusion technology was adopted. A block-based histogram is employed and similarity distance is adopted to confirm the diagnosis of absence of one or more nutrients of tea. The image information and the spectral information are acquired. The principal component factor is extracted and input into the back propagation neural network to judge the quality of the tea. The number of principal component factors of image information and spectral information is set to six and three; the overall recognition rate reached 97.8%. Therefore, the level of the system abnormality can be determined by the dynamic time warping distance. Test results indicate that it is possible to accurately determine whether tea is deficient and accurately determine whether the system data acquisition is abnormal.