Geng (Frank) Bai
Date of this Version
Nipuna Chamara, A.H.M. 2021. Development of an Internet of Things (IoT) Enabled Novel Wireless Multi Sensor Network for Infield Crop Monitoring (Master's Thesis). Biological Systems Engineering--Dissertations, Theses, and Student Research. Department of Biological Systems Engineering, University of Nebraska, Lincoln.
Multispectral imaging systems on satellite, aerial, and ground platforms are used commonly to monitor in-field crops in precision agriculture by farmers and researchers. Limited spatial and temporal resolution and weather dependence of the data collection are two main disadvantages of these methods. In-field sensor networks can continuously monitor environmental and plant physiological parameters by leveraging low-power computation and long-range communication technologies. We built and tested a novel sensor network equipped with soil moisture, multispectral and RGB imaging sensors in an experimental soybean field at Eastern Nebraska Research and Extension Center, NE, USA. 10 down-looking and 1 up-looking sensor node were set up at the experimental site in the 2020 growing season. Each down-looking sensor node utilized an 18-channel multispectral sensor, a soil moisture sensor, and an RGB imager to collect canopy reflectance, soil volumetric water content (VWC), and canopy images every 20 minutes. The up-looking sensor node measured the solar radiation using a multi-spectral sensor every 10 minutes. The setup allowed us to calculate the spectral reflectance of the soybean canopy under changing weather conditions. The sensor nodes were solar-powered and integrated into a Low Power, wide area network (LoRaWAN) through a LoRa gateway, which was connected to the internet via Wi-Fi. Captured images were saved on SD cards while other parameters were uploaded to cloud data storage for real-time processing and visualization. The result shows that the sensor network can plot canopy Normalized difference vegetation index (NDVI) and soil VWC continuously throughout the growing season. NDVI values of the irrigated and rainfed soybean plots showed a significant difference. The coefficient of determination or the R squared value was 0.8701 between the GreenSeeker Handheld Crop Sensor and the IoT enabled sensor node NDVI value. This research verified that NDVI value is not constant throughout the day. Daily NDVI variation has two peaks between 10.00 -11.00 am and 1:00 - 2:00 pm. This sensor network could help users to estimate the crop growth parameters and irrigation requirements in a real-time fashion. Further, the diurnal NDVI tracking with an in-field NDVI sensor node has the potential to improve the NDVI value-based variable-rate fertigation unit efficiency improvement.
Advisor Yufeng Ge