WiFi is starting to be adopted in agricultural settings because of the increasing use of data-centric farming devices and applications. Some of these applications require high data rates that other radio standards cannot offer, for example, realtime yield mapping, drone-based image upload and viewing and video monitoring. It is therefore desirable for farming wireless sensor networks (WSNs) to also utilise available on-farm WiFi networks. However, little information is available on WiFi signal propagation in these environments, as agricultural WSNs have traditionally been based on other radio standards. Therefore, the 2.4GHz WiFi signal propagation characteristics in real outdoor agricultural cropping environments were investigated using infield data loggers. Three distinct farming environments were studied, (i) bare fields, (ii) cotton fields, and (iii) ponded-water rice fields. We studied the effects of (i) weather conditions, (ii) crop growth and (iii) water depth on signal strength across an entire growing season. We also studied the range of reliable data transfer in each environment as a function of height of the logger WiFi antenna above the crop. Crop growth status was found to be much more significant in determining signal strength than weather conditions, with signal strength declining by 8dB over the season in a cotton field, and by 20dB in a rice field. In rice and cotton crops, provided the radios remain 20cm above the crop canopy, ranges in excess of 1km were measured. Significantly greater ranges are predicted if the antenna is more than 40cm above the top of the crop. Regression models were fitted to the measurements to allow predictions and recommendations, with correlation coefficient of determination (R2) values of better than 0.9 in most cases. Radio path loss exponents depended on the environment, and were typically between 3 and 5.

A new agricultural sensor data logging platform (WiField) is described, based on IEEE 802.11 WiFi technology. It is low-cost, low-power, and achieves long (>2km) range communication to on-farm WiFi access points. WiFi is an attractive choice for this application because of the wide range of other devices that increasingly need internet access in farming systems. The WiField devices include interfaces for many sensor types; weather, infrastructure (tank and irrigation water levels), and soil status sensing. The interfaces and example corresponding sensors include SDI-12 (capacitive soil moisture probes), soil tension (matric potential), analog voltage and current, UART (water depth sensing using ultrasonic transducers with a digital interface), RS-422 (integrated weather stations), one-wire (DS18B20 temperature sensors) and pulse (flow meters, wind and rain sensors). It integrates solar charging of rechargeable batteries, or can be run off disposable batteries for at least an entire growing season due to design choices that minimize power consumption. It is designed to upload data to cloud services in real-time. The data is then processed in the cloud and interactive graphs are produced, so multiple users can access up-to-date information in order to make optimized, timely farming decisions. The use of the WiField devices in a cotton farming operation is described, for scheduling irrigations and determining crop water use through the soil profile.

This study integrates measured soil moisture sensor data, a remotely sensed crop vegetation index, and weather data to train models, in order to predict future soil moisture. The study was carried out on a cotton farm, with wireless soil moisture monitoring equipment deployed across five plots. Lasso, Decision Tree, Random Forest and Support Vector Machine modeling methods were trialled. Random Forest models gave consistently good results (mean 7-day prediction error from 8.0 to 16.9 kPA except in one plot with malfunctioning sensors). Linear regression with two of the most important predictor variables was not as accurate, but allowed extraction of an interpretable model. The system was implemented in Google Cloud Platform and a model was trained continuously through the season. An online irrigation dashboard was created showing previous and forecast soil moisture conditions, along with weather and normalized difference vegetation index (NDVI). This was used to guide operators in advance of irrigation water needs. The methodology developed in this study could be used as part of a closed-loop sensing and irrigation automation system.