Root Zone Sensors for Irrigation Management in Intensive Agriculture

Crop irrigation uses more than 70% of the world’s water, and thus, improving irrigation efficiency is decisive to sustain the food demand from a fast-growing world population. This objective may be accomplished by cultivating more water-efficient crop species and/or through the application of efficient irrigation systems, which includes the implementation of a suitable method for precise scheduling. At the farm level, irrigation is generally scheduled based on the grower’s experience or on the determination of soil water balance (weather-based method). An alternative approach entails the measurement of soil water status. Expensive and sophisticated root zone sensors (RZS), such as neutron probes, are available for the use of soil and plant scientists, while cheap and practical devices are needed for irrigation management in commercial crops. The paper illustrates the main features of RZS’ (for both soil moisture and salinity) marketed for the irrigation industry and discusses how such sensors may be integrated in a wireless network for computer-controlled irrigation and used for innovative irrigation strategies, such as deficit or dual-water irrigation. The paper also consider the main results of recent or current research works conducted by the authors in Tuscany (Italy) on the irrigation management of container-grown ornamental plants, which is an important agricultural sector in Italy

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Research on Online Moisture Detector in Grain Drying Process Based on V/F Conversion

An online resistance grain moisture detector is designed, based on the model of the relationship between measurement frequency and grain moisture and the nonlinear correction method of temperature. The detector consists of lower computer, the core function of which is the sensing of grain resistance values which is based on V/F conversion, and upper computer, the core function of which is the conversion of moisture and frequency and the nonlinear correction of temperature. The performance of the online moisture detector is tested in a self-designed experimental system; the test and analysis results indicate that the precision and stability of the detector can reach the level of the similar products, which can be still improved

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Moisture Sensing in Baled Crops

This dissertation is comprised of three papers. The first paper describes in detail a planar dielectric probe design using finite element analysis to determine sensing range and efficiency. The probe is subsequently connected to a Keysight impedance analyzer to measure dielectric properties of raw cotton at controlled levels of moisture content, compressed densities, and source frequency sweeps. Sensitivity to compositional differences such as turnout (lint vs seed) and variety is also explored. The response to the different factors is shown graphically and further quantified statistically in the form of a predictive model for the complex permittivity (dielectric constant and loss tangent). The second paper extends the dielectric probe used in the first paper to real-time harvesting on a round-module cotton harvester by leveraging a packaged sensor with embedded impedance measurement circuit and probe all in one mobile unit. A moisture prediction model based on permittivity is developed from lab-measured data and adjusted based on field data collected during cotton harvesting in Fall of 2014 for pickers and Spring of 2015 for strippers. Verification of the prediction accuracy is performed on field data collected during cotton harvesting in 2016. Sources of variability and sensitivity to confounding factors are investigated and quantified. Finally, plots of diurnal trends of predicted and actual moisture content are overlaid for several days of harvesting. The third paper draws on the first two in applying capacitive-based moisture sensing to large-square bales of alfalfa. A lab characterization is performed on alfalfa over a wide range of moisture contents and densities using both the Keysight impedance analyzer and packaged sensor to measure permittivity. Field data (on-machine permittivity measurements of bales and corresponding ground truth moisture content) is subsequently collected during baling in 2015 and 2016 for alfalfa hay (\u3c30%) and silage (\u3e30%) and used for training and validation of prediction models. In following with the other two papers, sources of variability are discussed and sensitivity to factors quantified. Limitations in sensing range of the packaged sensor lead to multiple prediction models: a simple but limited model restricted to hay and another using modern fitting techniques (feature engineering and artificial neural network) for both hay and silage. Real-time filtering of the prediction signal is investigated using the simple model in light of what seems like mechanically induced oscillations, using a Kalman filter to isolate and remove them while minimizing delay. The real-time prediction signal is finally overlaid with actual moisture content found from core samples of the same bales

Electrical sensor development for the dielectric properties measurement and moisture content estimation of switchgrass and corn stover

The dielectric properties of material play a relevant role when developing moisture content sensors of agricultural products. However, little is known about the permittivity of switchgrass and corn stover in a wider frequency band. Thus, this research goal was to determine their dielectric constant and loss factor at different moisture contents and frequency range of 5 Hz to 13 MHz. Also, an electrical sensor system was developed to predict the amount of water in agricultural products with the material static and in movement. The dielectric properties of switchgrass and corn stover were calculated by measuring their admittance using an impedance analyzer at three different moisture content levels, approximately between 9 and 30.5% and a fixed bulk density of 0.133 g/cm3. Overall, it was observed that the dielectric properties of these materials increased with moisture content but decreased with frequency. Prediction models were developed using the data of a frequency range of 10 kHz to 5 MHz. These models R2\u27s were higher than 0.90 in general; however, the R2 was 0.9811 for a model in a frequency range from 100 kHz to 5 MHz for the loss factor of switchgrass in movement. A sensor system was designed to generate and read a super-imposed multi-frequency signal that was sent and received from a device under test (DUT) with switchgrass. These input and output signals were analyzed to estimate the moisture content at four levels. Overall, the attenuation between the input and output waves increased with moisture. Two models were created to estimate water from switchgrass. They had an R2 of 0.7901 and 0.9976 for the material static and in movement, respectively. The permittivity of switchgrass and corn stover were successfully estimated for the frequency range of 10 kHz to 5 MHz at three moisture levels. Additionally, the developed sensor system was capable of sensing the moisture of switchgrass, but more investigation is necessary. This study helps to comprehend the influence of the electric field at different frequencies on these materials