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- ItemIn-Situ Measurement of Fresh Produce Respiration Using a Modular Sensor-Based System(Basel : MDPI, 2020) Keshri, Nandita; Truppel, Ingo; Herppich, Werner B.; Geyer, Martin; Weltzien, Cornelia; Mahajan, Pramod VIn situ, continuous and real-time monitoring of respiration (R) and respiratory quotient (RQ) are crucial for identifying the optimal conditions for the long-term storage of fresh produce. This study reports the application of a gas sensor (RMS88) and a modular respirometer for in situ real-time monitoring of gas concentrations and respiration rates of strawberries during storage in a lab-scale controlled atmosphere chamber (190 L) and of Pinova apples in a commercial storage facility (170 t). The RMS88 consisted of wireless O2 (0% to 25%) and CO2 sensors (0% to 0.5% and 0% to 5%). The modular respirometer (3.3 L for strawberries and 7.4 L for apples) consisted of a leak-proof arrangement with a water-containing base plate and a glass jar on top. Gas concentrations were continuously recorded by the RMS88 at regular intervals of 1 min for strawberries and 5 min for apples and, in real-time, transferred to a terminal program to calculate respiration rates ( RO2 and RCO2 ) and RQ. Respiration measurement was done in cycles of flushing and measurement period. A respiration measurement cycle with a measurement period of 2 h up to 3 h was shown to be useful for strawberries under air at 10 °C. The start of anaerobic respiration of strawberries due to low O2 concentration (1%) could be recorded in real-time. RO2 and RCO2 of Pinova apples were recorded every 5 min during storage and mean values of 1.6 and 2.7 mL kg−1 h−1, respectively, were obtained when controlled atmosphere (CA) conditions (2% O2, 1.3% CO2 and 2 °C) were established. The modular respirometer was found to be useful for in situ real-time monitoring of respiration rate during storage of fresh produce and offers great potential to be incorporated into RQ-based dynamic CA storage system.
- ItemSoil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR)(Basel : MDPI, 2020) Erler, Alexander; Riebe, Daniel; Beitz, Toralf; Löhmannsröben, Hans-Gerd; Gebbers, RobinPrecision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.
- ItemDirect Measurements of the Volume Flow Rate and Emissions in a Large Naturally Ventilated Building(Basel : MDPI, 2020) Janke, David; Yi, Qianying; Thormann, Lars; Hempel, Sabrina; Amon, Barbara; Nosek, Štepán; van Overbeke, Philippe; Amon, ThomasThe direct measurement of emissions from naturally ventilated dairy barns is challenging due to their large openings and the turbulent and unsteady airflow at the inlets and outlets. The aim of this study was to quantify the impacts of the number and positions of sensors on the estimation of volume flow rate and emissions. High resolution measurements of a naturally ventilated scaled building model in an atmospheric boundary layer wind tunnel were done. Tracer gas was released inside the model and measured at the outlet area, using a fast flame ionization detector (FFID). Additionally, the normal velocity on the area was measured using laser Doppler anemometry (LDA). In total, for a matrix of 65 × 4 sensor positions, the mean normal velocities and the mean concentrations were measured and used to calculate the volume flow rate and the emissions. This dataset was used as a reference to assess the accuracy while systematically reducing the number of sensors and varying the positions of them. The results showed systematic errors in the emission estimation up to +97%, when measurements of concentration and velocity were done at one constant height. This error could be lowered under 5%, when the concentrations were measured as a vertical composite sample.
- ItemDetermination of Nutrients in Liquid Manures and Biogas Digestates by Portable Energy-Dispersive X-ray Fluorescence Spectrometry(Basel : MDPI AG, 2021) Horf, Michael; Gebbers, Robin; Vogel, Sebastian; Ostermann, Markus; Piepel, Max-Frederik; Olfs, Hans-WernerKnowing the exact nutrient composition of organic fertilizers is a prerequisite for their appropriate application to improve yield and to avoid environmental pollution by over-fertilization. Traditional standard chemical analysis is cost and time-consuming and thus it is unsuitable for a rapid analysis before manure application. As a possible alternative, a handheld X-ray fluorescence (XRF) spectrometer was tested to enable a fast, simultaneous, and on-site analysis of several elements. A set of 62 liquid pig and cattle manures as well as biogas digestates were collected, intensively homogenized and analysed for the macro plant nutrients phosphorus, potassium, magnesium, calcium, and sulphur as well as the micro nutrients manganese, iron, copper, and zinc using the standard lab procedure. The effect of four different sample preparation steps (original, dried, filtered, and dried filter residues) on XRF measurement accuracy was examined. Therefore, XRF results were correlated with values of the reference analysis. The best R2s for each element ranged from 0.64 to 0.92. Comparing the four preparation steps, XRF results for dried samples showed good correlations (0.64 and 0.86) for all elements. XRF measurements using dried filter residues showed also good correlations with R2s between 0.65 and 0.91 except for P, Mg, and Ca. In contrast, correlation analysis for liquid samples (original and filtered) resulted in lower R2s from 0.02 to 0.68, except for K (0.83 and 0.87, respectively). Based on these results, it can be concluded that handheld XRF is a promising measuring system for element analysis in manures and digestates.
- ItemHyperspectral Imaging Tera Hertz System for Soil Analysis : Initial Results(Basel : MDPI, 2020) Dworak, Volker; Mahns, Benjamin; Selbeck, Jörn; Gebbers, Robin; Weltzien, CorneliaAnalyzing soils using conventional methods is often time consuming and costly due to their complexity. These methods require soil sampling (e.g., by augering), pretreatment of samples (e.g., sieving, extraction), and wet chemical analysis in the laboratory. Researchers are seeking alternative sensor-based methods that can provide immediate results with little or no excavation and pretreatment of samples. Currently, visible and infrared spectroscopy, electrical resistivity, gamma ray spectroscopy, and X-ray spectroscopy have been investigated extensively for their potential utility in soil sensing. Little research has been conducted on the application of THz (Tera Hertz) spectroscopy in soil science. The Tera Hertz band covers the frequency range between 100 GHz and 10 THz of the electromagnetic spectrum. One important feature of THz radiation is its correspondence with the particle size of the fine fraction of soil minerals (clay < 2 µm to sand < 2 mm). The particle size distribution is a fundamental soil property that governs soil water and nutrient content, among other characteristics. The interaction of THz radiation with soil particles creates detectable Mie scattering, which is the elastic scattering of electromagnetic waves by particles whose diameter corresponds approximately to the wavelength of the radiation. However, single-spot Mie scattering spectra are difficult to analyze and the understanding of interaction between THz radiation and soil material requires basic research. To improve the interpretation of THz spectra, a hyperspectral imaging system was developed. The addition of the spatial dimension to THz spectra helps to detect relevant features. Additionally, multiple samples can be scanned in parallel and measured under identical conditions, and the high number of data points within an image can improve the statistical accuracy. Technical details of the newly designed hyperspectral imaging THz system working from 250 to 370 GHz are provided. Results from measurements of different soil samples and buried objects in soil demonstrated its performance. The system achieved an optical resolution of about 2 mm. The sensitivity of signal damping to the changes in particle size of 100 µm is about 10 dB. Therefore, particle size variations in the µm range should be detectable. In conclusion, automated hyperspectral imaging reduced experimental effort and time consumption, and provided reliable results because of the measurement of hundreds of sample positions in one run. At this stage, the proposed setup cannot replace the current standard laboratory methods, but the present study represents the initial step to develop a new automated method for soil analysis and imaging.
- ItemIoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato(Basel : MDPI, 2020) Rezvani, Sayed Moin-eddin; Abyaneh, Hamid Zare; Shamshiri, Redmond R.; Balasundram, Siva K.; Dworak, Volker; Goodarzi, Mohsen; Sultan, Muhammad; Mahns, BenjaminOptimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants’ comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.