Search Results (15)

Measuring canopy height is important for phenotyping as it has been identified as the most relevant parameter for the fast determination of plant mass and carbon stock, as well as crop responses and their spatial variability. In this work, we develop a low-cost tool for measuring plant height proximally based on an ultrasound sensor for flexible use in static or on-the-go mode. The tool was lab-tested and field-tested on crop systems of different geometry and spacings: in a static setting on faba bean (Vicia faba L.) and in an on-the-go setting on chia (Salvia hispanica L.), alfalfa (Medicago sativa L.), and wheat (Triticum durum Desf.). Cross-correlation (CC) or a dynamic time-warping algorithm (DTW) was used to analyze and correct shifts between manual and sensor data in chia. Sensor data were able to reproduce with minor shifts in canopy profile and plant status indicators in the field when plant heights varied gradually in narrow-spaced chia (R² = 0.98), faba bean (R² = 0.96), and wheat (R² = up to 0.99). Abrupt height changes resulted in systematic errors in height estimation, and short-scale variations were not well reproduced (e.g., R² in widely spaced chia was 0.57 to 0.66 after shifting based on CC or DTW, respectively)). In alfalfa, ultrasound data were a better predictor than NDVI (Normalized Difference Vegetation Index) for Leaf Area Index and biomass (R² from 0.81 to 0.84). Maps of ultrasound-determined height showed that clusters were useful for spatial management. The good performance of the tool both in a static setting and in the on-the-go setting provides flexibility for the determination of plant height and spatial variation of plant responses in different conditions from natural to managed systems. Full article

The design of a readily useable technology for routine paddock-scale soil porosity estimation is described. The method is non-contact (proximal) and typically from “on-the-go” sensors mounted on a small farm vehicle around 1 m above the soil surface. This ultrasonic sensing method is unique in providing estimates of porosity by a non-invasive, cost-effective, and relatively simple method. Challenges arise from the need to have a compact low-power rigid structure and to allow for pasture cover and surface roughness. The high-frequency regime for acoustic reflections from a porous material is a function of the porosity ϕ, the tortuosity α_∞, and the angle of incidence θ. There is no dependence on frequency, so measurements must be conducted at two or more angles of incidence θ to obtain two or more equations in the unknown soil properties ϕ and α_∞. Sensing and correcting for scattering of ultrasound from a rough soil surface requires measurements at three or more angles of incidence. A system requiring a single transmitter/receiver pair to be moved from one angle to another is not viable for rapid sampling. Therefore, the design includes at least three transmitter/reflector pairs placed at identical distances from the ground so that they would respond identically to power reflected from a perfectly reflecting surface. A single 25 kHz frequency is a compromise which allows for the frequency-dependent signal loss from a natural rough agricultural soil surface. Multiple-transmitter and multiple-microphone arrays are described which give a good signal-to-noise ratio while maintaining a compact system design. The resulting arrays have a diameter of 100 mm. Pulsed ultrasound is used so that the reflected sound can be separated from sound travelling directly through the air horizontally from transmitter to receiver. The average porosity estimated for soil samples in the laboratory and in the field is found to be within around 0.04 of the porosity measured independently. This level of variation is consistent with uncertainties in setting the angle of incidence, although assumptions made in modelling the interaction of ultrasound with the rough surface no doubt also contribute. Although the method is applicable to all soil types, the current design has only been tested on dry, vegetation-free soils for which the sampled area does not contain large animal footprints or rocks. Full article

Carbon sequestration in soils under agricultural use can contribute to climate change mitigation. Spatial–temporal soil organic carbon (SOC) monitoring requires more efficient data acquisition. This study aims to evaluate the potential of spectral on-the-go proximal measurements to serve these needs. The study was conducted as a long-term field experiment. SOC values ranged between 14 and 25 g kg⁻¹ due to different fertilization treatments. Partial least squares regression models were built based on the spectral laboratory and field data collected with two spectrometers (site-specific and on-the-go). Correction of the field data based on the laboratory data was done by testing linear transformation, piecewise direct standardization, and external parameter orthogonalization (EPO). Different preprocessing methods were applied to extract the best possible information content from the sensor signal. The models were then thoroughly interpreted concerning spectral wavelength importance using regression coefficients and variable importance in projection scores. The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy for SOC monitoring. The use of different spectrometers under varying soil conditions revealed shifts in wavelength importance. Still, our findings on the use of on-the-go spectroscopy for spatial–temporal SOC monitoring are promising. Full article

The use of drones to gather remote data and soil sensors to collect ground information has become a powerful method for agricultural monitoring and analysis. However, integrating data from drone remote sensing and soil sensors in agricultural contexts can be problematic due to variations in spatial and temporal resolutions. Ensuring precise synchronization and calibration is crucial for accurate comparative analysis. The objective of this study was to investigate the strengths and limitations of drone-based remote sensing and on-the-go Veris U3 sensor in agricultural contexts and explore the potential for data fusion. Through a series of field trials, data from drone-based remote sensing and ground-based soil sensing were collected in parallel. These data encompassed a range of factors, including vegetation health (vegetation indices), soil properties such as EC, pH, and optical measurements. The study delves into the challenges of data synchronization, calibration, and validation between the two methodologies. We discuss the potential for synergy in building a more holistic understanding of agriculture by fusing data from drones and in situ soil sensors. The findings of this research have implications for environmental monitoring, agriculture, and ecosystem management, suggesting that the combination of aerial and ground sensing offers a multi-dimensional perspective that can enhance decision-making processes and our grasp of intricate environmental processes. Full article

Knowledge about spatial distribution patterns of soil attributes is very much needed for site-specific soil nutrient management (SSSNM) under precision agriculture. High spatial heterogeneity exists in the agricultural soils of India due to various reasons. The present practice of assessing the spatial variability of the vast cultivated landscape of India by using traditional soil sampling and analysis is costly and time consuming. Hence, proximal soil sensing (PSS) is an attractive option to assess the plot-scale spatial variability pattern (SVP) of soil attributes for SSSNM. A PSS system, either in a fixed position or mounted on a vehicle (on-the-go), can be used to obtain measurements by having direct contact with soil. PSS measurements provide low-cost and high-density data pertaining to the SVPs of soil attributes. These data can be used to generate digital elevation and soil attribute variability maps at the field scale in a crop production environment. Based on the generated variability maps, locally available and economically feasible agricultural inputs can be applied using variable rate application strategies for sustainable cropping and enhanced farm profit. This overview presents the potential of adopting PSS in India and other developing countries. The scope, challenges, and probable solutions are also proposed. There is ample scope for adoption of PSS in India in view of diverse soil types, climatic conditions, cropping patterns, crop management practices, and ultimately, the ever-increasing demand for higher agricultural production. However, the successful adoption of the PSS technique in India will be dependent on the proper design and adoption of strategies which require adequate planning and analysis. There are several studies that have highlighted the usefulness of soil sensing technologies in Indian soils. There are also certain challenges and limitations associated with PSS in India, which could be addressed. The available proximal soil sensing technologies will be of great help in improving the understanding of soil heterogeneity for adopting SSSNM in order to optimize crop production in India and other developing countries. Full article

Many current precision agriculture applications involve on-the-go field measurements of soil and plant properties that require accurate georeferencing. Specific equipment configuration characteristics or data transmission, reception, or logging delays may cause a mismatch between the logged data and the GPS coordinates because of time and position lags that occur during data acquisition. We propose a simple coordinate translation along the measurement tracks to correct for such positional inaccuracies, based on the local travel speed and time lag, which is estimated by minimizing the average ln-transformed absolute difference with the nearest neighbors. The correction method is evaluated using electromagnetic induction soil-sensor data for different spatial measurement layouts and densities and by comparing variograms for raw and modified coordinates. Time lags of 1 s are shown to propagate into the spatial correlation structure up to lag distances of 10 m. The correction method performs best when repeated measurements in opposite driving directions are used and worst when measurements along parallel driving tracks are only repeated at the headland turns. In the latter case, the performance of the method is further improved by limiting the search neighborhood to adjacent measurement tracks. The proposed coordinate correction method is useful for improving the positional accuracy in a wide range of soil- and plant-sensing applications, without the need to grid the data first. Full article

The presented paper deals with the issue of selecting a suitable system for monitoring the winter wheat crop in order to determine its condition as a basis for variable applications of nitrogen fertilizers. In a four-year (2017–2020) field experiment, 1400 ha of winter wheat crop were monitored using the ISARIA on-the-go system and remote sensing using Sentinel-2 multispectral satellite images. The results of spectral measurements of ISARIA vegetation indices (IRMI, IBI) were statistically compared with the values of selected vegetation indices obtained from Sentinel-2 (EVI, GNDVI, NDMI, NDRE, NDVI and NRERI) in order to determine potential hips. Positive correlations were found between the vegetation indices determined by the ISARIA system and indices obtained by multispectral images from Sentinel-2 satellites. The correlations were medium to strong (r = 0.51–0.89). Therefore, it can be stated that both technologies were able to capture a similar trend in the development of vegetation. Furthermore, the influence of climatic conditions on the vegetation indices was analyzed in individual years of the experiment. The values of vegetation indices show significant differences between the individual years. The results of vegetation indices obtained by the analysis of spectral images from Sentinel-2 satellites varied the most. The values of winter wheat yield varied between the individual years. Yield was the highest in 2017 (7.83 t/ha), while the lowest was recorded in 2020 (6.96 t/ha). There was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha). Full article

Hyperspectral imaging offers enormous potential for measuring grape composition with a high degree of representativity, allowing all exposed grapes from the cluster to be examined non-destructively. On-the-go hyperspectral images were acquired using a push broom hyperspectral camera (400–100 nm) that was mounted in the front part of a motorized platform moving at 5 km/h in a commercial Tempranillo vineyard in La Rioja, Spain. Measurements were collected on three dates during grape ripening in 2018 on the east side of the canopy, which was defoliated in the basal fruiting zone. A total of 144 grape clusters were measured for Total soluble solids (TSS), Titratable acidity (TA), pH, Tartaric and Malic acid, Anthocyanins and Total polyphenols, using standard wet chemistry reference methods, throughout the entire experiment. Partial Least Squares (PLS) regression was used to build calibration, cross validation and prediction models for the grape composition parameters. The best performances returned determination coefficients values of external validation (R²_p) of 0.82 for TSS, 0.81 for Titratable acidity, 0.61 for pH, 0.62 for Tartaric acid, 0.84 for Malic acid, 0.88 for Anthocyanins and 0.55 for Total polyphenols. The promising results exposed in this work disclosed a notable methodology on-the-go for the non-destructive, in-field assessment of grape quality composition parameters along the ripening period. Full article

Crop biomass is an important attribute to consider in relation to site-specific nitrogen (N) management as critical N levels in plants vary depending on crop biomass. Whilst LiDAR technology has been used extensively in small plot-based phenomics studies, large-scale crop scanning has not yet been reported for cereal crops. A LiDAR sensing system was implemented to map a commercial 64-ha wheat paddock to assess the spatial variability of crop biomass. A proximal active reflectance sensor providing spectral indices and estimates of crop height was used as a comparison for the LiDAR system. Plant samples were collected at targeted locations across the field for the assessment of relationships between sensed and measured crop parameters. The correlation between crop biomass and LiDAR-derived crop height was 0.79, which is similar to results reported for plot scanning studies and greatly superior to results obtained for the spectral sensor tested. The LiDAR mapping showed significant crop biomass variability across the field, with estimated values ranging between 460 and 1900 kg ha⁻¹. The results are encouraging for the use of LiDAR technology for large-scale operations to support site-specific management. To promote such an approach, we encourage the development of an automated, on-the-go data processing capability and dedicated commercial LiDAR systems for field operation. Full article

Rapid and accurate measurement of high-resolution soil total nitrogen (TN) information can promote variable rate fertilization, protect the environment, and ensure crop yields. Many scholars focus on exploring the rapid TN detection methods and corresponding soil sensors based on spectral technology. However, soil spectra are easily disturbed by many factors, especially soil moisture and particle size. Real-time elimination of the interferences of these factors is necessary to improve the accuracy and efficiency of measuring TN concentration in farmlands. Although, many methods can be used to eliminate soil moisture and particle size effects on the estimation of soil parameters using continuum spectra. However, the discrete NIR spectral band data can be completely different in the band attribution with continuum spectra, that is, it does not have continuity in the sense of spectra. Thus, relevant elimination methods of soil moisture and particle size effects on continuum spectra do not apply to the discrete NIR spectral band data. To solve this problem, in this study, moisture absorption correction index (MACI) and particle size correction index (PSCI) methods were proposed to eliminate the interferences of soil moisture and particle size, respectively. Soil moisture interference was decreased by normalizing the original spectral band data into standard spectral band data, on the basis of the strong soil moisture absorption band at 1450 nm. For the PSCI method, characteristic bands of soil particle size were identified to be 1361 and 1870 nm firstly. Next, normalized index N_p, which calculated wavelengths of 1631 and 1870 nm, was proposed to eliminate soil particle size interference on discrete NIR spectral band data. Finally, a new coupled elimination method of soil moisture and particle size interferences on predicting TN concentration through discrete NIR spectral band data was proposed and evaluated. The six discrete spectral bands (1070, 1130, 1245, 1375, 1550, and 1680 nm) used in the on-the-go detector of TN concentration were selected to verify the new method. Field tests showed that the new coupled method had good effects on eliminating interferences of soil moisture and soil particle size. Full article

High resolution data on plant available potassium (Ka) is crucial to optimize variable rate potassium fertilizer recommendations, and subsequently improve crop growth and yield. A gamma-ray passive spectrometry sensor was evaluated for on-the-go mapping and management of the spatial distribution of Ka over a 8.4 ha field at Huldenberg, Belgium. During the on-the-go measurement, a 5 s sampling interval was used while driving at 3 km/h speed along 10 m parallel transects. Two calibration models to predict Ka across the field were developed and compared: (1) a simple third order polynomial function (3DPF) was established between the sensor reading of the naturally occurring radioactive isotope of potassium (K-40) and laboratory measured Ka and (2) a partial least squares regression (PLSR) model linking gamma-ray spectra and laboratory measured Ka. Although a relatively small number of samples (45 samples) were used for the development of the PLSR calibration model, the cross-validation analysis resulted in a very good performance with a coefficient of determination (R²) of 0.85, a residual prediction deviation (RPD) of 2.67, a root mean square error of cross-validation (RMSECV) of 2.29 (mg/100 g) and a ratio of performance to interquartile distance (RPIQ) of 2.61. This was a much better result that that obtained with the 3DPF model (R² = 0.69). The spatial distribution of Ka developed based on 3DPF and PLSR methods showed great similarity with the corresponding map developed using the data from the laboratory analysis. The calculated variable rate fertilizer recommendation based on gamma-ray data showed marginal differences in the amount of K₂O fertilizer applied, compared to the uniform rate fertilization based on the conventional laboratory chemical soil analyses. The on-the-go measurement of Ka using gamma-ray spectrometry shows high potential, although the technology needs to be evaluated in a larger number of fields. Full article

Precision nitrogen (N) management requires an accurate and timely in-season assessment of crop N status. The proximal fluorescence sensor Multiplex^®3 is a promising tool for monitoring crop N status. It performs a non-destructive estimation of plant chlorophyll, flavonol, and anthocyanin contents, which are related to plant N status. The objective of this study was to evaluate the potential of proximal fluorescence sensing for N status estimation at different growth stages for rice in cold regions. In 2012 and 2013, paddy rice field experiments with five N supply rates and two varieties were conducted in northeast China. Field samples and fluorescence data were collected in the leaf scale (LS), on-the-go (OG), and above the canopy (AC) modes using Multiplex^®3 at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. The relationships between the Multiplex indices or normalized N sufficient indices (NSI) and five N status indicators (above-ground biomass (AGB), leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI)) were evaluated. Results showed that Multiplex measurements taken using the OG mode were more sensitive to rice N status than those made in the other two modes in this study. Most of the measured fluorescence indices, especially the N balance index (NBI), simple fluorescence ratios (SFR), blue–green to far-red fluorescence ratio (BRR_FRF), and flavonol (FLAV) were highly sensitive to N status. Strong relationships between these fluorescence indices and N indicators, especially the LNC, PNC, and NNI were revealed, with coefficients of determination (R²) ranging from 0.40 to 0.78. The N diagnostic results indicated that the normalized N sufficiency index based on NBI under red illumination (NBI_R_NSI) and FLAV achieved the highest diagnostic accuracy rate (90%) at the SE and HE stages, respectively, while NBI_R_NSI showed the highest diagnostic consistency across growth stages. The study concluded that the Multiplex sensor could be used to reliably estimate N nutritional status for rice in cold regions, especially for the estimation of LNC, PNC, and NNI. The normalized N sufficiency indices based on the Multiplex indices could further improve the accuracy of N nutrition diagnosis by reducing the influences of inter-annual variations and different varieties, as compared with the original Multiplex indices. Full article

Visible-Short Wave Near Infrared (VIS + SW − NIR) spectroscopy is a real alternative to break down the next barrier in precision viticulture allowing a reliable monitoring of grape composition within the vineyard to facilitate the decision-making process dealing with grape quality sorting and harvest scheduling, for example. On-the-go spectral measurements of grape clusters were acquired in the field using a VIS + SW − NIR spectrometer, operating in the 570–990 nm spectral range, from a motorized platform moving at 5 km/h. Spectral measurements were acquired along four dates during grape ripening in 2017 on the east side of the canopy, which had been partially defoliated at cluster closure. Over the whole measuring season, a total of 144 experimental blocks were monitored, sampled and their fruit analyzed for total soluble solids (TSS), anthocyanin and total polyphenols concentrations using standard, wet chemistry reference methods. Partial Least Squares (PLS) regression was used as the algorithm for training the grape composition parameters’ prediction models. The best cross-validation and external validation (prediction) models yielded determination coefficients of cross-validation (R²_cv) and prediction (R²_P) of 0.92 and 0.95 for TSS, R²_cv = 0.75, and R²_p = 0.79 for anthocyanins, and R²_cv = 0.42 and R²_p = 0.43 for total polyphenols. The vineyard variability maps generated for the different dates using this technology illustrate the capability to monitor the spatiotemporal dynamics and distribution of total soluble solids, anthocyanins and total polyphenols along grape ripening in a commercial vineyard. Full article

Kochia (Kochia scoparia L.), Russian thistle (Salsola tragus L.), and prickly lettuce (Lactuca serriola L.) are economically important weeds infesting dryland wheat (Triticum aestivum L.) production systems in the western United States. Those weeds produce most of their seeds post-harvest. The objectives of this study were to determine the ability of an optical sensor, installed for on-the-go measurement of grain protein concentration, to detect the presence of green plant matter in flowing grain and assess the potential usefulness of this information for mapping weeds at harvest. Spectra of the grain stream were recorded continuously at a rate of 0.33 Hz during harvest of two spring wheat fields of 1.9 and 5.4 ha. All readings were georeferenced using a Global Positioning System (GPS) receiver with 1 m positional accuracy. Chlorophyll of green plant matter was detectable in the red (638–710 nm) waveband. Maps of the chlorophyll signal from both fields showed an overall agreement of 78.1% with reference maps, one constructed prior to harvest and the other at harvest time, both based on visual evaluations of the three green weed species conducted by experts. Information on weed distributions at harvest may be useful for controlling post-harvest using variable rate technology for herbicide applications. Full article

Soil pH is a key parameter for crop productivity, therefore, its spatial variation should be adequately addressed to improve precision management decisions. Recently, the Veris pH Manager^TM, a sensor for high-resolution mapping of soil pH at the field scale, has been made commercially available in the US. While driving over the field, soil pH is measured on-the-go directly within the soil by ion selective antimony electrodes. The aim of this study was to evaluate the Veris pH Manager^TM under farming conditions in Germany. Sensor readings were compared with data obtained by standard protocols of soil pH assessment. Experiments took place under different scenarios: (a) controlled tests in the lab, (b) semicontrolled test on transects in a stop-and-go mode, and (c) tests under practical conditions in the field with the sensor working in its typical on-the-go mode. Accuracy issues, problems, options, and potential benefits of the Veris pH Manager^TM were addressed. The tests demonstrated a high degree of linearity between standard laboratory values and sensor readings. Under practical conditions in the field (scenario c), the measure of fit (r²) for the regression between the on-the-go measurements and the reference data was 0.71, 0.63, and 0.84, respectively. Field-specific calibration was necessary to reduce systematic errors. Accuracy of the on-the-go maps was considerably higher compared with the pH maps obtained by following the standard protocols, and the error in calculating lime requirements was reduced by about one half. However, the system showed some weaknesses due to blockage by residual straw and weed roots. If these problems were solved, the on-the-go sensor investigated here could be an efficient alternative to standard sampling protocols as a basis for liming in Germany. Full article