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The intensification of the Montado mixed ecosystem (agro–silvo–pastoral) is a current endeavor in the context of promoting the sustainability of extensive livestock production in the Mediterranean region. Increased pasture productivity and extensive animal production involves the use of technologies to monitor spatial variability and to implement differentiated management of pasture grazing, fertilization or soil amendment. An intermediate step should lead to the identification and demarcation of areas with similar characteristics (soil and/or crop development), known as homogeneous management zones (HMZ) to implement site-specific management strategies. In this study, soil apparent electrical conductivity (ECa) and altimetry surveys were carried out in six experimental pasture fields with a non-contact electromagnetic induction sensor (EM38) associated with a Global Navigation Satellite System (GNSS) receiver. These ECa and topographic maps were used in geostatistical analyses for designing and establishing final classification maps with three HMZ (less, intermediate and more potential). The normalized difference vegetation index (NDVI), obtained from a proximal optical sensor, and soil and biomass sampling were used to validate these HMZ. From a practical perspective, these HMZ are the basis for preparation of fertilizer prescription maps and use of variable rate technology (VRT) in a Precision Agriculture project. [ABSTRACT FROM AUTHOR]

The biophysical characteristics of vegetative canopies, such as biomass, height, and canopy diameter, are of paramount importance for the study of the development and productive behavior of crops. Faced with a scarcity of studies aimed at estimating these parameters, the objective of this study was to evaluate the performance of artificial neural networks (ANNs) applied to Proximal Remote Sensing (PRS) to estimate biophysical characteristics of soybean culture. The data used to train and validate the ANNs came from an experiment composed of 65 plots with 30 x 30 m mesh, its development was carried out in the 2016/2017 crop in the Brazilian agricultural area. The evaluations were carried out at 30, 45, 60, and 75 days after sowing (DAS), monitoring the spatial and temporal variability of the biophysical characteristics of the soybean crop. Vegetation indexes were collected using canopy sensors. The accuracy and precision were determined by the coefficient of determination (R2) and the error of the forecasts by MAPE (Mean Absolute Percentage Error). PRS and ANNs showed high potential for application in agriculture, since they obtained good performance in the estimation of height (R2 = 0.89) and canopy diameter (R2 = 0.96), being fresh biomass (R2 =0.98) and dry biomass (R2 = 0.97) were the best-estimated variables.

The biophysical characteristics of vegetative canopies, such as biomass, height, and canopy diameter, are of paramount importance for the study of the development and productive behavior of crops. Faced with a scarcity of studies aimed at estimating these parameters, the objective of this study was to evaluate the performance of artificial neural networks (ANNs) applied to Proximal Remote Sensing (PRS) to estimate biophysical characteristics of soybean culture. The data used to train and validate the ANNs came from an experiment composed of 65 plots with 30 x 30 m mesh, its development was carried out in the 2016/2017 crop in the Brazilian agricultural area. The evaluations were carried out at 30, 45, 60, and 75 days after sowing (DAS), monitoring the spatial and temporal variability of the biophysical characteristics of the soybean crop. Vegetation indexes were collected using canopy sensors. The accuracy and precision were determined by the coefficient of determination (R2) and the error of the forecasts by MAPE (Mean Absolute Percentage Error). PRS and ANNs showed high potential for application in agriculture, since they obtained good performance in the estimation of height (R2 = 0.89) and canopy diameter (R2 = 0.96), being fresh biomass (R2 =0.98) and dry biomass (R2 = 0.97) were the best-estimated variables. [ABSTRACT FROM AUTHOR]

Crop monitoring through remote sensing techniques enable greater knowledge of average variability in crop growth. Canopy sensors help provide information on the variability of crop through the use of vegetation indices. The objective of this work was to compare the potential and performance of three vegetation indices used for monitoring soybean variability with canopy sensors was compared. The optimal time for sensor readings was determined during the soybean crop development stages. Also, the quality of the readings between vegetation indices [the normalized difference vegetation index (NDVI), normalized difference red-edge (NDRE), and inverse ratio (IRVI)] was compared through control charts and the saturation detection index. The experimental design was based on statistical quality control and comprised 65 sampling points within a 30 × 30 m grid. At 30, 45, 60, 75, and 90 days after sowing (DAS), the parameters used as quality indicators, such as fresh and dry biomass, canopy width, chlorophyll index, plant height, yield, and the vegetation indices were assessed using canopy sensors. The optimal time for canopy sensor readings, based mainly on the NDRE, was at 45 and 60 DAS. The lower variability exhibited by NDRE led to higher process quality when compared with those for NDVI and IRVI. The control charts proved to be promising in identifying the moment when saturation occurs for the indices more susceptible to saturation, such as the NDVI. [ABSTRACT FROM AUTHOR]

• Plant-O-Meter is suitable for low-cost, easy-to-perform, plant status measurements. • The device is supported by android app for generation of real-time maps of vegetation indices. • NDVI measured by the device was strongly correlated to the NDVI obtained by hyperspectral camera. • The performance of the device is not affected by the different illumination conditions. Plant stress monitoring is of crucial importance to understand the plant response to environmental conditions, and has been widely applied in various fields including biology, agronomy, botany, and horticulture. A number of methods and instruments for plant stress monitoring have been proposed, most of which show significant disadvantages such as high cost, poor accuracy or complex operation that limit the use of such devices. In this paper, we propose a low cost, portable active multispectral optical device for precise plant stress detection and field mapping named Plant-O-Meter. The device has an integrated multispectral source that comprises light sources of the four most indicative wavelengths (850, 630, 535 and 465 nm), and enables simultaneous illumination of the whole plant. Sequential illumination and detection provide rapid reflectance measurements, which are wirelessly transmitted to android operated devices for processing and data storing. The device was tested in laboratory conditions comparing the Plant-O-Meter measurements with the image results from a SPECIM hyperspectral camera and a GreenSeeker handheld device and under field conditions with the GreenSeeker. The comparison revealed comparable performance, showing strong correlation with both the hyperspectral (R2 = 0.997) and the GreenSeeker handheld (R2 = 0.954 from the laboratory measurements and R2 = 0.886 from the field experiments), indicating that the device exhibits strong potential for accurate plant stress measurements. Moreover, owing to the very simple operating principle, the device represents a compact, cost-effective, and easy-to-operate solution. [ABSTRACT FROM AUTHOR]

The 3D measuring range and accuracy in traditional active optical sensing, such as Fourier transform profilometry, are influenced by the zero frequency of the captured patterns. The phase-shifting technique is commonly applied to remove the zero component. However, this phase-shifting method must capture several fringe patterns with phase difference, thereby influencing the real-time performance. This study introduces a smart active optical sensor, in which a composite pattern is utilized. The composite pattern efficiently combines several phase-shifting fringes and carrier frequencies. The method can remove zero frequency by using only one pattern. Model face reconstruction and human face measurement were employed to study the validity and feasibility of this method. Results show no distinct decrease in the precision of the novel method unlike the traditional phase-shifting method. The texture mapping technique was utilized to reconstruct a nature-appearance 3D digital face. [ABSTRACT FROM AUTHOR]

The remote sensing data obtained at field level can provide detailed information about the variability of biophysical parameters related to yield over large areas, and present potential for monitoring these parameters throughout the crop development cycle. This study aimed to map the spatial variability of the Normalized Difference Vegetation Index (NDVI) and its components in two commercial cotton (Gossipium hirsutum L.) fields, by using an active optical sensor at the ground level. Data were collected with the aid of an optical sensor installed in a self-propelled agricultural sprayer. A GPS receiver was connected to the sensor, in order to obtain the coordinates of the sampling points. The readings were performed in rows spaced 21.0 m apart by the sensor installed on a vehicle, during the spraying operation, and data analyzed by using the classical statistics and geostatistics. Spatial distribution maps of the variables were generated by kriging interpolation. It was observed a higher spatial variability of NDVI and spectral reflectance of vegetation in the region of near infrared (NIR) (880 nm) and visible infrared (590 nm) in the crop with higher physiological stress, due to the brown bug [Scaptocoris castanea (Hem.: Cydnidae)] attack, when compared to the healthy one.

The nitrogen (N) nutrition index (NNI) is a reliable indicator of crop N status and there is an urgent need to develop efficient technologies for non-destructive estimation of NNI to support the practical applications of precision N management strategies. The objectives of this study were to: (i) validate a newly established critical N dilution curve for spring maize in Northeast China; (ii) determine the potential of using the GreenSeeker active optical sensor to non-destructively estimate NNI; and (iii) evaluate the performance of different N status diagnostic approaches based on estimated NNI via the GreenSeeker sensor measurements. Four field experiments involving six N rates (0, 60, 120,180, 240, and 300 kg·ha-1) were conducted in 2014 and 2015 in Lishu County, Jilin Province in Northeast China. The results indicated that the newly established critical N dilution curve was suitable for spring maize N status diagnosis in the study region. Across site-years and growth stages (V5-V10), GreenSeeker sensor-based vegetation indices (VIs) explained 87%-90%, 87%-89% and 83%-84% variability of leaf area index (LAI), aboveground biomass (AGB) and plant N uptake (PNU), respectively. However, normalized difference vegetation index (NDVI) became saturated when LAI > 2 m²·m-2, AGB > 3 t·ha-1 or PNU > 80 kg·ha-1. The GreenSeeker-based VIs performed better for estimating LAI, AGB and PNU at V5-V6 and V7-V8 than the V9-V10 growth stages, but were very weakly related to plant N concentration. The response index calculated with GreenSeeker NDVI (RI-NDVI) and ratio vegetation index (R² = 0.56-0.68) performed consistently better than the original VIs (R² = 0.33-0.55) for estimating NNI. The N status diagnosis accuracy rate using RI-NDVI was 81% and 71% at V7-V8 and V9-V10 growth stages, respectively. We conclude that the response indices calculated with the GreenSeeker-based vegetation indices can be used to estimate spring maize NNI non-destructively and for in-season N status diagnosis between V7 and V10 growth stages under experimental conditions with variable N supplies. More studies are needed to further evaluate different approaches under diverse on-farm conditions and develop side-dressing N recommendation algorithms. [ABSTRACT FROM AUTHOR]

Winter wheat ( Triticum aestivum L.) represents almost 50% of total cereal production in the European Union, accounting for approximately 25% of total mineral nitrogen (N) fertilizer applied to all crops. Currently, several active optical sensor (AOS) based systems for optimizing variable N fertilization are commercially available for a variety of crops, including wheat. To ensure successful adoption of these systems, definitive measurable benefits must be demonstrated. Nitrogen management strategies developed based on small-scale plot research are not always meaningful for large-scale farm conditions. In 2010–2012 (5 site-years) on-farm study was implemented in northern Poland utilizing a strip-trial design. The objective was to evaluate the performance of AOS in combination with a built-in algorithm for variable N rate fertilization. In this study, the reference uniform N rates (farmer’s practice) were comparable to optimum variable N rate recommendations. Side-by-side comparisons of uniform and variable N application revealed inconsistent benefits in terms of grain yield, grain protein content (GPC), N use and N use efficiency (NUE). Anticipated yield increases and/or reduced N rates are typical drivers for AOS adoption. Significant yield increases are not easily attained on farms with winter wheat yields already close to maximum yield potential. Thus, sensor-based variable N rate recommendations for fields previously fertilized with relatively low uniform N rates would often entail more appropriate allocation (redistribution) of the same amount of total N. This would minimize N surplus in areas of lower productivity and to improve the sustainability of N management overall. [ABSTRACT FROM AUTHOR]

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Abstract: Operational airborne and satellite remote sensing in agriculture remains constrained by matching platform availability to suitable daytime weather and illumination conditions, crop development, and availability of ground staff. An ultra low-level aircraft carrying an active NIR/Red CropCircle™ sensor was successfully deployed to record and subsequently map crop vigour via the simple ratio (SR) index over a field of sorghum. Given the logging frequency of ≈20Hz and the presence of alternate rows of bare soil, the Moiré effect reduced the contrast between crop and bare soil skip-rows. Such effects would not be expected to occur in non-skip-row crops. The ultra low-level airborne (ULLA)-SR map derived from the 20m transect records compared favorably with the SR map derived from a meter-resolution airborne digital multispectral image that was re-sampled to a similar spatial resolution. This case study, involving a CropCircle™ sensor mounted in a low-level aircraft demonstrates another deployment option for users of this class of sensor. Moreover, an ULLA configuration offers the potential for greater flexibility in scheduling compared to airborne imaging, given it can be flown at any sun-angle, under cloud, at night, and may easily be incorporated into aircraft already conducting low-level operations, for example crop dusting and reconnaissance, over agricultural fields. [Copyright &y& Elsevier]