Suitability of a laser rangefinder to characterize winter wheat

In recent years, laser rangefinder sensors have been introduced to the practice and research of agricultural engineering. In research, laser rangefinders have been investigated in horticulture and agriculture. For vehicle-based determination of crop biomass, commercially available laser rangefinders have been analysed and tested to measure aboveground biomass in oilseed rape, winter rye, winter wheat, oats and grassland. Resulting from limited measuring range and fixed beam types, the laser rangefinder models that were investigated only partially met the specific demands for agricultural field and crop conditions. Therefore, a new laser rangefinder scanner (ibeo-ALASCA XT) was chosen. This sensor was specifically developed for driver assistance and autonomous guiding of road vehicles. The scanner was tested in 2008 focusing on the measurement of crop stand parameters in winter wheat under field conditions. The sensor achieved good results with reproducible measurements. Measuring from a stationary vehicle, the standard deviation for the measurements of crop height to characterise the crop stand was less than 3 mm in low, medium, and high biomass areas. The ground speed of the vehicle, ranging from 6 to 24 km h^?1, did not significantly influence the readings. For measurements in front of tractors and self-propelled machines (field sprayers, combines and forage harvesters), the sensor has to scan the crop stands at different inclination angles. It was shown that the inclination angle of the laser beam, which varied from 10° to 80°, significantly influenced the readings. Higher inclination angles resulted in apparent increased heights of the crop stand. For the functional relationship between reflection height levels (95th, 75th, 50th and 25th percentiles, and mean values) and crop biomass density, the coefficient of determination (R²) was greater than 0.9.     

Sources of angle-dependent errors in terrestrial laser scanner-based crop stand measurement

In past years, research was conducted to investigate the potential of laser rangefinders and scanners for vehicle-based measuring of crop stand physical parameters. High coefficients of determination (R² > 0.90) were found between the crop biomass and the laser rangefinder and scanner readings in the form of the mean height of reflection points of the laser beam. It was observed that the height of the reflection point increased depending on the detecting angle of the laser beam in a considerable manner. This phenomenon indicates that farther crop plants generate higher reflection points, resulting in an overestimation of crop height respectively of crop biomass. In the face of these unsolved problems, the object of the paper was to investigate the measuring properties of a chosen laser scanner depending on the inclination angle and the scanning angle and to analyze the error sources for vehicle-based laser scanner measurements in crop stands. Therefore, the scanner was investigated in two test series (May 30, 2008 in winter wheat, and June 10, 2009 in winter rye) along a transect (tramline) with a length of approximately 700 m. The performed comparisons demonstrated that one part of the observed overestimation of the reflection point height can be explained theoretically by the geometry of the laser beam. The main part of overestimation was explained by the recognizability of the gap fraction in crop stands. Because no sufficient theoretical compensation algorithm for overestimation resulting from gaps exists, it must be concluded that for each specific laser rangefinder type and crop species, the overestimation depending on detection angles has to be investigated individually in field tests.     

Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach

Easy-to-capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields. In this study, aerial red, green and blue color space (RGB) photography and near-infrared (NIR) photography was performed on an experimental field site with nine different cover crops. A lightweight unmanned aerial system (UAS) served as platform, consumer cameras as sensors. Photos were photogrammetrically processed to orthophotos and digital surface models (DSMs). In a first validation step, the spatial precision of RGB orthophotos (x and y, ± 0.1 m) and DSMs (z, ± 0.1 m) was determined. Then, canopy cover (CC), plant height (PH), normalized differenced vegetation index (NDVI), red edge inflection point (REIP), and green red vegetation index (GRVI) were extracted. In a second validation step, the PHs derived from the DSMs were compared with ground truth ruler measurements. A strong linear relationship was observed (R ² = 0.80?0.84). Finally, destructive biomass samples were taken and compared with the remotely-sensed characteristics. Biomass correlated best with plant height (PH), and good approximations with linear regressions were found (R ² = 0.74 for four selected species, R ² = 0.58 for all nine species). CC and the vegetation indices (VIs) showed less significant and less strong overall correlations, but performed well for certain species. It is therefore evident that the use of DSM-based PHs provides a feasible approach to a species-independent non-destructive biomass determination, where the performance of VIs is more species-dependent.     

Evaluation of the use of LIDAR laser scanner to map pruning wood in vineyards and its potential for management zones delineation

Vine vigour assessment has been a major concern of precision viticulture studies in order to identify areas of uniform vine performance within vineyards. Moreover, the counting and weighing of winter dormant canes is considered as the most informative measurement to indicate vine balance and is commonly performed manually by grape growers for management purposes. The main concern of this measurement is that it is time consuming and laborious and it cannot accommodate detailed sampling density. In the present study, the potential of using laser scanner technology as an automated, easy and rapid way to perform mapping of the winter pruning wood across the vineyard was investigated. The study was conducted during 2010 and 2011, in a one hectare commercial vineyard in central Greece, planted with cv. Agiorgitiko, a traditional Greek variety for the production of red wine. Parameters of topography, soil depth, soil texture, canopy properties (NDVI), yield, and grape quality were mapped and analysed in conjunction to winter canes weighing at pruning time. The mapping of the dormant canes was carried out using a 2D laser scanner sensor prior to pruning and manually measuring the pruning weight on a 10 × 20 m grid. Laser scanner measurements showed significant relationship in both 2010 and 2011 with pruning weight (r = 0.809 and r = 0.829 respectively, p < 0.001), yield and early season NDVI, showing the potential of using laser scanner measurements to assess variability in vine vigour within vineyards. These results suggest that laser scanners offer great promise to characterize within field variability in vine performance.     

Monitoring nitrogen,phosphorus and sulphur in hybrid rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) using hyperspectral remote sensing

Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI₁₅₁₀ ((R₁₅₁₀ ? R₆₆₀)/(R₁₅₁₀ + R₆₆₀)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R₁₂₆₀ ? R₆₇₀)/(R₁₂₆₀ + R₆₇₀)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R₁₀₉₀ ? R₆₇₀)/(R₁₀₉₀ + R₆₇₀)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.     

Leaf area index estimation in vineyards using a ground-based LiDAR scanner

Estimation of grapevine vigour using mobile proximal sensors can provide an indirect method for determining grape yield and quality. Of the various indexes related to the characteristics of grapevine foliage, the leaf area index (LAI) is probably the most widely used in viticulture. To assess the feasibility of using light detection and ranging (LiDAR) sensors for predicting the LAI, several field trials were performed using a tractor-mounted LiDAR system. This system measured the crop in a transverse direction along the rows of vines and geometric and structural parameters were computed. The parameters evaluated were the height of the vines (H), the cross-sectional area (A), the canopy volume (V) and the tree area index (TAI). This last parameter was formulated as the ratio of the crop estimated area per unit ground area, using a local Poisson distribution to approximate the laser beam transmission probability within vines. In order to compare the calculated indexes with the actual values of LAI, the scanned vines were defoliated to obtain LAI values for different row sections. Linear regression analysis showed a good correlation (R ² = 0.81) between canopy volume and the measured values of LAI for 1 m long sections. Nevertheless, the best estimation of the LAI was given by the TAI (R ² = 0.92) for the same length, confirming LiDAR sensors as an interesting option for foliage characterization of grapevines. However, current limitations exist related to the complexity of data process and to the need to accumulate a sufficient number of scans to adequately estimate the LAI.     

Methodology for measuring fAPAR in crops using a combination of active optical and linear irradiance sensors: a case study in Triticale (X Triticosecale Wittmack)

The amount of photosynthetically active radiation (PAR, 0.4–0.7 μm) absorbed by plants for photosynthesis relative to incident radiation is defined as the fraction of absorbed photosynthetically active radiation (fAPAR). This is an important variable in both plant biomass production and plant growth modeling. This study investigates the application of a newly developed, linear irradiance sensor (LightScout Quantum Bar Sensor, LightScout, Spectrum Technologies, Inc. USA), to quantify fAPAR for a demonstrator crop, Triticale (X Triticosecale Wittmack). A protocol was devised for sensor placement to determine reflected PAR components of fAPAR and to determine the optimal time of day and sensor orientation for data collection. Coincident, top of canopy, normalized difference vegetation index (NDVI) measurements were also acquired with a CropCircle? ACS-210 sensor and measurements correlated with derived fAPAR values. The optimum height of the linear irradiance sensor above soil or plant canopy was found to be 0.4 m while measuring reflected PAR. Measurement of fAPAR was found to be stable when conducted within 1 h of local solar noon in order to avoid significant bidirectional effects resulting from diurnal changes of leaf orientation relative to the vertically-placed sensor. In the row crop studied, averaging fAPAR readings derived from the linear irradiance sensor orientated across and along the plant row provided an R² = 0.81 correlation with above-canopy NDVI. Across row sensor orientation also gave a similar correlation of R² = 0.76 allowing the user to reduce sampling time.     

Laser rangefinder-based measuring of crop biomass under field conditions

Knowledge of site-specific crop parameters such as plant height, coverage and biomass density is important for optimising crop management and harvesting processes. Sensors for measuring crop parameters are essential pre-requisites to gather this information. In recent years, laser rangefinder sensors have been adopted in many industrial applications. In agricultural engineering, the potential of laser rangefinders for measuring crop parameters has been little exploited. This paper reports the design and the performance of a measuring system based on a triangulation and a time-of-flight laser rangefinder for estimating crop biomass density in representative crops under field conditions. It was shown that the mean height of reflection point is a suitable parameter for non-contact indirect measurement of crop biomass by laser rangefinder sensors. The main parameters for potential assessment were the coefficient of determination (R ² ) and the standard error (RMSE) for the relation between crop biomass density and the mean height of the reflection point in crop stands from oilseed rape, winter rye, winter wheat and grassland during the vegetation period in 2006. For the triangulation sensor, R ² was in the range from 0.87 to 0.98 and for the time-of-flight sensor in the range from 0.75 to 0.99 for both fresh matter and dry matter density. The triangulation sensor had a reduced suitability caused by masking effects of the reflected beam and because of limited measuring range. Based on the results of experiments and technical data, it was concluded that the time-of-flight principle has good potential for site-specific crop management.     

Evaluation of spectral indices and continuous wavelet analysis to quantify aphid infestation in wheat

Wheat aphid, Sitobion avenae F. is one of the most destructive insects infesting winter wheat and appears almost annually in northwest China. Past studies have demonstrated the potential of remote sensing for detecting crop diseases and insect damage. This study aimed to investigate the spectroscopic estimation of leaf aphid density by applying continuous wavelet analysis to the reflectance spectra (350–2 500 nm) of 60 winter wheat leaf samples. Continuous wavelet transform (CWT) was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength location and scale of decomposition. Linear regression between the wavelet power and aphid density was to identify wavelet features (coefficients) that might be the most sensitive to aphid density. The results identified five wavelet features between 350 and 2 500 nm that provided strong correlations with leaf aphid density. Spectral indices commonly used to monitor crop stresses were also employed to estimate aphid density. Multivariate linear regression models based on six sensitivity spectral indices or five wavelet features were established to estimate aphid density. The results showed that the model with five wavelet features (R² = 0.72, RMSE = 16.87) performed better than the model with six sensitivity spectral indices (R² = 0.56, RMSE = 21.19), suggesting that the spectral features extracted through CWT might potentially reflect aphid density. The results also provided a new method for estimating aphid density using remote sensing.     

Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor

In-season site-specific nitrogen (N) management is a promising strategy to improve crop N use efficiency and reduce risks of environmental contamination. To successfully implement such precision management strategies, it is important to accurately estimate yield potential without additional topdressing N application (YP₀) as well as precisely assess the responsiveness to additional N application (RI) during the growing season. Previous research has mainly used normalized difference vegetation index (NDVI) or ratio vegetation index (RVI) obtained from GreenSeeker active crop canopy sensor with two fixed bands in red and near-infrared (NIR) spectrums to estimate these two parameters. The development of three-band Crop Circle active sensor provides a potential to improve in-season estimation of YP₀ and RI. The objectives of this study were twofold: (1) identify important vegetation indices obtained from Crop Circle ACS-470 sensor for estimating rice YP₀ and RI; and (2) evaluate their potential improvements over GreenSeeker NDVI and RVI. Four site-years of field N rate experiments were conducted in 2012 and 2013 at the Jiansanjiang Experiment Station of China Agricultural University located in Northeast China. The GreenSeeker and Crop Circle ACS-470 active canopy sensor with green, red edge, and NIR bands were used to collect rice canopy reflectance data at different key growth stages. The results indicated that both the GreenSeeker (best R² = 0.66 and 0.70, respectively) and Crop Circle (best R² = 0.71 and 0.77, respectively) sensors worked well for estimating YP₀ and RI at the stem elongation stage. At the booting stage, Crop Circle red edge optimized soil adjusted vegetation index (REOSAVI, R² = 0.82) and green ratio vegetation index (R² = 0.73) explained 26 and 22 % more variability in YP₀ and RI, respectively, than GreenSeeker NDVI or RVI. At the heading stage, the GreenSeeker sensor indices became saturated and consequently could not be used for YP₀ or RI estimation, while Crop Circle REOSAVI and normalized green index could still explain more than 70 % of YP₀ and RI variability. It is concluded that both sensors performed similarly at the stem elongation stage, but significantly better results were obtained by the Crop Circle sensor at the booting and heading stages. Furthermore, the results revealed that Crop Circle green band-based vegetation indices performed well for RI estimation while the red edge-based vegetation indices were the best for estimating YP₀ at later growth stages.     

Evaluating the performance of xanthophyll,chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species

This study assessed the capability of several xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in a commercial farm consisting of five fruit tree crop species with contrasting phenology and canopy architecture. Plots irrigated and non-irrigated for eight days of each species were used to promote a range of plant water status. Multi-spectral and thermal images were acquired from an unmanned aerial system while concomitant measurements of stomatal conductance (g_s), stem water potential (Ψ_s) and photosynthesis were taken. The Normalized Difference Vegetation Index (NDVI), red-edge ratio (R₇₀₀/R₆₇₀), Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil Adjusted Vegetation Index (TCARI/OSAVI), the Photochemical Reflectance Index using reflectance at 530 (PRI) and 515 nm [PRI_(570–515)] and the normalized PRI (PRI_norm) were obtained from the narrow-band multi-spectral images and the relationship with the in-field measurements explored. Results showed that within the Prunus species, Ψ_s yielded the best correlations with PRI and PRI_(570–515) (r² = 0.53) in almond trees, with TCARI/OSAVI (r² = 0.88) in apricot trees and with PRI_norm, R₇₀₀/R₆₇₀ and NDVI (r² from 0.72 to 0.88) in peach trees. Weak or no correlations were found for the Citrus species due to the low level of water stress reached by the trees. Results from the sensitivity analysis pointed out the canopy temperature (T_c) and PRI_(570–515) as the first and second most sensitive indicators to the imposed water conditions in all the crops with the exception of apricot trees, in which Ψ_s was the most sensitive indicator at midday. PRI_norm was the least sensitive index among all the water stress indicators studied. When all the crops were analyzed together, PRI_(570–515) and NDVI were the indices that better correlations yielded with Crop Water Stress Index, g_s and, particularly, Ψ_s (r² = 0.61 and 0.65, respectively). This work demonstrated the feasibility of using narrow-band multispectral-derived indices to retrieve water status for a variety of crop species with contrasting phenology and canopy architecture.     

Retrieval of biophysical vegetation parameters using simultaneous inversion of high resolution remote sensing imagery constrained by a vegetation index

This study proposes a new method for inverting radiative transfer models to retrieve canopy biophysical parameters using remote sensing imagery. The inversion procedure is improved with respect to standard inversion, and achieves simultaneous inversion of leaf area index (LAI), soil reflectance (ρ_soil), chlorophyll content (C_a+b) and average leaf angle (ALA). In this approach, LAI is used to constrain modelling conditions during the inversion process, providing information about the phenological state of each plot under study. Due to the small area of the vegetation plots used for the inversion procedure and in order to avoid redundant information and improve computation efficiency, existing plot segmentation was used. All retrieved biophysical parameters, except LAI, were assumed to be invariant within each plot. The proposed methodology, based on the combination of PROSPECT and SAILH models, was tested over 16 cereal fields and 51 plots, on two dates, which were chosen to ensure crop assessment at different phenological stages. Plots were selected to provide a wide range of LAI between 0 and 6. Field measurements of LAI, ALA and C_a+b were conducted and used as ground truth for validation of the proposed model-inversion methodology. The approach was applied to very high spatial resolution remote sensing data from the QuickBird 2 satellite. The inversion procedure was successfully applied to the imagery and retrieved LAI with R ² = 0.83 and RMSE = 0.63 when compared to LAI2000 ground measurements. Separate inversions for barley and wheat yielded R ² = 0.89 (RMSE = 0.64) and R ² = 0.56 (RMSE = 0.61), respectively.     

Apparent electrical conductivity in dry versus wet soil conditions in a shallow soil

The general objective of this study was to evaluate the stability of patterns of apparent soil electrical conductivity (EC_a) in dry versus wet soil conditions in a shallow soil typically used for pastures in Mediterranean conditions of the southern region of Portugal. A 6 ha experimental field of permanent bio-diverse pasture was divided into 76 squares of 28 × 28 m. The soil electrical conductivity was measured using a Dualem 1S sensor under dry conditions (June 2007) and under wet conditions during the rainy season (March 2010). Soil samples, geo-referenced with GPS, were collected in a depth range of 0–0.30 m. The soil was characterized in terms of bedrock depth, moisture content, texture, pH, organic matter content, and macronutrients (nitrogen, phosphorus, and potassium). Pasture samples, also geo-referenced with GPS, were collected to measure the pasture dry matter yield. The statistical analysis of apparent electrical conductivity between dry and wet soil conditions resulted in a linear significant correlation coefficient (R = 0.88). The results also showed a significant correlation between apparent electrical conductivity and the relative field elevation (R = ?0.64 and R = ?0.66), the pasture dry matter yield (R = 0.42 and R = 0.48), the bedrock depth (R = 0.40 and R = 0.27), the pH (R = 0.50 and R = 0.49), the silt (R = 0.27 and R = 0.38) and soil moisture content (R = 0.48 and R = 0.45), in dry and wet conditions, respectively. A multi-variate regression was carried out using the following soil parameters that showed significant correlation with EC_a and that did not present multi-collinearity: pH, bedrock depth, silt and moisture content. The results showed, in dry and wet conditions, that the analysis was significant (R = 0.75 and R = 0.84, respectively). Overall, these results indicate the temporal stability of EC_a patterns under different soil moisture contents, which is relevant with respect to the time when a field should be surveyed and is important for using the electrical conductivity sensor, as a decision support tool for management zones in precision agriculture.     

Mobile low-cost 3D camera maize crop height measurements under field conditions

To tackle global challenges such as food supply and renewable energy provision, the improvement of efficiency and productivity in agriculture is of high importance. Site-specific information about crop height plays an important role in reaching these goals. Crop height can be derived with a variety of approaches including the analysis of three-dimensional (3D) geodata. Crop height values derived from 3D geodata of maize (1.88 and 2.35 m average height) captured with a low-cost 3D camera were examined. Data were collected with a unique measurement setup including the mobile mounting of the 3D camera, and data acquisition under field conditions including wind and sunlight. Furthermore, the data were located in a global co-ordinate system with a straightforward approach, which can strongly reduce computational efforts and which can subsequently support near real-time data processing in the field. Based upon a comparison between crop height values derived from 3D geodata captured with the low-cost approach, and high-end terrestrial laser scanning reference data, minimum RMS and standard deviation values of 0.13 m (6.91% of average crop height), and maximum R² values of 0.79 were achieved. It can be concluded that the crop height measurements derived from data captured with the introduced setup can provide valuable input for tasks such as biomass estimation. Overall, the setup is considered to be a valuable extension for agricultural machines which will provide complementary crop height measurements for various agricultural applications.     

Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop

This study was conducted to explore whether hyperspectral data could be used to discriminate between the effects of different rates of nitrogen application to a potato crop. The field experiment was carried out in the Central Potato Research Station, Jalandhar, on seven plots with different nitrogen (N) treatments. Spectral reflectance was measured using a 512-channel spectroradiometer with a range of 395–1075 nm on two different dates during crop growth. An optimum number of bands were selected from this range based on band–band r ², principal component analysis and discriminant analysis. The four bands that could discriminate between the rates of N applied were 560, 650, 730, and 760 nm. An ANOVA analysis of several narrow-band indices calculated from the reflectance values showed the indices that were able to differentiate best between the different rates of N application. These were reflectance ratio at the red edge (R_740/720) and the structure insensitive pigment index (SIPI). To estimate leaf N, reflectance ratios were determined for each band combination and were evaluated for their correlation with the leaf N content. A regression model for N estimation was obtained using the reflectance ratio indices at 750 and 710 nm wavelengths (F-ratio = 32 and r ² = 0.551, P < 0.000).     

Application of day and night digital photographs for estimating maize biophysical characteristics

In this study, an inexpensive camera-observation system called the Crop Phenology Recording System (CPRS), which consists of a standard digital color camera (RGB cam) and a modified near-infrared (NIR) digital camera (NIR cam), was applied to estimate green leaf area index (LAI), total LAI, green leaf biomass and total dry biomass of stalks and leaves of maize. The CPRS was installed for the 2009 growing season over a rainfed maize field at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, NE, USA. The vegetation indices called Visible Atmospherically Resistant Index (VARI) and two green–red–blue (2g–r–b) were calculated from day-time RGB images taken by the standard commercially-available camera. The other vegetation index called Night-time Relative Brightness Index in NIR (NRBI_NIR) was calculated from night-time flash NIR images taken by the modified digital camera on which a NIR band-pass filter was attached. Sampling inspections were conducted to measure bio-physical parameters of maize in the same experimental field. The vegetation indices were compared with the biophysical parameters for a whole growing season. The VARI was found to accurately estimate green LAI (R² = 0.99) and green leaf biomass (R² = 0.98), as well as track seasonal changes in maize green vegetation fraction. The 2g–r–b was able to accurately estimate total LAI (R² = 0.97). The NRBI_NIR showed the highest accuracy in estimation of the total dry biomass weight of the stalks and leaves (R² = 0.99). The results show that the camera-observation system has potential for the remote assessment of maize biophysical parameters at low cost.     

Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle

Characterizing the spatial variability in water status across vineyards is a prerequisite for precision irrigation. The crop water stress index (CWSI) indicator was used to map the spatial variability in water deficits across an 11-ha ‘Pinot noir’ vineyard. CWSI was determined based on canopy temperatures measured with infrared temperature sensors placed on top of well-watered and water-stressed grapevines in 2009 and 2010. CWSI was correlated with leaf water potential (Ψ_L) (R ² = 0.83). This correlation was also tested with results from high resolution airborne thermal imagery. An unmanned aerial vehicle equipped with a thermal camera was flown over the vineyard at 07:30, 09:30, and 12:30 h (solar time) on 31 July 2009. At about the same time, Ψ_L was measured in 184 grapevines. The image obtained at 07:30 was not useful because it was not possible to separate soil from canopy temperatures. Using the airborne data, the correlation between CWSI and Ψ_L had an R ² value of 0.46 at 09:30 h and of 0.71 at 12:30 h, suggesting that the latter was the more favorable time for obtaining thermal images that were linked with Ψ_L values. A sensitivity analysis of varying pixel size showed that a 0.3 m pixel was needed for precise CWSI mapping. The CWSI maps thus obtained by airborne thermal imagery were effective in assessing the spatial variability of water stress across the vineyard.     

Optimization of corn plant population according to management zones in Southern Brazil

Precision agriculture relies on site-specific interventions determined by the spatial variability of factors driving plant growth. The main objective of this study was to assess the efficiency of variable-rate seeding of corn (Zea mays L.) with delineated management zones. This study involved two experiments carried out in Não-Me-Toque, Rio Grande do Sul, Brazil. For the first experiment, carried out in 2009/2010, management zones were delineated by the farmer’s knowledge of the crop field. The field was split into low (LZ), medium (MZ) and high (HZ) crop performance zones. In the second experiment, carried out in 2010/2011, management zones were delineated by overlaying standardized yield data from nine crop seasons (seven of soybean and two of corn). The experiment was carried out with a randomized block design with three management zones and five corn seeding rates ranging from 50 000 to 90 000 seeds per ha^?1. The soil was a Rhodic Hapludox with a subtropical climate. Optimization of the corn plant population within the field increased grain yield compared to the reference plant population (70 000 plants ha^?1). Yield increases in the LZ, due to corn plant population reduction in relation to the target population, were 1.20 and 1.90 Mg ha^?1 for first and second experiments, respectively. This resulted in economic gains of 19.8 and 28.7 %, respectively. Yield increases in the HZ were 0.89 and 0.94 Mg ha^?1, respectively, and were due to an increase in plant population in relation to the target population. This resulted in economic gains of 5.6 and 6.6 % for the first and second experiments, respectively. In the MZ, the adjustment of the target plant population was not necessary. Optimizing corn population according to management zones is a promising tool for precision agriculture in Southern Brazil.     

Developing a new Crop Circle active canopy sensor-based precision nitrogen management strategy for winter wheat in North China Plain

Active canopy sensor (ACS)—based precision nitrogen (N) management (PNM) is a promising strategy to improve crop N use efficiency (NUE). The GreenSeeker (GS) sensor with two fixed bands has been applied to improve winter wheat (Triticum aestivum L.) N management in North China Plain (NCP). The Crop Circle (CC) ACS-470 active sensor is user configurable with three wavebands. The objective of this study was to develop a CC ACS-470 sensor-based PNM strategy for winter wheat in NCP and compare it with GS sensor-based N management strategy, soil N_min test-based in-season N management strategy and conventional farmer’s practice. Four site-years of field N rate experiments were conducted from 2009 to 2013 to identify optimum CC vegetation indices for estimating early season winter wheat plant N uptake (PNU) and grain yield in Quzhou Experiment Station of China Agricultural University located in Hebei province of NCP. Another nine on-farm experiments were conducted at three different villages in Quzhou County in 2012/2013 to evaluate the performance of the developed N management strategy. The results indicated that the CC ACS-470 sensor could significantly improve estimation of early season PNU (R² = 0.78) and grain yield (R² = 0.62) of winter wheat over GS sensor (R² = 0.60 and 0.33, respectively). All three in-season N management strategies achieved similar grain yield as compared with farmer’s practice. The three PNM strategies all significantly reduced N application rates and increased N partial factor productivity (PFP) by an average of 61–67 %. It is concluded that the CC sensor can improve estimation of early season winter wheat PNU and grain yield as compared to the GS sensor, but the PNM strategies based on these two sensors perform equally well for improving winter wheat NUE in NCP. More studies are needed to further develop and evaluate these active sensor-based PNM strategies under more diverse on-farm conditions.     

Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds

LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r = 0.56 to r = 0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e., height, width and volume) and on canopy structure (i.e., light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.