多年生黑麦草和绒毛草在放牧条件下的生物学特性

比较了多年生黑麦草(Lolium perenne)和绒毛草(Holcus lanatus)在放牧绵羊条件下的产草量、草层高度和分蘖密度。结果:两类草地的牧前产草量相似,但绒毛草草地的牧后产量明显高于黑麦草草地。草层高度在放牧前后均无显著差异。绒毛草的枯死物质在放牧前后都高于多年生黑麦草。绒毛草具有很高的分蘖密度。     

Effects of changing simulated sky cover on hyperspectral reflectance measurements for dry matter yield and forage quality prediction

Sensor based analysis methods to assess dry matter yield and nutritive values of legume-grass swards are time and labour saving and can facilitate a site-specific forage management. Nevertheless, in-field measurements, based on canopy reflectance are highly dependent on weather conditions, like, e.g. wind or clouds. This study was conducted to explore the potential of field spectral measurements for a non destructive prediction of dry matter yield (DM), metabolisable energy (ME), ash content (XA), and crude protein (XP), of a binary legume-grass mixture (Trifolium pratense L. and Lolium multiflorum L.) under changing weather conditions. Five different degrees of sky cover were simulated by shadowing measurement plots with layers of cotton to reduce incoming radiation at different growth stages (leaf developing to flowering). Additionally, a halogen lamp was established over the plots to examine the influence of an artificial light source on the spectral response under changing cloud stages. Modified partial least squares (MPLS) regression was used for analysis of the hyperspectral data set (350-2500 nm). Artificial illumination led to spectral interferences of solar radiation and additional light, and hence, partly reduced prediction accuracies. In contrast, prediction accuracy increased, when solar radiation was completely excluded. Coefficients of determination (RSQ_cal) range from 0.87 to 0.94 without artificial illumination and from 0.86 to 0.94 with artificial illumination for DM yield and nutritive values, respectively.     

Assessing effect of wind tunnel sizes on air velocity and concentration boundary layers and on ammonia emission estimation using computational fluid dynamics (CFD)

The objective of this study was to investigate the effect of different geometric sizes of wind tunnels on aerial boundary layers above the emission surface and therefore their effect on ammonia emission using CFD tool. Five wind tunnels of different sizes were used for the CFD simulation. Detail experimental measurements on air velocity and concentration profiles above the emission surface were realised using average inlet velocities of 0.1, 0.2, 0.3, and 0.4 m s⁻¹ in two wind tunnels under isothermal condition. The TAN (Total Ammoniacal Nitrogen) and pH of ammonia aqueous solution were kept constant during the experiments. The boundary conditions necessary for the CFD study were obtained from the measured experimental data. The CFD model used for simulations was first validated using the data from the two wind tunnel experiments. For assessing the effects of wind tunnel size, the air velocity range of 0.1-0.6 m s⁻¹ was used in CFD investigations. It was found that the velocity and concentration boundary layer thickness decreased with the increase of inlet air velocity where the concentration boundary was thinner than the corresponding velocity boundary layer. Wind tunnel sizes affected both velocity and concentration boundary layer thicknesses (P < 0.001). The velocity and concentration boundary layer was thicker in a wind tunnel with larger heights than in small height. The over estimation of ammonia emission of smaller wind tunnels were observed comparing to the largest wind tunnel or open field situation (P < 0.001). Non-linear regression equations were developed using four wind tunnels data for velocity boundary layer thickness (δ_u), concentration boundary layer thickness (δ_c), and ammonia mass transfer coefficient (k_G) as a function of wind tunnel height (H) and average inlet velocity (u_m), which given clear quantitative indication of effects of H, and u_m on δ_u, δ_c, and k_G, respectively.     

A new integrated CFD modelling approach towards air-assisted orchard spraying—Part II: Validation for different sprayer types

A computational fluid dynamics (CFD) model to simulate airflow from air-assisted orchard sprayers through pear canopies was validated for three different sprayers; single-fan (Condor V), two-fan (Duoprop) and four-fan sprayers (AirJet Quatt). The first two sprayers are widely used in Belgium and the latter one is a new design. Validation experiments were carried out in an experimental orchard (pcfruit, Velm, Belgium) in spring 2008. Ultrasonic anemometers were used to measure the time-averaged velocity components at different vertical positions before the tree and after the tree when the sprayers were driven through the orchard. The model was able to predict accurately the peak jet velocity, U_m from all the sprayers considered at all distances from the sprayer centre and vertical positions. More than 95% of the local relative errors of U_m were below 20%. Average relative errors, E, and root mean square errors, E_RMS, were all less than 11.04% and 1.68 m s⁻¹, respectively. The regions of high- (up to 18.0 m s⁻¹ upstream) and low (down to 2.8 m s⁻¹ downstream)-air velocity zones for all the sprayers were accurately predicted. The simulation results showed that the Condor V sprayer had a highly disturbed vertical jet velocity profile, especially at higher heights. The Duoprop sprayer had high jet velocities at the two-fan positions and lower jet velocity in between the two fans. Within the canopy height the AirJet Quatt sprayer showed a more uniform distribution of air than the other two sprayers except the minor peaks at the fan positions. These situations were all confirmed by the measurements.     

Prediction of yield and the contribution of legumes in legume-grass mixtures using field spectrometry

Productivity and botanical composition of legume-grass swards in rotation systems are important factors for successful arable farming in both organic and conventional farming systems. As these attributes vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate more targeted management of crops and nutrients in the soil–plant–animal system. Two pot experiments were conducted to examine the potential of field spectroscopy to assess total biomass and the proportions of legume, using binary mixtures and pure swards of grass and legumes. The spectral reflectance of swards was measured under artificial light conditions at a sward age ranging from 21 to 70 days. Total biomass was determined by modified partial least squares (MPLS) regression, stepwise multiple linear regression (SMLR) and the vegetation indices (VIs) simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP). Modified partial least squares and SMLR gave the largest R ² values ranging from 0.85 to 0.99. Total biomass prediction by VIs resulted in R ² values of 0.87–0.90 for swards with large leaf to stem ratios; the greatest accuracy was for EVI. For more mature and open swards VI-based detection of biomass was not possible. The contribution of legumes to the sward could be determined at a constant biomass level by the VIs, but this was not possible when the level of biomass varied.     

Estimating the mass of mango fruit (Mangifera indica, cv. Chok Anan) from its geometric dimensions by optical measurement

Uniform fruit mass and shapes of mangoes for export are required in order to ease handling and transportation, and to help satisfy consumer preferences. However, with its irregular shape, one which cannot be approximated by using standard geometrical figures, the mango is not as easy to model as round or oval-shaped fruits.In this study, more than 1000 mango ‘Chok Anan’ fruit were measured with respect to their length, maximum width and maximum thickness, to within an accuracy of 1.0  mm, and their weight was determined to within an accuracy of 0.1  g. Pictures were taken of 30 fruit, through the use of digital cameras, manually rectified and analyzed by use of “Measure 2.1” a computer code.An equation was proposed in order to calculate the mass of the mangoes based on three geometric dimensions, which then showed a high level of explanatory power of R² = 0.97. The root mean standard error of 12.22 was determined, and the mean relative deviation found was 0.05. Based on measurements taken from the digital photos, the equation was then used to determine the estimated mass based on three dimensions. Estimated and measured values were plotted against each other. A high correlation (R² = 0.96) was found between measured and calculated mass.It was shown that the mass can be estimated based on data obtained from two orthogonally taken photographs.     

Evaluation of the precision of the rising plate meter for measuring compressed sward height on heterogeneous grassland swards

Accurate estimation of herbage mass (HM) is essential for optimising grass utilisation and increasing profit for pasture-based livestock agriculture. The rising plate meter (RPM) is used for predicting HM based on average compressed sward height (CSH). Sampling resolution and distribution are primary parameters in determining spatial heterogeneity of HM. There is no definitive sampling protocol for the RPM. The objectives of this study were to: investigate spatial variation of HM within pastures, determine the number of RPM measurements required to accurately predict mean HM, and assess the precision of the RPM in terms of measurement repeatability. Intensive CSH measurements and HM reference cuts were carried out on controlled plots and grazed paddocks over two grazing seasons. Sward heterogeneity was estimated as the coefficient of variation (CV) of CSH and compared to empirically derived ‘true’ sward heterogeneity in terms of HM CV. Retrospective analysis simulations were performed to identify the effect of various reduced measurement resolutions on estimated mean CSH error. Repeated measures analysis was performed on grass samples to determine RPM measurement system precision. Results indicated that pasture heterogeneity varied by 36% across the growing season and was affected by grazing, fertilisation, sward composition and seasonality. Mean CSH could be estimated to within 5% relative prediction error by recording 24 measurements per ha in a random stratified manner. The standard deviation of RPM measurement repeatability was calculated to be 4.34 mm. The findings of this study will be used to inform the implementation of a more optimum grass measurement protocol.

     

A new integrated CFD modelling approach towards air-assisted orchard spraying. Part I. Model development and effect of wind speed and direction on sprayer airflow

The current trend in modelling flow phenomena within trees such as in orchards follows the assumption of the space occupied by the trees as a porous and horizontally homogeneous medium to avoid the flow details associated with the individual plants. This being sufficient at a larger field or regional scale much has to be done at a plant scale to analyse the flow details within the plant and its elements especially for sensitive agricultural operations such as spraying. This article presents an integrated 3D computational fluid dynamics (CFD) model of airflow from a two-fan air-assisted cross-flow orchard sprayer through non-leafed orchard pear trees of 3 m average height. In this model the effect of the solid part of the canopy on airflow was modelled by directly introducing the actual 3D architecture of the canopy into the CFD model. The effect of small canopy parts, such as very short and thin branches and flowers that were not incorporated in the geometrical model, on airflow was simulated by introducing source-sink terms in the Reynolds averaged Navier-Stokes (RANS) momentum and k-? turbulence equations in a sub-domain created around the branches. This model was implemented in a CFD code of ANSYS-CFX-11.0 (ANSYS, Inc., Canonsburg, PA, USA). In this work it was possible to link the real 3D architecture of pear canopy into a CFD code of CFX. The model was able to capture the local effects of the canopy and its parts on wind and sprayer airflow directly by inserting the tree structure into the model which gave realistic results. The model showed that within the injection region of the sprayer there was an average reduction of the jet velocity by 1 m s⁻¹ for a distance of 2.3 m from the sprayer outlet due to the presence of leafless pear canopy. This reduction was variable at different vertical positions due to the difference in the canopy density. Maximal effect of the canopy was observed in the middle height of the trees between 0.25 m and 2.5 m which is the denser region with a bunch of several branches. The maximum velocity difference observed between these two positions was 1.35 m s⁻¹ at 1.75 m height. Thus, regions of high and low air velocity zones of the sprayer due to the variable branch density of the pear tree were predicted. The effects of wind speed and direction on the air jet from the sprayer were investigated using the model. For a cross- (direction of 90°) wind speed of 5 m s⁻¹ there was about 2 m s⁻¹ reduction in the sprayer jet velocity at the jet centre and 0.5 m horizontal shift of the jet centre towards the wind direction. Generally there was a decrease in the jet velocity with increasing cross-wind and decreasing wind direction with respect to the jet direction.     

Quantitative analysis of minerals and electric conductivity of red grape homogenates by near infrared reflectance spectroscopy

The use of near infrared (NIR) reflectance spectroscopy to measure the concentration of minerals and electric conductivity (EC) in red grape homogenates was investigated. Wine grape samples (n = 209) from two vintages, representing a wide range of varieties and regions were analysed by Inductively Coupled Plasma Optical Emission Spectrometry (ICPOES) for the concentrations of calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), sulphur (S), iron (Fe), and manganese (Mn) and scanned in reflectance in a NIR instrument (400-2500 nm). The spectra were pre-processed using multiple scatter correction (MSC) before developing the calibration models using partial least squares (PLS) regression and cross validation. Coefficients of determination in cross validation (R²) and the standard errors of cross validation (SECV) obtained were for Fe (0.60 and 1.49 mg kg⁻¹), Mn (0.71 and 0.41 mg kg⁻¹), Ca (0.75 and 60.89 mg kg⁻¹), Mg (0.84 and 12.93 mg kg⁻¹), K (0.78 and 285.34 mg kg⁻¹), P (0.70 and 40.19 mg kg⁻¹), S (0.88 and 14.45 mg kg⁻¹) and EC (0.87 and 7.66 mS). The results showed that Mg, S and EC in grape berries might be measured by NIR reflectance spectroscopy.     

Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae)

Detection of crop stress is one of the major applications of hyperspectral remote sensing in agriculture. Many studies have demonstrated the capability of remote sensing techniques for detection of nutrient stress on cotton with only few on pest damage but none so far on leafhopper (LH) severity. Subsequent to introduction of Bt cotton, leafhopper is emerging as a key pest in several countries. In view of its wide host range, geographical distribution and damage potential, a study was initiated to characterise leafhopper stress on cotton, identify sensitive bands, and derive hyperspectral vegetation indices specific to this pest. Cotton plants with varying levels of LH severity were selected from three locations across major cotton growing regions of India. About 57-58 cotton plants from each location exhibiting different levels of LH damage symptoms were selected. Reflectance measurements in the spectral range of 350-2500 nm were made using hyperspectral radiometer. Simultaneously chlorophyll (Chl) and relative water content (RWC) were also estimated from the selected plants. Reflectance from healthy and leafhopper infested plants showed a significant difference in VIS and NIR regions. Decrease in Chl a pigment was more significant than Chl b in the infested plants and the ratio of Chl a/b showed a decreasing trend with increase in LH severity. Regression analysis revealed a significant linear relation between LH severity and Chl (R² = 0.505^∗∗), and a similar fit was also observed for RWC (R² = 0.402^∗∗). Plotting linear intensity curves between reflectance at each waveband with infestation grade resulted in six sensitive bands that exhibited maximum correlation at different regions of the electromagnetic spectrum (376, 496, 691, 761, 1124 and 1457 nm). Regression analysis of several ratio indices formulated with two or more of these sensitive bands led to the identification of new leaf hopper indices (LHI) with a potential to detect leafhopper severity. These new indices along with 20 other stress related hyperspectral indices compiled from literature were further tested for their ability to detect LH severity. Two novel indices LHI 2 and LHI 4 proposed in this study showed significantly high coefficients of determination across locations (R² range 0.521 to 0.825^∗∗) and hence have the potential use for detection of leafhopper severity in cotton.     

Aerodynamic analysis and CFD simulation of several cellulose evaporative cooling pads used in Mediterranean greenhouses

The present work makes an aerodynamic analysis and computational fluid dynamics (CFD) simulation of the four commercial models of corrugated cellulose evaporative cooling pads that are most widely used in Mediterranean greenhouses. The geometric characteristics of the pads have been determined as well as the volume of water they retain at different flows of water, thus obtaining the mean thickness of the sheet of water which runs down them and their porosity. By means of low velocity wind tunnel experiments, the pressure drop produced by the pads has been recorded at different wind speeds and water flows. In this way it has been possible to obtain the relationship of the permeability and the inertial factor with pad porosity using a cubic type equation. Finally, a CFD simulation with a 3D model has been carried out for both dry pads (Q_w = 0 l s⁻¹ m⁻²) and wet ones (Q_w = 0.256 l s⁻¹ m⁻²), finding good correlation between the simulated and experimental pressure drop, with maximum differences of 9.08% for dry pads and 15.53% for wet ones at an airspeed of 3 m s⁻¹.     

Extended-altitude, aerial mapping of crop NDVI using an active optical sensor: A case study using a Raptor™ sensor over wheat

Recently reported testing of active, optical crop sensors in low-level aircraft have demonstrated a new class of airborne sensing system that can be deployed under any ambient illumination conditions, including at night. A second-generation, high-powered, light-emitting diode system has been assembled and tested over a 80 ha field of wheat (Triticum aestevum) by mapping the normalised difference vegetation index (NDVI) at altitudes ranging from 15 to 45 m above the canopy; significantly higher altitudes than existing systems. Comparisons with a detailed on-ground NDVI survey indicated the aerial sensor values were highly correlated to the on-ground sensor (0.79 < R² < 0.85), with close to unity slope and zero offset. The maximum average deviation between aerial and on-ground NDVI values was 0.04. Sample calculations involving two exemplar algorithms, one for estimating monoculture pasture biomass and the other for estimating wheat yield, indicate such deviations to have no significant effect on prediction accuracy. The subsequent NDVI maps proved to be invariant to sensor height over the 15-45 m altitude range meaning this new sensor configuration can be deployed over undulating crops and pastures and in areas with nearby obstacles such as trees and buildings.     

Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat

Hyperspectral data sets contain useful information for characterizing vegetation canopies not previously available from multi-spectral data sources. However, to make full use of the information content one has to find ways for coping with the strong multi-collinearity in the data. The redundancy directly results from the fact that only a few variables effectively control the vegetation signature. This low dimensionality strongly contrasts with the often more than 100 spectral channels provided by modern spectroradiometers and through imaging spectroscopy. With this study we evaluated three different chemometric techniques specifically designed to deal with redundant (and small) data sets. In addition, a widely used 2-band vegetation index was chosen (NDVI) as a baseline approach. A multi-site and multi-date field campaign was conducted to acquire the necessary reference observations. On small subplots the total canopy chlorophyll content was measured and the corresponding canopy signature (450-2500 nm) was recorded (n_obs = 42). Using this data set we investigated the predictive power and noise sensitivity of stepwise multiple linear regression (SMLR) and two ‘full spectrum’ methods: principal component regression (PCR) and partial least squares regression (PLSR). The NDVI was fitted to the canopy chlorophyll content using an exponential relation. For all techniques, a jackknife approach was used to obtain cross-validated statistics. The PLSR clearly outperformed all other techniques. PLSR gave a cross-validated RMSE of 51 mg m⁻² for canopy chlorophyll contents ranging between 38 and 475 mg m⁻² (0.99 ≤ LAI ≤ 8.74 m² m⁻²). The lowest accuracy was achieved using PCR (RMSE_cv = 82 mg m⁻² and ). The NDVI, even using chlorophyll optimized band settings, could not reach the accuracy of PLSR. Regarding the sensitivity to artificially created (white) noise, PCR showed some advantages, whereas SMLR was the most sensitive chemometric technique. For relatively small, highly multi-collinear data sets the use of partial least square regression is recommended. PLSR makes full use of the rich spectral information while being relatively insensitive to sensor noise. PLSR provides a regression model where the entire spectral information is taken - in a weighted form - into account. This method seems therefore much better adapted to deal with potentially confounding factors compared to any 2-band vegetation index which can only avoid the most harmful factor of variation.     

The effects of step-wise improvement of forage combination in total mixed rations on fatty acid profile in the rumen and milk of Holstein cows

Five lactating Holstein cows in a 5×5 Latin square experiment were fed five high-concentrate total mixed rations(TMRs) to investigate the effects of step-wise improvement of forage combination on ruminal and milk fatty acid profiles. The ratio of concentrate to forage was fixed as 61:39, and the step-wise improvement of forage combination was applied as: TMR1, a ration containing corn stover; TMR2, a ration containing corn stover and ensiled corn stover; TMR3, a ration containing ensiled corn stover and Chinese wild ryegrass hay(Leymus chinensis); TMR4, a ration containing the ryegrass hay and whole corn silage; TMR5, a ration containing the ryegrass hay, whole corn silage and alfalfa hay. The TMRs were offered to the cows twice daily at 0700 and 1900 h. The entire experiment was completed in five periods, and each period lasted for 18 days. Diurnal samples of rumen fluids were collected at 0100, 0700, 1300 and 1900 h(day 16); 0300, 0900, 1500 and 2100 h(day 17); and 0500, 1100, 1700 and 2300 h(day 18). The step-wise improvement of forage combination increased energy and crude protein contents and decreased fibre content. As a result, the step-wise improvement of forage combination increased dry matter intake and milk yield(P0.05). The step-wise improvement increased dietary content of linolenic acid(C18:3 n-3), but did not alter dietary proportions of palmitic acid(C16:0), stearic acid(C18:0), oleic acid(C18:1 cis-9), linoleic acid(C18:2 n-6) and arachidic acid(C20:0). In response to the forage combination, ruminal concentration of C16:0, C18:2 n-6 and C18:3 n-3 linearly increased against their dietary intakes(P0.10). The step-wise improvement increased milk contents of C10:0, C12:0, C14:0, C16:0, C18:2 n-6 and C18:3 n-3(P0.10) and decreased milk contents of C18:0 and C18:1 cis-9(P0.05). Milk yields of C16:0, C18:1 cis-9, C18:2 n-6 and C18:3 n-3 were linearly increased by the increase of these fatty acids in the rumen(R2≥0.79, P0.05), and milk yields of C18:2 n-6 and C18:3 n-3 were also positively correlated with dietary intake of these fatty acids(R2≥0.85, P0.05). The step-wise improvement increased the transfer efficiencies from feed to milk for C18:2 n-6 from 11.8 to 14.2% and for C18:3 n-3 from 19.1 to 22.3%. In a brief, along with the step-wise improvement of forage combination, more dietary linoleic and linolenic acids might escape microbial hydrogenation in the rumen and consequently accumulated in milk fat though these fatty acids were present in low concentrations in ruminal fluids. The step-wise improvement of forage combinations could be recommended as a dietary strategy to increase the transfer efficiency of linoleic and linolenic acids from feed to milk.     

EM38 for volumetric soil water content estimation in the root-zone of deep vertosol soils

Electromagnetic induction sensors, such as EM38, are used widely for monitoring and mapping soil attributes via the apparent electrical conductivity (EC_a) of the soil. The sensor response is the depth-integrated combination of the depth-response function of the EM38 and ‘local’ electrical conductivity (EC_az) at depth. In deep, Vertosol soils, assuming the instrument depth-response function is not perturbed by the soil and where volumetric moisture content at depth (θ_v(z)) dominates EC_az, EM38 should be capable of predicting average moisture content without recourse to mathematically complicated, and unstable profile inversion processes. Firstly a multi-height EM38 experiment was conducted over deep Vertosol soils to confirm the veracity of the EM38 depth-response function and test the concomitant hypothesis of the EM38 response being an integrated (i.e. additive) combination of depth-response function and θ_v(z). Secondly, depth profiles of moisture content were used to calibrate the EM38 to infer average θ_v(z) within the ‘root-zone’ of crop plants—here taken to be surface—0.8 m and surface—1.2 m. EM38 calibration was performed using soil samples acquired from both extracted cores and excavated pits. Mathematical summation of measured θ_v(z) from sectioned cores and the known depth-response function of the EM38 was found to explain 99% and 97% of the variance in measured EC_a for horizontal and vertical dipole configurations at multiple sensor heights above the ground. Average θ_v from surface to 0.8 m () and surface to 1.2 m () explained only 37% and 46% of the variance in on-ground EC_a for vertical dipole configuration measurements compared to 55% and 56% of the variance for horizontal dipole configuration. In a separate validation experiment, the shape of the vertical moisture profile proved highly influential in determining the ability of the calibration equations to infer underlying average moisture content, especially where the depth profile shapes differed between sensor calibration and subsequent field validation (for example following rainfall or irrigation).     

Determination of visual quality of tomato paste using computerized inspection system and artificial neural networks

An artificial neural network (ANN) integrated computerized inspection system (CIS) was developed to determine tomato paste color in CIE L^∗, a^∗, and b^∗ color format and the number and size of dark specks which exist in the product. The usability of CIS in the determination of the number and the size of dark specks in tomato paste were investigated by comparing the results of CIS and human inspectors. While the inspectors had difficulties not only in determination of the specks having a diameter less than 0.2 mm but also in correct diameter measurement for all specks, the CIS had good determination and measurement capability. In 99 tomato paste samples, the number of the specks having diameter more than 0.2 mm were found by human inspectors and CIS as 233 and 235, respectively. However, the manual inspection gave inaccurate results for the diameter measurement of the specks. In the color evaluation of the tomato paste, strong correlations (R) were found between the results estimated from ANN-integrated CIS and those obtained from colorimeter (0.889, 0.958, 0.907 and 0.987 for L^∗, a^∗, b^∗ and a^∗/b^∗, respectively). The whole system is adapted to a graphical user interface (GUI) for use by a non-skilled person working in the tomato paste sector. While manual methods need approximately 5 min, GUI needs 20-25 s to determine, count and classify the dark specks and to measure the product color.     

2.4GHz无线信号在苹果园中的衰减模型

为解决苹果园中无线传感器网络的规划和部署问题,研究2.4GHz无线信道在苹果园中的传播特性。在山东省肥城市普通的苹果园进行实地试验。选取对信号传播影响最大的一列果树,发射天线固定在两棵树之间发射信号,分别测量6个高度18个位置点的接收信号强度和丢包率。回归分析结果表明:无论发射天线多高,不同水平高度上的接收信号强度衰减均符合对数路径损耗模型,拟合的决定系数为0.927~0.987。发射天线高度不变时,衰减系数n值能用接收天线高度的二次函数曲线拟合,拟合的决定系数为0.71~0.89;模型参数A和接收天线高度符合线性关系,拟合的RMSE为0.2~1.2。建立以发射天线高度、接收天线高度和传播距离为参数的衰减模型并进行验证试验,结果表明:RMSE为2~5,94%的R2值大于0.9,预测模型能较好的估算收发天线高度不同时的信号强度损耗。     

Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maize crops

LiDAR (Light Detection And Ranging) is a remote-sensing technique for the measurement of the distance between the sensor and a target. A LiDAR-based detection procedure was tested for characterisation of the weed vegetation present in the inter-row area of a maize field. This procedure was based on the hypothesis that weed species with different heights can be precisely detected and discriminated using non-contact ranging sensors such as LiDAR. The sensor was placed in the front of an all-terrain vehicle, scanning downwards in a vertical plane, perpendicular to the ground, in order to detect the profile of the vegetation (crop and weeds) above the ground. Measurements were taken on a maize field on 3 m wide (0.45 m²) plots at the time of post-emergence herbicide treatments. Four replications were assessed for each of the four major weed species: Sorghum halepense, Cyperus rotundus, Datura ferox and Xanthium strumarium. The sensor readings were correlated with actual, manually determined, height values (r² = 0.88). With canonical discriminant analysis the high capabilities of the system to discriminate tall weeds (S. halepense) from shorter ones were quantified. The classification table showed 77.7% of the original grouped cases (i.e., 4800 sampling units) correctly classified for S. halepense. These results indicate that LiDAR sensors are a promising tool for weed detection and discrimination, presenting significant advantages over other types of non-contact ranging sensors such as a higher sampling resolution and its ability to scan at high sampling rates.     

Development of model based sensors for the supervision of a solar dryer

Solar dryers are increasingly used in developing countries as an alternative to drying in open air, however the inherent variability of the drying conditions during day and along year drive the need for achieving low cost sensors that would enable to characterize the drying process and to react accordingly. This paper provides three different and complementary approaches for model based sensors that make use of the psychrometric properties of the air inside the drying chamber and the temperature oscillations of the wood along day. The simplest smart sensor, Smart-1, using only two Sensirion sensors, allows to estimate the accumulated water extracted from wood along a complete drying cycle with a correlation coefficient of 0.97. Smart-2 is a model based sensor that relays on the diffusion kinetics by means of assesing temperature and relative humidity of the air inside the kiln. Smart-2 model allows to determine the diffusivity, being the average value of D for the drying cycle studied equal to 5.14 × 10⁻¹⁰ m² s⁻¹ and equal to 5.12 ×  10⁻¹⁰ m² s⁻¹ for two experiments respectively. The multidistributed supervision of the dryer shows up the lack of uniformity in drying conditions supported by the wood planks located in the inner or center of the drying chamber where constant drying rate kinetics predominate. Finally, Smart-3 indicates a decreasing efficiency along the drying process from 0.9 to 0.2