Crop acreage estimation is a key aspect to forecast crop production. Maize acreage estimation becomes more and more important because the fast production changes every year due to the dynamics of the prices. This paper focuses on maize acreage estimation in the North China Plain using ENVISAT MERIS and CBERS-02B CCD data of 2008. Firstly, adaptive maximum likelihood classification of CBERS-02B CCD images based on ground survey provided reliable maize area fraction image (AFI). CBERS derived AFIs (as reference AFI) were used to train a 3-layer back-propagation neural network, this was then used to the whole MERIS data to generate MERIS AFIs (AFIe). To estimate maize acreage, the maize AFI from MERIS was masked with cropland dataset and maize acreages were estimated by zonal statistic of maize AFI at district level. The statistical results were also modified using a non-arable coefficient to remove the effects of non-arable factors. The results showed a close relationship between estimated and statistical maize acreage (R2 ≈ 0.88). At province level, the estimation error is approximately 8%. This method is valuable for wide-scale, regional crop acreage estimation at the early stage of growing season. The study gives suggestions about high resolution image acquisition, spatial distribution and cropland datasets.Highlights► Valuable for wide-scale, regional crop acreage estimation at the early stage. ► Maize acreage estimation using ENVISAT MERIS and CBERS-02B CCD data. ► Crop acreage estimation combining high and coarse spatial resolution data.
Of late, a series of methods and tools have evolved in order to assist farmers in making decisions, based on the development of computers. In this paper, an attempt has been made to focus on simulation tools as a means to expand interactivity. The first part deals with the understanding of interactivity. Then the use of simulation as a support for interactivity is developed. In this endeavor, the evolution of understanding of the role played by simulation tools for farm management decision-making has been considered crucial, based on experiments carried out in an interactive manner with both farmers and consultants. Finally, we introduce and discuss numerous potential opportunities provided by the new techniques through automatic machine learning and multi-agent modeling.
A review is presented of several potentially useful applications of artificial neural networks (NN) to greenhouse climate control. Subjects covered are: Quasi-steady-state modelling, reduction (compression) of input and state vectors, NNs used as difference equations and replacing controllers (algorithms or humans) with NNs. In this context the strength of NNs is their flexibility to adapt to non-linear and non-physical data. Their main disadvantage is that their proper training requires large multi-dimensional sets of data to reduce the risk of extrapolation. Therefore, minimizing the dimensionality of the problem (both input and state vectors) becomes of paramount importance. Bottleneck NNs may be used for this purpose.
This paper describes a project to enhance farm level computer use education entitled The Year 2000 Computerized Farm. The farm management information system implemented at the Stiles Foundation Farm is described as well as its effect on farm profits. An evaluation of the short courses taught at the farm provide an insight as to the approach and results of that educational effort. The suggestion is made that other educational institutions could follow a similar approach for staff and user training.
A brief report is given of the field robot contest held in Wageningen, 5–6 June 2003. The experience of the student competitors sheds light on a number of issues that need to be addressed before autonomous vehicles will become a reality.
A preliminary study on the potential application of electronic tracking in poultry in vivo has been conducted. The experimental procedure for this study was based on previous in vitro findings (Fröschle et al., 2009) as part of the same research programme. The study consisted of two phases whereby an initial experiment using inkjet printing of 10 × 10 DataMatrix barcodes onto the beaks of broiler chickens in a live commercial setting has been carried out. Results demonstrated very poor percentage of readability after a short period of time. Barcodes deteriorated very rapidly and this was attributed to the physical effects on the barcodes of the actions of the chickens in a commercial environment, together with the inability of the ink to bond to the hard keratinous surface of the beak. In a subsequent part of the study, a number of commercially available ink types were screened, using a predetermined abrasion testing procedure, for their ability to bond to the beak and provide a readable barcode on the beaks following some predetermined graduated physical abrasion.
Contemporary Precision Livestock Technology in poultry production is very limited and does not meet European standards for traceability and Best Available Technology (BAT), as laid down in EN ISO 2205:2007 standards (2007) and the European Directive 2008/1/EC (2008). A worldwide occurrence of Avian Influenza additionally calls for a fraud-proof tagging device and source verification system for poultry and poultry products in order to complete partially existing documentary trails.During a preliminary laboratory trial, a procedure for the application of miniature linear and two-dimensional Data Matrix (DM) barcodes onto poultry beaks and legs through inkjet printing was set up and assessed. Results regarding the proportion of readability (p%), the standard error in readability (SE) and general statistics on the reading time were calculated. Tests for independence based on Chi-square and Pearson's were performed on the categorical data, to estimate the differences between proportions of readability of reading groups. The resulting data was used to define the optimal position of barcodes as well as the optimal reading mode of the barcode scanner to be used for further trials. As this experiment provided an estimate of readability of barcodes imprinted on chicken beaks and legs, it is intended to serve as a basis for sample size calculation for an ongoing live trial.
A tractor drawbar performance program that predicts the performance of two-wheel-drive (2WD) tractors for haulage as well as field operations for both bias-ply and radial-ply tyres is developed to meet user requirements in educational and research organizations. The program is written in Visual Basic programming language. The program provides an intuitive user interface by linking databases such as tractor specifications, tyre data, implement and trailer specifications and traction equation coefficients to predict the performance of a selected tractor model. The program has been proven to be user friendly and efficient for various field operations under frictional–cohesive soils.
Recent advances in sensor and wireless radio frequency (RF) technologies and their convergence with the Internet offer vast opportunities for development and application of sensor systems for agriculture. The objective was to create regional and on-farm sensor networks that provide remote, real-time monitoring and/or control of important farming operations that add value through improved efficiency and efficacy of targeted management practices. This paper describes hardware and software components of technologies we developed for regional and on-farm sensor networks and their implementation in two agricultural applications in Washington State, an agricultural weather network and an on-farm frost monitoring network. The regional sensor network consists of our AWN200 data logger equipped with a 900 MHz, frequency hopping, spread spectrum (FHSS) radio configured into master–repeater–slave network for broad geographic coverage. A single master is configured with multiple repeaters to provide a RF line-of-sight telemetry backbone network. Independent network backbones from disparate geographic regions are then aggregated in a central database via standard Internet protocols for further processing and dissemination. Software includes firmware to operate the data logger and radio telemetry aspects of the AWN200 in an agricultural weather network application called AgWeatherNet (http://www.weather.wsu.edu). The on-farm sensor network uses our SS100 radio/logger which includes a 900 MHz, FHSS radio, with software designed primarily for mobile, real-time farm operations and management applications. The network is deployed in a star topology in which a strategically placed base radio is responsible for network synchronization, data collection from remote stations within the network, and re-broadcasting collected data to roamer radio units attached to mobile computers and/or directly to the Internet. Client software, AgFrostNet, operating on a computer connected to a roamer, collects, manages, and display data in real-time. This software was designed specifically for air temperature monitoring during frost/freeze protection events. Both the regional AgWeatherNet WSN and the on-farm AgFrostNet networks were successfully implemented in Washington State. Problems encountered were mainly associated with power management under periods of low solar energy and with electrostatic discharge (ESD) damage to gallium-arsenide (GaAs) based transmit–receive switches in the radios during storms, a problem now corrected. Both systems have been made commercially available to growers via a novel arrangement between WSU and a local manufacturer.
In order to investigate and understand drift from field sprayers, a steady state computational fluid dynamics (CFD) model was developed. The model was developed in 3D in order to increase the understanding of the causes of drift: a deviation in the wind direction cannot be captured by a 2D approach, the wake behind a wind screen is not symmetrical, the effects of a changed nozzle orientation may not be symmetrical. The model's accuracy was validated with field experiments carried out according to the international standard ISO 22866. A field sprayer with a spray boom width of 27 m and 54 nozzles (Hardi ISO F110-03 at 3 bar) was driving at 2.22 m/s over a flat pasture. During the experiments the wind direction was perpendicular to the tractor track. The model explained the variation in drift replicates during each single field experiment through varying boom height (0.3–0.7 m), wind velocity (1.3–2.5 m/s), wind deviation (−18° to +18°) from the direction perpendicular to the tractor track and injection velocity of the droplets (17–27 m/s). Boom movements had the highest impact on the variations in drift values (deviations in drift deposits of 25%), followed by variation in wind velocity (deviations in drift deposits of 3%) and injection velocity of the droplets (deviations in drift deposits of 2.5%). Wind deviation from the direction perpendicular to the tractor track had a reducing effect on the drift values (deviations in drift deposits of 2%). Small variations in driving speed had little influence on drift values. Near drift (<5 m) is predicted well by the model but the increased complexity compromised the predictions at greater distances. The model will be further developed in order to improve far drift prediction. Dynamic simulations will be performed and the model for turbulent dispersion will be optimized. The model did not require calibration.
A 3D stand generator and visualization system was developed for generating a spatially explicit forest for central Appalachian hardwood forests. Spatial pattern of the stand generator was modeled and validated by characterizing a 75-year old central Appalachian mixed hardwood forest dominated by red oak, chestnut oak, red maple, and yellow poplar. All the trees larger than 12.7 cm diameter at breast height (DBH) were measured for DBH, total height, crown height and crown width along with their locations in thirty 0.162 ha plots. Stand attributes, i.e. species compositions, mean DBH, total height, basal area and volume in the generated stand never exceeded a difference of 10% from the actual stand attributes. The generated stand can thus be used as an alternate to time consuming manual measurement of spatial location of trees for related ecological studies and training purposes and to visualize the same in 3D perspectives. The results also indicated the potential of using this stand generator in simulating stand spatial patterns and generating stands in other regions with some modification in growth functions.
Several approaches have been reported previously to identify internal log defects automatically using computed tomography (CT) imagery. Most of these have been feasibility efforts and consequently have had several limitations: (1) reports of classification accuracy are largely subjective, not statistical; (2) there has been no attempt to achieve real-time operation; and (3) texture information has not been used for image segmentation, but has been limited to region labeling. Neural network classifiers based on local neighborhoods have the potential to greatly increase computational speed, can be implemented to incorporate textural features during segmentation, and can provide an objective assessment of classification performance. This paper describes a method in which a multilayer feed-forward network is used to perform pixel-by-pixel defect classification. After initial thresholding to separate wood from background and internal voids, the classifier labels each pixel of a CT slice using histogram-normalized values of pixels in a 3 × 3 × 3 window about the classified pixel. A post-processing step then removes some spurious pixel misclassifications. Our approach is able to identify bark, knots, decay, splits, and clear wood on CT images from several species of hardwoods. By using normalized pixel values as inputs to the classifier, the neural network is able to formulate and apply aggregate features, such as average and standard deviation, as well as texture-related features. With appropriate hardware, the method can operate in real time. This approach to machine vision also has implications for the analysis of 2D gray-scale images or 3D RGB images.
The most common method for in situ assessment of soil salinity, namely the electrical conductivity (EC) of the soil solution (ECw), is to measure the apparent electrical conductivity (ECa) and volumetric water content (θ) of the soil and apply measured or predicted ECa(ECw, θ) calibration curves. The water content and electrical conductivity of a soil solution are indeed the major factors affecting its apparent electrical conductivity, which justifies the assessment of salinity from apparent EC measurements. However, the ECa(ECw, θ) relationship depends on some additional soil and environmental attributes affecting the soil ECa. Non-spherical particle shapes and a broad particle-size distribution tend to decrease ECa, and when non-spherical particles have some preferential alignment in space, the soil becomes anisotropic, i.e., its ECa depends on the direction in which it is measured. The electrical conductance of adsorbed counterions constitutes a major contribution to the ECa of medium- and fine-textured soils, especially under conditions of low solution conductivity. In such soils and with such salinity levels, the temperature response of the soil ECa should be stronger than that of its free solution, and care should be taken when extrapolating from field-measured ECa values to obtain the ECa at a given temperature. The above-mentioned and other secondary findings should, on one hand, indicate some limitations for the application of existing ECa–ECw models, and, on the other hand, can serve as guidelines for further development of such essential models.
Dairy farmers using automatic milking are able to manage mastitis successfully with the help of mastitis attention lists. These attention lists are generated with mastitis detection models that make use of sensor data obtained throughout each quarter milking. The models tend to be limited to using the maximum or average value of the sensor data pattern, potentially excluding other valuable information. They often put cows on the lists unnecessarily, and their sensitivity for abnormal milk classification is too low for automated separation. Therefore, we analyzed sensor data patterns within quarter milkings in order to identify potentially predictive variables for abnormal milk and clinical mastitis classification. The data used in this study was obtained at a commercial dairy farm in Germany in September 2002, where a German Simmental herd was milked by a Lely Astronaut system. In total, 3232 quarter milkings from 63 cows were analysed; 94 quarter milkings were defined as milk with abnormal homogeneity and 270 as clinical mastitis. A data flow diagram was developed to systematically describe the steps involved in the transformation of within quarter milking measurements into variables that potentially predict abnormal milk and clinical mastitis. Three types of pattern descriptors were used: level, variability, and shape. In addition to using the absolute value of the pattern descriptor, the descriptors were considered relative to their expected value based on pattern descriptor values from previous milkings and from other quarters within the same cow milking. Using this method, potentially predictive variables were computed for electrical conductivity, the colours red, green and blue, a combination of colour sensors, and milk production. The importance of a variable in predicting abnormal milk and clinical mastitis was evaluated by computing correlation coefficients as well as information gain ratios. The most important variables came from the sensors for electrical conductivity, blue and green. Variables describing the variability and shape of the measurement patterns were as important as mean and maximum values, and should be included in future modelling. Also variables that are based on absolute values should be considered for future modelling. Results suggest that clinical mastitis and abnormal milk classification models may include similar predictive variables, but requirements for these models differ resulting in the need for different models. The schematic approach to developing potentially predictive variables will be helpful when exploring the usefulness of new sensors, researching other approaches to estimate expected values, and studying sensor data patterns in general.
Thinning is a silvicultural practice to improve tree growth and health. Thinning from below for the even-aged silviculture and thinning from above for the uneven-aged silviculture are the two mainly applied thinning practices. In forest management simulations, algorithms that describe which individual trees to be removed from a forest have developed in five growth simulators (Söderbergh and Ledermann, 2003). We have developed a shifting algorithm that determines the proportion of trees to be thinned from different diameter classes to complement the individual tree selection algorithms. Sampled (or mapped) tree diameters are grouped into diameter classes. Given the target thinning volume, the algorithm automatically computes the thinning rate in each of the diameter classes using the three-parameter Weibull distribution. The thinning rate is obtained by shifting the location parameter of an estimated Weibull distribution either to the right or to the left for thinnings from below and above, respectively. A modified bisection method is used to search for the new location parameter that yields the desired thinning volume. The proposed algorithm is demonstrated in examples by using experimental forest datasets. A stand-alone program called Weibull_thinning is downloadable at http://www.it.abo.fi/suswood/weibull_thinning/.Research highlights▶ A shifting algorithm to compute thinning rates by using the Weibull distribution for thinnings from below and above is presented. ▶ The shifting algorithm complemented the individual tree selection algorithms in published simulators. ▶ A modified bisection method is used to find the root, i.e., the target thinning volume.
The Russian wheat aphid (Diuraphis noxia (Mordvilko)) infests wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and other small grains and grasses. Russian wheat aphid infestations are unpredictable in time and space. In favorable conditions, Russian wheat aphid feeding can result in heavy damage to wheat and barley in a short period of time. A repetitive monitoring strategy that allows for rapid assessment of aphid infestation and damage over the growing season is critically needed. Tracking the irregular infestation patterns of Russian wheat aphid in order to optimize control efforts is central to the successful management of this aphid. One method that has been shown over a number of years to be useful for monitoring some insect outbreaks is to measure the light reflected by the infested canopy, plant, or leaf. Hence, this research was designed to investigate: (1) the potential use of remotely sensed data to discern and identify differences in spectral reflection patterns (spectral signatures) of winter wheat canopies with and without Russian wheat aphid infestation, and (2) the relationship between spectral indices and Russian wheat aphid abundance in wheat canopies growing in field conditions. Russian wheat aphid-infested wheat canopies had significantly lower reflectance in the near infrared region and higher in the visible range of the spectrum when compared with noninfested canopies. Linear regression analyses showed that there were varying relationships between Russian wheat aphid density and spectral vegetation indices, with coefficients of determination (r2) ranging from 0.91 to 0.01. These results indicate that remote sensing data have the potential to distinguish damage by Russian wheat aphid and quantify its abundance in wheat. However, success for Russian wheat aphid density estimation depends on the selection of spectral vegetation indices.
A particle acceleration device for implanting DNA material into plant cells using a timed burst of pressurized helium gas was developed. The device did not require the use of consumable materials (other than helium and microparticles), as used in some similar systems, and was thus easier and less expensive to operate. The system was designed to provide more precise control of the duration of the helium burst and of pressure differentials to ensure more reproducible results. Safety interlock circuitry was incorporated. The device was found to be efficient for delivery of DNA into alfalfa and soybean cell cultures.
Automatic monitoring of animal behavior in livestock production opens up possibilities for on-line monitoring of, among others, oestrus, health disorders, and animal welfare in general. The aim of this study is to use time series of acceleration measurements in order to automatically classify activity types performed by group-housed sows. Extracts of series collected for 11 sows are associated with 5 activity types: feeding (FE), rooting (RO), walking (WA), lying sternally (LS) and lying laterally (LL). A total of 24 h of three-dimensional series is used. One univariate model and four multivariate models are used to describe all five activity types. Three multivariate models differ in their variance/covariance structure; a fourth alternative multivariate model (MU) simply combines the 3-axes of the univariate model, assuming independence. For each model, the activity-specific parameters are estimated using the EM algorithm. The classification method, based on a Multi-Process Kalman Filter provides posterior probabilities for each of the 5 activities, for a given series. For the univariate model, LL is the activity which is best recognized by the 3-axes; FE, RO and WA are best recognized by one particular axis; LS is poorest recognized. The average results are improved by using all four types of multivariate models. The percentages of activity recognition are similar among the multivariate models. By grouping the activity types into active (FE, RO, WA) vs. passive (LS, LL) categories, the method allows to correctly classify 96% of the active category and 94% of the passive category.
Cattle behavior is potentially a valuable indicator of health and well-being; however, natural movement patterns can be influenced by the presence of a human observer. A remote system could augment the ability of researchers, and eventually cattle producers, to monitor changes in cattle behavior. Constant video surveillance allows non-invasive behavior monitoring, but logging the movement patterns on individual animals over long periods of time is often cost prohibitive and labor intensive. Accelerometers record three-dimensional movement and could potentially be used to remotely monitor cattle behavior. These devices collect data based on pre-defined recording intervals, called epochs. Our objectives were to (1) determine if accelerometers can accurately document cattle behavior and (2) identify differences in classification accuracy among accelerometer epoch settings. Video-recorded observations and accelerometer data were collected from 15 crossbred beef calves and used to generate classification trees that predict behavior based on accelerometer data. Postural orientations were classified as lying or standing, while dynamic activities were classified as walking or a transition between activities. Video analysis was treated as the gold standard and logistic regression models were used to determine classification accuracy related to each activity and epoch setting. Classification of lying and standing activities by accelerometer illustrated excellent agreement with video (99.2% and 98.0% respectively); while walking classification accuracy was significantly (P < 0.01) lower (67.8%). Classification agreement was higher in the 3 s (98.1%) and 5 s (97.7%) epochs compared to the 10 s (85.4%) epoch. Overall, we found the accelerometers provided an accurate, remote measure of cattle behavior over the trial period, but that classification accuracy was affected by the specific behavior monitored and the reporting interval (epoch).
Theoretically, using information about crop growth would allow the extension of present greenhouse control strategies towards a truly economic optimal control strategy. A brief survey is given of developments in the scientific literature. A full solution would require to consider the long-term crop development as well as all relevant short-term dynamics of the crop, the greenhouse and the external weather. Obstacles for the acceptance of such solutions are briefly discussed. One of the key factors is the lack of reliable crop development models for the wide variety of crops encountered in practice, and the need to leave part of the decision freedom in the hands of the grower. An analysis is given of simplified approaches resulting from integrating the crop equations over a day or more. The temperature integral concept, a specific example of such approach, is gaining popularity, despite the fact that it lacks exploitation of knowledge about the fast crop responses. The discussion leads to the concept of separation of responsibilities, where the short-term effects, including photosynthesis and evapo-transpiration, are handled by an automated model-predictive optimal controller, while the long-term effects are left to the grower, with support from a flexible decision support system based on crop models whenever they become available.
A relational database was developed for the agricultural chemical use data collected by the US Department of Agriculture, National Agricultural Statistics Service since 1990. coldfusion Markup Language was used for the client-side interface and server side process programming. The database is accessible from the Web at URL: http://www.pestmanagement.info/nass. Users can obtain information about agricultural chemical use in the database by search of crop, year, region, and active ingredient. Various agricultural chemical usage statistics are provided as Web tables, dynamically generated US maps, charts and graphs, and downloadable Excel files. We used a centralized software architecture in this project, which is suitable for projects with moderate programming complexity. A distributed approach might be more appropriate for the more complex projects. The current database information, spanning 1990–2001, will be augmented in the future, possibly using an automated updating scheme.
Writing down mathematical models of agricultural greenhouses and regulating
them via advanced controllers are challenging tasks since strong perturbations,
like meteorological variations, have to be taken into account. This is why we
are developing here a new model-free control approach and the corresponding
intelligent controllers, where the need of a good model disappears. This
setting, which has been introduced quite recently and is easy to implement, is
already successful in many engineering domains. Tests on a concrete greenhouse
and comparisons with Boolean controllers are reported. They not only
demonstrate an excellent climate control, where the reference may be modified
in a straightforward way, but also an efficient fault accommodation with
respect to the actuators.
This study compared two alternative techniques for predicting forest cover types from cartographic variables. The study evaluated four wilderness areas in the Roosevelt National Forest, located in the Front Range of northern Colorado. Cover type data came from US Forest Service inventory information, while the cartographic variables used to predict cover type consisted of elevation, aspect, and other information derived from standard digital spatial data processed in a geographic information system (GIS). The results of the comparison indicated that a feedforward artificial neural network model more accurately predicted forest cover type than did a traditional statistical model based on Gaussian discriminant analysis.
This paper seeks to establish the accuracy of an autonomous vehicle working in a field of transplanted cauliflowers. The main sensing systems, odometry and image analysis, are briefly described as is the control system which is based on a fusion of the two data sources using a Kalman filter. Experiments to establish accuracy are described. These were carried out on four plots of cauliflowers with varying degrees of disruption to the visual scene. The RMS error of vehicle lateral position control was 2- mm, while the RMS error of estimated vehicle position was about 10 mm. Little effect of the disruption on position control was observed. It is concluded that these accuracies would be sufficient to control a vehicle and an associated crop treatment device but that improvements to the vehicle controller would make the control of the treatment device easier.
A local position measurement system based on radar technology was set-up in a dairy cow free-stall barn. This system could potentially track up to 16,000 individual objects at a frequency of 300 position estimates/s. We describe the general steps for achieving positioning estimates and the transponder developed to be suitable for dairy cows. Measurements at fixed positions and data of dynamic circular measurements are provided, showing that estimates of the location of a transponder were within ≤0.5 m, regardless of whether it was moving or not. Such accurate position information can be used to track cows and to record their travel paths and their use of different areas of the barn. In addition, we tested the system's suitability for monitoring and quantifying social interactions. Though displacements of one cow by another seemed to result in characteristic patterns of changes in the relative distance between the two cows, most of the displacements did not follow this pattern closely enough to allow the automatic detection of displacements. By contrast, we show that the proximity between two cows recorded automatically with the positioning measurement system correlated well with the proximity recorded by direct observation of the cows, and provided a more detailed and exact record over the same period of time. There were no indications that wearing the transponder restricted the behaviour of the cows. In conclusion, the results of our evaluation suggest that the radar-based position measurement system is a useful tool for simultaneously recording the positions of all animals in large dairy-cow herds with great accuracy.
Soil apparent electrical conductivity (ECa) has been used as a surrogate measure for such soil properties as salinity, moisture content, topsoil depth (TD), and clay content. Measurements of ECa can be accomplished with commercially available sensors and can be used to efficiently and inexpensively develop the dense datasets desirable for describing within-field spatial variability in precision agriculture. The objective of this research was to investigate accuracy issues in the collection of soil ECa data. A mobile data acquisition system for ECa was developed using the Geonics EM381 sensor. The sensor was mounted on a wooden cart pulled behind an all-terrain vehicle, which also carried a GPS receiver and data collection computer. Tests showed that drift of the EM38 could be a significant fraction of within-field ECa variation. Use of a calibration transect to document and adjust for this drift was recommended. A procedure was described and tested to evaluate positional offset of the mobile EM38 data. Positional offset was due to both the distance from the sensor to the GPS antenna and the data acquisition system time lags. Sensitivity of ECa to variations in sensor operating speed and height was relatively minor. Procedures were developed to estimate TD on claypan soils from ECa measurements. Linear equations of an inverse or power function transformation of ECa provided the best estimates of TD. Collection of individual calibration datasets within each surveyed field was necessary for best results. Multiple measurements of ECa on a field were similar if they were obtained at the same time of the year. Whole-field maps of ECa-determined TD from multiple surveys were similar but not identical. There was a significant effect of soil moisture and temperature differences across measurement dates. Classification of measurement dates as hot vs. cold and wet vs. dry provided TD estimations nearly as accurate as when individual point soil moisture and temperature data were included in the calibration equation.
A low-cost, non-differentially corrected hand-held GPS receiver was tested on an industrial peat production bog. A correction procedure (‘pseudo-differential correction’) was derived that corrected data points to the nearest position on a line defining the centre of each 15-m wide field. The result was a corrected log of track points for each field for all points lying along the field. It was found that the mean orthogonal distance from a field centreline was linearly correlated with mean uncorrected GPS data error (r2=0.99) such that as GPS error increased so the accuracy obtained by correction decreased. For a signal with a mean uncorrected error of ∼30 m it was possible to reduce the error to ∼12 m. The results are discussed within the design requirements of a precision peat production system for peat energy. It is concluded that low-cost GPS could be used without differential correction as part of a precision peat production system because over 80% of the time positional error could be constrained to within 15 m. When compared with the perceived patterns of variability and the 30-m resolution of Landsat imagery which can be used for making application maps, this is acceptable.
This paper presents a novel image analysis scheme for accurate detection of fruit blemishes. The detection procedure consists of two steps: initial segmentation and refinement. In the first step, blemishes are coarsely segmented out with a flooding algorithm and in the second step an active contour model, i.e. a snake algorithm, is applied to refine the segmentation so that the localization and size accuracy of detected blemishes is improved. The concept and the formulation of the snake algorithm are briefly introduced and then the refinement procedure is described. The initial tests for sample apple images have shown very promising results.
Ruminant animals convert forage containing cellulose by bacterial fermentation into nutrients. The health of the bacterial culture in the rumen is essential for the health and productivity of the animal. Over a number of years fistulated animals have been used to study the rumen and its bacterial population. It has been shown that techniques to maintain the pH of the rumen between 7 and 5.5 pH are essential for the health of the dairy cow. The rumen pH has been recorded by using sensors suspended in the rumen at intervals or exceptionally with data recorders. However, fistulation of an animal requires surgery and is only suitable for a few research animals. This paper describes the development of a telemetric bolus that measured and recorded pH continuously. When interrogated by wireless the bolus transmitted the recorded data to an operator standing beside the cow with a receiving station. Boluses were placed in fistulated animals so that a comparison could be made with a laboratory instrument. Data are presented that show a close correlation between the calibrated laboratory instrument and the bolus at time intervals when the instrument was inserted. From this it can be assumed that the bolus accurately records the temporal variation in rumen pH. Data are presented to show the diurnal change in rumen pH over a 6-week period. Methods of increasing the lifetime and accuracy of the bolus are discussed.
A prototype of an acoustic on-line grain moisture meter was designed and developed. It consists of a funnel to produce a continuous grain stream with about 72 cm3 s−1 flow rate falling onto a 30° inclined sensor surface with 150 mm diameter. A microphone, which is placed underneath the surface, receives sound waves generated by kernel impact and converts them to an electronic signal. The sensor was insulated to eliminate the ambient noise effects. Through the experiments the sound pressure level (SPL) variations of the impact sound were measured as output voltage and their relationship with grain moisture content was determined. In the first experiment, effects of grain drop height and types of impact surfaces (glass, wood and metal) on SPL were evaluated. Results showed that a drop height of 10 cm with the glass sensor surface had the best calibration equation (R2 = 0.94) with the highest accuracy and sensitivity for wheat moisture measurement. In the second experiment, effects of wheat varieties (Mahooty, Karaj-3 and C-73-18) with the selected impact surface (glass) and grain drop height (10 cm) on the output voltage related to SPL, were studied. Relationships between the output voltage and kernel moisture content for three wheat varieties were developed as calibration equations. The maximum instrumental error in 8–20% moisture content range was 1.25%.
The red palm weevil (RPW) is a key pest of horticultural and ornamental palm species in Asia, the Middle East and the Mediterranean region, currently dispersing in Mediterranean European countries, endangering the landscape. The RPW larvae bore deep into palm crowns, trunks and offshoots, concealed from visual inspection until the palms are nearly dead. Traded palm trees are intensively transported between and within countries, spreading the pest worldwide. Consequently, an urgent need exists to identify and monitor concealed RPW larvae. Acoustic signals of boring RPW larvae can be recorded from the infested palms using off-the-shelf recording devices, but the resolution of the signals emitted by healthy palms is often difficult to discriminate. The purpose of this research was to develop a mathematical method to automatically detect acoustic activity of RPW in offshoots and implement it in a prototype setup. The methodology applied was similar to techniques used in the field of speech recognition, utilizing Vector quantization (VQ) or Gaussian mixture modeling (GMM). The algorithm successfully achieved detection ratios as high as 98.9%. The study shows that it is feasible to detect RPW sounds using the mathematical method of speech recognition and commercial recording devices, which could be utilized to monitor trade and transportation of offshoots.
In this work, the concept of data fusion is applied to nondestructive testing data for classification of fresh intact tomatoes based on their ripening stages. A Bayesian classifier considering a multivariate, three-class problem was incorporated for data fusion. Probability of error was estimated numerically for univariate and multivariate cases based on Bhattacharyya distance. Numerical results showed that multi-sensorial data fusion reduces the classification error considerably. The Bayesian classifier was tested on data of tomato fruits taken by the following nondestructive tests: colorimeter and acoustic impact. Results of Bayesian classifier agree with numerical estimations showing an 11% classification error in the multivariate (multi-sensor) case compared with a 48% obtained by the univariate case (single sensor).
A low-altitude platform utilising a 1.8-m diameter tethered helium balloon was used to position a multispectral sensor, consisting of two digital cameras, above a fertiliser trial plot where wheat (Triticum spp.) was being grown. Located in Cecil Plains, Queensland, Australia, the plot was a long-term fertiliser trial being conducted by a fertiliser company to monitor the response of crops to various levels of nutrition. The different levels of nutrition were achieved by varying nitrogen application rates between 0 and 120 units of N at 40 unit increments. Each plot had received the same application rate for 10 years. Colour and near-infrared images were acquired that captured the whole 2 ha plot. These images were examined and relationships sought between the captured digital information and the crop parameters imaged at anthesis and the at-harvest quality and quantity parameters. The statistical analysis techniques used were correlation analysis, discriminant analysis and partial least squares regression. A high correlation was found between the image and yield (R2 = 0.91) and a moderate correlation between the image and grain protein content (R2 = 0.66). The utility of the system could be extended by choosing a more mobile platform. This would increase the potential for the system to be used to diagnose the causes of the variability and allow remediation, and/or to segregate the crop at harvest to meet certain quality parameters.
The study analyses the possibility of improving the automated monitoring of dairy cows by combining the data given by various measurement systems already existing on farms. On a dairy farm where two groups of cows were monitored by different commercial systems, all the measured parameters were collected over 5 months: group A was milked in a traditional parlour equipped with instruments measuring milk production, flow and animal activity; group B was milked by an AMS (automatic milking system) measuring milk production and flow, milk electrical conductivity (per quarter), and animal activity. For each group all the monitoring systems were connected in a network and their data managed by means of a dedicated software. The acquired parameters were first treated to obtain alarms when their standard deviation exceeded a pre-determined threshold. All the animals giving such alarms were then inspected by the farm personnel and the respective normal or not normal (oestrus or pathology) conditions ascertained. Afterwards two models were developed aimed at detecting the animals’ abnormalities: one based on linear discriminant analysis, one based on fuzzy logic. The reliability of these models in detecting the relevant animal conditions was verified by comparing the alarms given by each method with the results of the farm observations. Both models were not very accurate in detecting specific abnormalities, but the model based on fuzzy logic was very effective in detecting general abnormal statuses and was also capable of producing warnings on so far undetected abnormalities in advance.
This paper presents a feasibility study on GSM–SMS technology application to field data acquisition. This feasibility study is based on a field data collection prototype system that is composed of field monitoring and host control platforms. The data transmission, communication, and control of these two platforms are accomplished using GSM–SMS technology. Based on the transmission characteristics and capacity of short message, this paper proposes a GSM–SMS based communication architecture and then develops a package format of short message that is suitable for monitoring the farming area and collecting field data, such as temperature, humidity, wind speed, and pest/insect captured numbers. After establishing the prototype system, the authentication and performance tests were conducted. The authentication test shows that the field data were transmitted correctly. Based on the performance testing results on over 915 data transmissions, the one-way SMS transmission time for a field monitoring platform to a host control platform is about 10–15 s, while the average transmission time of a field monitoring platform response to host control command is 30.5 s. Considering fluctuation of the environmental parameters, these delays are within tolerant boundaries. Also, the correctness of field data collected using GSM–SMS was 100% based on cross checking the sent and received data, and the integrity of transmission is guaranteed. The rate of data loss achieved can be lowered to 0.66%, which mainly depends on the service quality of the commercial telecommunication company. The proposed technique is well suited for implementation in field data monitoring and acquisition for precision agriculture.
Recent studies have shown that satellite data can be used in the detection and monitoring of potential outbreak areas of the Australian plague locust. However, the routine monitoring of such areas using Landsat MSS data is precluded by the high cost of the data, although it is still used selectively for forecasting.An alternative approach has been to make use of lower-cost meteorological satellite data with lower spatial resolution, for example that of the NOAA and GMS satellites, which provide an acceptable compromise between frequency of monitoring, relevant data and cost.This paper describes the acquisition of GMS LRFAX and NOAA APT data and other relevant meteorological data at the Australian Plague Locust Commission (APLC) Headquarters in Canberra and the use of the data in the APLC.
A portable data acquisition system, using a laptop computer, was developed to monitor performance of soil-engaging implements during field operation. The vertical and horizontal forces exerted by the implement. ground speed and other parameters were measured. A spreadsheet program was used to aid data analysis and storage.
To cope with future developments in glasshouse climate control, a distributed computer system is installed at the GCRS. The hard- and software of the system are described. The system has four types of computers networked together by Ethernet. It controls and collects data from eight glasshouse blocks with over 70 different compartments. The advantages of this system are: reliability, speed, flexibility, easy servicing, user-friendliness and easy expansibility.
A comprehensive computerized measurement and data acquisition system was developed to collect dynamic data relating to thermal environment profiles, energy use, operational characteristics, and animal performance of four field research broiler houses (12 × 121 m each). Although the basic components of the system consisted of available industrial electronic and mechanical products, certain design and application features involved were noteworthy and could be of reference value to those who wish to conduct similar field measurements.
In this study an online information and documentation system for the performance data of a forage harvester was developed and tested. A data acquisition system with positioning sensing and a communication module were integrated into the harvester. The data were transferred from the mobile equipment to the co-operative's control centre in two ways: short message service (SMS) and manually. The following online information was recorded: performance data (operation speed, location, harvested yield, …), machine settings (knife drum speed, …) and machine warnings (oil levels, oil pressure, oil temperature, …). Harvester position on the maps was displayed on a monitor installed in the cab. Harvested area was calculated from the field patterns registered by global positioning system (GPS). It was necessary to adapt the existing cartography to the reality of the co-operative's land. In the first design of the mounted prototype the operator's ease of use and the reliability of the system were analyzed. At this stage operation and ergonomic improvements were made. An evaluation was done by comparing the costs of processing the current information with the costs following the implementation of the new system. In a second investigation a first analysis was done of the recorded time to harvest each field and then regression lines were plotted to compare the field capacity value collected by the system with the field size and the crop yield. Correlations between the field capacity of the forage harvester, the area of the plot and the crop yield were found in these first tests.
A microprocessor-based system was constructed to control experimental conditions in the study of the moisture content of agricultural products. The equipment also recorded the condition of the air and the sample weights during the thin-layer drying tests for these products. This paper describes the hardware and software which were developed for use in the study of the properties of parboiled rice. Two samples were studied simultaneously at 13 combinations of relative humidity and temperature. The drying conditions were held constant enough that the variation in conditions in the drying chamber was shown to have no measurable effect on the thin-layer drying data, producing data repeatable to 0.001 moisture content dry basis.
This study illustrates the use of spatial information with high spatial resolution digital images to monitor the growth and development of crops in research plots and under commercial management. A robust and cost-effective technology to generate information endogenously from low altitude aerial imagery is described, and examples of applications in common beans and bananas are presented. Images were acquired using a standard commercial digital or SLR camera lifted by a kite or balloon. The optimum camera settings varied with conditions. The feasibility of correcting for distortions in the images and calibration of the sensor was explored, but it was not possible to automatically correct images and standardize the radiometric parameters over a range of conditions. Nevertheless, a combination of automated and visual analysis of the images obtained provided a means of classifying and quantifying various aspects of crop growth and development. This information was shown to be related to various bean and banana growth characteristics and can be used to predict bean yields and to estimate plant growth related parameters in banana plantations. The information is readily understood and, due to the robustness of the technology at a budget price, the methodology is a potentially powerful way of generating and providing information to local managers of genetic and natural resources as well as to owners of commercial plantations.
This paper presents methodology developed for knowledge acquisition for crop management expert systems. The proposed methodology is described through an extended waterfall model for knowledge acquisition. The way in which the methodology was implemented is presented, and the experience gained is discussed. Although the methodology has evolved through the development of an expert system for cucumber seedling production, it can be used for other crops. A field prototype of this expert system was implemented and is currently being tested in a real environment.
A data acquisition system fast enough to facilitate the recording of the primary phases of chlorophyll-α fluorescence transients has been developed. Based on a microcomputer equipped with an analog to numeric converter, this system permits the acquisition of a data point every 43 ms or 0.13 ms, depending on the program used. In this last case, obtained resolution permits to locate phenomena which occur during the first 400 ms of the measurement on the kinetic curve. An example of usage and details about programs are given.
Decision support systems (DSSs) can play a powerful role in natural resource management (NRM), by allowing more effective and collective use of information in addressing complex and often poorly structured questions. CSIRO Tropical Agriculture, in Townsville, Australia, is developing a DSS generator that provides a flexible environment for the construction of decision support tools that assist in assessing the implications of proposed policies or management actions. In this context, the POSEIDON system was designed to help resource managers with the problem formulation phase of building a DSS, i.e. identifying specific issues that need to be analysed to provide an answer to a broad NRM question. This is achieved by a form of knowledge acquisition from free text, which performs intelligent analysis of NRM documents. This article describes the design of POSEIDON and its application in NRM problem formulation.
This paper gives a general discussion of knowledge acquisition and formalization using structured induction, and illustrates the application of induction with an example. The example is taken from actual work on a knowledge based system for malting barley crop management, where the ID3 algorithm was used to derive rules and generate a decision tree to make the irrigation decision. The objective of the paper is to give the reader a working understanding of the principles of induction and the mechanics of the ID3 algorithm.
An infrared telemetry and telecontrol system was employed in a project of automatic irrigation scheduling based on soil moisture sensing. It was used to transmit continuous soil moisture potential from a remote field planted with wheat to a base station for analysis. In the base station, where a microcomputer was housed, the field information was processed through water management software developed for this purpose to decide when to start or stop irrigation based on a predetermined threshold for soil water potential. When start or stop irrigation was decided by the computer, a telecontrol signal was sent through the infrared system to the pumping station to deliver water or to stop it.
This article describes the interfacing of National Semiconductor's MM58167 real-time clock/calendar to a 6502 microprocessor-based SYM-1 microcomputer. The functions of the clock, details for interfacing to a microcomputer, programming required, and use of its special features are described.A real-time clock/calendar is an essential part of many microcomputer data acquisition and control systems. It provides a 24-hour clock and an accurate interval timer for data acquisition and control functions. The main advantage of a real-time clock is that it keeps track of true time independent of software execution speeds.The MM58167 clock is a CMOS integrated circuit in a 24-pin, dual-in-line package. It is designed for direct connection to the address and data buses of most common microcomputers. This application involved interfacing the clock with a SYM-1 6502-based microcomputer. The clock has eight counters and corresponding latches that contain months through thousands of seconds. The latches can be used for alarm-type functions. Low power battery backup is available through a special ‘power down’ mode. The clock has two interrupt outputs that can be used for control functions.
Apparent electrical conductivity (ECa) of the soil profile can be used as an indirect indicator of a number of soil physical and chemical properties. Commercially available ECa sensors can efficiently and inexpensively develop the spatially dense datasets desirable for describing within-field spatial soil variability in precision agriculture. The objective of this research was to relate ECa data to measured soil properties across a wide range of soil types, management practices, and climatic conditions. Data were collected with a non-contact, electromagnetic induction-based ECa sensor (Geonics EM38) and a coulter-based sensor (Veris 3100) on 12 fields in 6 states of the north-central United States. At 12–20 sampling sites in each field, 120-cm deep soil cores were obtained and used for soil property determination. Within individual fields, EM38 data collected in the vertical dipole orientation (0–150 cm depth) and Veris 3100 deep (0–100 cm depth) data were most highly correlated. Differences between ECa sensors were more pronounced on more layered soils, such as the claypan soils of the Missouri fields, due to differences in depth-weighted sensor response curves. Correlations of ECa with clay content and cation exchange capacity (CEC) were generally highest and most persistent across all fields and ECa data types. Other soil properties (soil moisture, silt, sand, organic C, and paste EC) were strongly related to ECa in some study fields but not in others. Regressions estimating clay and CEC as a function of ECa across all study fields were reasonably accurate (r2 ≥ 0.55). Thus, it may be feasible to develop relationships between ECa and clay and CEC that are applicable across a wide range of soil and climatic conditions.
Forest management planning usually takes place at the stand, enterprise or regional level. To maintain and conserve forest ecological processes, management operations should be transformed to an individual tree scale. This paper describes a method for supporting decision making at the level of individual trees following an approach based on close-to-nature forestry. An iterated conditional mode algorithm, as a relaxation of simulated annealing optimisation, was used to find an optimal solution. Using the principles of close-to-nature silviculture, a value function assigns management actions at the tree level. The reference conditions for optimisation are the decision-maker's preferences. The method was applied to an uneven-aged stand of Pinus sylvestris in the Guadarrama Mountains of Madrid (Spain) to find an optimal combination of individual trees to be harvested. An energy function is used to find an economic maximum for the remaining trees in the stand, in terms of the amount of low and high-quality timber to be harvested using the constraints: forest cover, biodiversity and regeneration. The optimal solution achieved an increased value for the stand of €321.32/ha (€96,396 for the 300 ha forest), while the residual diameter distribution favoured ecosystem services and regeneration. Even though this type of model is adaptable to a variety of decision-maker preferences and optimisation constraints, its data requirements limit application to small, intensively managed properties.