Crop sensing and management - CiteSeerX

Crop sensing and management P C H MILLER Silsoe Research Institute,Wrest Park, Silsoe, Bedfordshire, MK45 4HS Summary New technologies, particularly GPS, computers and microprocessor-controlled equipment, are making it possible to vary various management inputs according to the needs of the crop. An important first stage is the accurate sensing, whether of the crop, of weeds or of the soil. The paper reviews progress so far and identifies future research needs. Introduction Developing technologies are starting to provide the farmer with tools that will significantly change the ways in which inputs to arable crops are managed. Particular drivers that relate to the development of these technologies include: • • • •

the availability of rugged, robust, reliable and relatively low cost computer-based methods for logging, manipulating and transferring data from field equipment to the farm office; methods for determining in-field location using a range of possible systems and of which the satellite-based Global Positioning System (GPS) is now becoming commercially dominant; the potential development of relatively cheap sensing systems particularly where these use technologies that come from non-agricultural applications; development of computer-based decision support systems.

Many of the recent technological advances have been used in establishing the concept of precision farming that recognises that fields are not uniform and that substantial improvements in the ways that crops are managed can be achieved by accounting for this variability (Dawson, 1997). Legg and Stafford (1998) recognised that although the terminology relating to precision agriculture has been used mainly in relation to within-field spatial variability, a full definition would include a timeliness component such that the approach would involve “applying the right treatment in the right place and at the right time”. These authors also identified decision making as an important component of the precision farming approach and stated that much of the decision making process needed to be automated if it was to be acceptable to the majority of farmers. This implies a need for both sensors and access to databases to provide the information needed to make such decisions. The use of crop sensors and management systems need not relate directly to methods for accounting for spatial variability. There is a need, for example, to relate factors such as the development of the crop canopy to the inputs of fertiliser and crop protection chemicals (Dampney et al., 1998). If this is to be done in a rational manner, then methods for sensing crop condition and interpreting the output from the _____________________________________________________________________ HGCA conference: Crop management into the Millennium

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appropriate sensing systems may be equally relevant to a whole field treatment as to a spatially variable approach. The research, development and application of many crop sensing approaches has tended to be based on existing and available technologies that have been applied to agricultural applications. There is a need therefore for continuing research to identify sensor needs for improved crop production and then to assess the extent to which these can be met by existing and potentially new sensor concepts. This paper briefly reviews some of the approaches that have been taken and aims to identify future directions relating to crop sensing and management that may be appropriate for both research and development and commercial exploitation. It is important that the future development of sensing approaches is not constrained to be consistent with current field operations such as the application of fertilisers or crop protection chemical sprays. Developments of autonomous scouting vehicles may remove some of the constraints relating, for example, to the speed of travel and may enable field data to be collected with a minimum of manual labour. It is likely that timeliness of data collection will impose some constraints on autonomous data collecting methods but the longer term future is likely to increasingly involve automated data collection systems. The use of remote sensing from satellite or aerial platforms also provides a route towards automated data collection but even with the latest generation of systems, adequate resolution and availability are likely to remain as limitations to this approach. The need for crop sensors While many initial sensing approaches identified an available sensing system and then sought to identify possible useful methods of deploying this in an agricultural context, more recent studies have aimed at reviewing crop characteristics that are important to management decisions and the way in which sensor outputs can contribute to this decision making process. The results of such an analysis reported by Dampney et al. (1998) are summarised in Table 1. Decisions have been classified as relating to either productivity/profitability or environmental factors and have been allocated an arbitrary score to reflect priorities and importance. The analysis indicates a relatively high correlation between the priorities for these two factors and suggests no conflict between decision making for improved production and environmental performance. The information in Table 1 allocates high priorities to the use of sensors for determining characteristics of the growing crop canopy and the potential competition from weeds, pests and diseases. Dampney et al. (1998) cite results from studies that show a close relationship between crop canopy structure as defined by leaf area index and the uptake of nitrogen by a winter wheat crop. They indicate that mean potential financial benefits when using a nitrogen application strategy based on a canopy management approach could be in the order of £9.50/ha when compared with conventional systems. Work reported by Lutman et al. (1998) and Stafford and Miller (1993) relating to the spatially variable application of herbicides to control grass weeds in cereal crops indicated that savings in herbicide use in the region of 50% of herbicide could be made in many of the fields examined. If, as a realistic estimate, _____________________________________________________________________ HGCA conference: Crop management into the Millennium

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half the grass weed occurrences in cereal crops are found to be patchy, then this could lead to a mean input cost saving in the order of £7.50/ha. Both the above examples rely on being able to obtain input information on which decisions can be made at a relatively low cost in order to achieve the financial benefits indicated. This in turn provides a strong background for the development of appropriate sensing approaches that can be delivered to operate in an on-farm environment reliably, at low cost and with the minimum maintenance. It is possible that the output from a single sensor will be able to give information relating to more than one crop parameter, particularly as more sophisticated sensors and sensor combinations are developed – see Section 5 of this paper. For example, reflectance type sensors may, in the future, be able to give information relating to pest and disease occurrence, photosynthetic status of weeds and crop nitrogen content as well as canopy size/vegetative index. Table 1. Important crop parameters for decision making for combinable crops (in order of priority 1 = High, 2 = Medium, 3 = Low) (from Dampney et al., 1998) Crop parameter

Canopy size/Green Area Index (GAI) Crop moisture stress Crop N concentration Weed patch location Mycotoxin producing fungi in grain Canopy shoot number (cereals) Grain quality (nitrogen)

Weed species identification Crop biomass (fresh or dry) Crop moisture content Pest and disease

Decision influenced

Nitrogen timing and amount Growth regulator use Herbicide and Fungicide timing and dose Irrigation timing/scheduling Herbicide and insecticide use N fertiliser use/nutrient offtake Fungicide dose and timing Herbicide use Storage/marketing/food safety Nitrogen timing Growth regulator use Storage and market planning (malting barley and breadmaking wheat) Use of late N N offtake and post-harvest N balance Herbicide choice and dose Growth model prediction Nutrient offtake Dry biomass calculation Fungicide/insecticide selection

Priority for Farm economics Environment 1

1

1

1

1

1

1

1

1

1

1

2

1

2

1

2

1

2

1 1

3 3

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identification Yield at harvest Detection of previous spray location Water soluble stem carbohydrate (cereals) % Ground cover Pest and disease occurrence Photosynthetic status of weeds Crop height Grain quality (nonnitrogen) Crop nutrient concentration (other major and minor nutrients) Crop emergence Wax cover to leaf

Disease management Crop inventory Strategic planning Problem diagnosis Spray application accuracy

1

3

2

2

2

3

2

2

2

2

2

2

Growth regulator use 3 Moisture content Specific weight 3 Hagberg Falling Number Fertiliser and trace element use

3 3

3

2

3

3

3

3

Variety selection Late season fungicide use Yield prediction Disease risk (potatoes) Herbicide use Fungicide/insecticide dose and timing Herbicide use

Nutrient offtake and postharvest balance Growth predictions N Fertiliser use Disease risk Herbicide use

Available sensing technologies 1. Relating directly to the crop One of the parameters of arable crop production that has been amenable to sensing is crop yield at harvest. By adding a flow rate sensor in the main elevator delivering grain to the tank and linking the output of this sensor to measurements of forward speed and width of cut (or assuming a constant value for cut width) an assessment of crop yield can be readily made. Linked to a system giving in-field location, then this system provides the basis for generating a yield map. Any yield map information is historical and therefore what is needed in terms of crop management, is an approach to the analysis of this historical information that will help provide information relevant to the management existing and future crops. It is unlikely that the simple averaging of yield maps over a number of seasons will give adequate resolution with respect to crop inputs particularly given the complex interactions between crop yield and soil parameters, location, the weather, weed, pest and disease pressures and other historical factors. A number of approaches to the interpretation of yield maps obtained over a number of years have therefore been developed (Lark et al., 1997; Larscheid et al., 1997). Lark et al. (1997) developed an analytical method which identifies distinct clusters corresponding to different patterns of between season variation in yield from a sequence of yield maps using pattern recognition techniques. _____________________________________________________________________ HGCA conference: Crop management into the Millennium

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Where additional data was available, it was shown that spatial variability of some soil properties were significantly associated with the distribution of the clusters. This therefore provides a method for determining sampling strategies for economically assessing variations in soil properties across a field and for managing field areas in a spatially variable manner. The development of sensors to assess characteristics of the growing crop canopy based on spectral reflectance characteristics has been the subject of a number of research studies (e.g. Stafford and Bolam, 1998) that have also been taken through to the production of commercial sensors (Wollring et al., 1998). In the visible region of the energy spectrum, vegetation absorbs radiation and relatively little is reflected. The reflected energy from a vegetative surface has a minimum value at a wavelength in the region of 680 nm due to the absorption by chlorophyll in the leaf whereas, in contrast, the reflectance from a soil surface increases steadily in the range 400 to 1300 nm. Measurements of the reflectance from crop/soil surfaces at two wavelengths either side of the absorbance peak for chlorophyll (e.g. 660 and 730 nm used by Stafford and Bolam (1998)) therefore gives a basis for estimating characteristics of a crop canopy such as total cover or leaf area index. Reflectance measurements can be made using sensors mounted on a boom supported from ground-based vehicles, from aerial platforms or from satellites. It is recognised that the approach has important limitations because it cannot distinguish between factors that influence the spectral signature of a crop canopy. For example, effects due to canopy size cannot be distinguished from those due to chlorosis. However, it has been found that for a “well managed, healthy (cereal) crop”, there is a good correlation between the output from this type of sensor and the optimum nitrogen input (Wollring et al., 1998), and this has been the basis for the development of a commercial sensor system. A number of methods for determining the optimum rate of nitrogen based on assessments of plant tissue have been developed and reviewed by Wollring et al. (1998). A hand-held sensor that estimates chlorophyll concentration in individual leaf samples presented to the unit based on absorbance spectra has been shown to give good results for a range of crop types. The principle has been developed commercially with direct recommendations for fertiliser use linked to the readings obtained with the meter. The use of sensors with an ability to monitor spectral reflectance characteristics at a much larger number of wavelengths than the two or three that have been mainly used in initial work may provide a means of obtaining more information relating to factors influencing the crop canopy. This is the subject of continuing research some of which in the UK is funded by the Home-Grown Cereals Authority. Sensors based on the use of laser light to determine structural properties of a crop canopy have been used particularly in bush and tree crops and with some evidence that the same approaches may be useful in arable crop canopies. Walklate et al. (2000) report the use of a LIDAR (Light Detection And Ranging) instrument based on an infra-red laser and a rotating mirror to monitor the ‘time of flight’ to the nearest point of interception by the crop canopy. The LIDAR was used at a fixed height of _____________________________________________________________________ HGCA conference: Crop management into the Millennium

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1.0 m and traversed along a tree crop row at a speed of 0.2 m s-1 to accumulate approximately 0.5 million range samples to represent the target crop structure for spray application work. These data were then analysed to give parameters such as tree row volume, crop area index and tree area density that could be used to determine a basis for spray application to such a crop. Collaborative experiments involving Silsoe Research Institute and Long Ashton Research Station, funded by the Ministry of Agriculture, Fisheries and Food, have used the same approaches to characterise a wheat crop canopy and to relate the deposition of sprays directed into the canopy to these structural measurements. Results from initial measurements with the same instrument as used in orchard crops suggested that the range resolution of 0.05 m was too large for such an application. A further set of experiments using a similar instrument but with a finer resolution (to 0.01 m) showed that the method could be used to characterise cereal crop canopies in a way that correlated well with spray deposition patterns. LIDAR instruments have been developed commercially capable of reliable operation in agricultural environments but to date the numbers produced and the number of applications have been small such that these systems are still at the expensive, early development stage. Dampney et al. (1998) included the potential for using radar as a crop sensor in their review study and suggest that, although there are no ground-based systems used for agricultural applications, airborne and spaceborne systems do exist. They concluded that radar systems may have some potential as a crop canopy sensor but that substantial research effort is needed if such systems are to be developed to a stage such that the agricultural industry will have the confidence and funding to invest in such systems. Work at Silsoe Research Institute has used image analysis as a basis for the guidance of an autonomous vehicle operating in widely spaced row crops (Tillett et al., 1998). A charged couple device (CCD) was located centrally at the front of an experimental vehicle looking forward and down in order to provide images of crop rows. The images were analysed using a Hough transform algorithm to provide guidance information based on identifying row directions from a small number of plant images in the field of view at any one time. Signals from vehicle motion sensors (odometry and accelerometers) and a compass were combined with the image analysis data in a filter based algorithm to provide vehicle control information that was robust to natural variability. Additionally, the CCD images were further analysed to give size and geometric information to pick out crop plants requiring treatment. This approach has also been used to guide steerage hoes down sugar beet and widely spaced cereal crop rows. Images of the crop row have been analysed to give row directions and to account for effects such as the presence of shadows or weeds between the rows. Results from initial field trials with this system indicate that steerage information having a standard deviation of 15 mm about the mean can be obtained in practical operating conditions. This research work shows: (a)

that image analysis may have an important role particularly in weed detection in widely spaced crops; and

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(b)

that the control system developed could be used on an autonomous scouting vehicle carrying other sensor systems.

2. For automatic weed/weed patch detection Many of the principles that have been developed for sensing the characteristics of crop canopies have also been used as the basis for weed/weed patch detection. Ground-based reflectance sensors using the red/near infra-red reflectance ratio as a measure of chlorophyll have been studied for more than twenty years and used commercially to detect the presence of weeds in fallow (bare ground – (Felton, 1995), on roadways and pavements and to detect green weeds in senescing cereal crops (Haggar et al., 1983). Many studies have found however that, although there are differences in the spectral characteristics of weed and crop plants, the differences are not sufficient to enable reliable pre-programmed discrimination because of the variation in characteristics for both crop and weed with growing conditions, soil type and crop variety. Approaches to overcome this limitation have involved: (i) (ii)

calibration of the reflectance sensor in a weed free part of the field- the success of this method has been very limited because of the variation in crop canopy across a field; the use of a multi-spectral approach and using both weed and crop plants to calibrate the system for given field conditions (Feyaerts et al., 1999) – this approach has been shown to give an accuracy of approximately 90% in terms of weed detection accuracy when working in a sugar beet crop.

Lutman and Perry (1999) indicate that multi-spectral reflectance techniques have been found to be successful for mapping weed patches at a resolution of 1.0 m x 1.0 m when the weed density was relatively high (e.g. > 20 plants/m2 for wild oats in a cereal crop in Australian conditions) but was not reliable at lower weed plant populations. Research is continuing to establish how such approaches might be further developed and so establish a basis for commercial sensor development. The detection of weeds or weed patches in arable crops by imaging systems alone is likely to be very limited because of the over-lapping of leaves at all but the very early stages of growth. Some experimental systems have used a combination of colour (spectral characteristics) and shape to analyse for weeds in images collected with a digital camera. Good field performance is claimed for this system in terms of weed detection but it is likely to be slow and very expensive of computing resources and this may make it difficult to implement in a practical system. It is concluded that there are currently no commercially available sensors that will automatically aid the detection of weeds or weed patches in arable crops. There is research that should lead to practical developments but the current evidence is that these are some distance from the commercial market. 3. For soil properties The analysis in Table 1 suggests that one of the most important parameters influencing the management of combinable crops is the moisture status of the crop. _____________________________________________________________________ HGCA conference: Crop management into the Millennium

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While there are currently no commercial sensors that will determine crop moisture status directly, it may be possible to obtain valuable management information from sensed parameters of the soil that relate to moisture holding capacity. Non-intrusive sensors are now available that operate at the soil surface using two different principles, namely electromagnetic induction (EMI) and ground penetrating radar (GPR). The electromagnetic induction sensor provides a measure of soil electrical conductivity between the soil surface and a pre-set depth up to 1.0 m. Soil conductivity is mainly a function of soil texture and mixture content and therefore this type of sensor can be used to identify areas of a field with apparently similar physical characteristics that can then be samples in a more conventional manner. This type of sensor is now being used commercially to map soil variation as part of spatially variable management approaches. Ground penetrating radar measures the backscatter of energy waves which is again influenced by soil physical properties including moisture content and soil interfaces (Dampney et al., 1998). The characteristics of such sensors depends upon the energy wavelengths and power levels used and this also influences the sampling depth. Both these sensor systems are at early stages of development and work is in progress to relate outputs to the main soil properties of interest. Prototype and experimental sensors have also been developed for measuring soil surface properties and tilth (e.g. Scarlett et al., 1997). Both ultrasonic and optical sensors have been used to obtain measures of mean aggregate size at the surface, and this information is used as a basis for the control of cultivation machinery. Management of sensed information There is a need for sensors and the systems for handling sensed information to be well integrated. Legg and Stafford propose a system in which data relating to sensed field parameters would be received into a central database and that the output would be treatment maps that would directly control the actions of field equipment. A number of the components for this system already exist but it has yet to be joined together to give a complete system. A possible scheme for such a system shown in Figure 1 has important components relating to: (i) (ii) (iii) (iv)

the main crop response model component being common with decision support systems such as described by Brooks (1998); the existence of the user interface that enables a manager to monitor the overall process and make adjustments when necessary; the need for the decision making algorithms to take account of sensor characteristics; the basis and platforms on which decisions are made particularly with respect to the collection and collation of input data into the office computer and output to the treatment vehicle as a treatment map or the use of on-line decision making in the treatment vehicle in the field.

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Data input from sensors

S1

Out put product prices/supply

S2

S3

Input database

Crop response model

S4

Input product characteristics

Weather forcast

Recommended treatment map

User interface

Calibration of models

Agreed treatment map

Control of field machinery

Record for traceability

Field records (including yield)

Figure 1. An integrated decision and control system for agricultural production. (after Legg and Stafford, 1998) _____________________________________________________________________ HGCA conference: Crop management into the Millennium

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Manager

An example of the need to match the performance of the sensor system to the decision making algorithm relates to the case of the spatially variable application of herbicides. Christensen et al. (1999) report the use of a simulation model to study the behaviour of black-grass patches subjected to three different treatment strategies over a period of eight years and the way in which such a model can be used to transform weed map data into a treatment map. The model, and hence the weed to treatment map transform takes account of factors such as weed seed movement during cultivation and harvesting, the dose response characteristics of the herbicide and parameters influencing weed seed viability. Once the characteristics of the weed patch detection system are known in terms of the probability of a weed patch being detected which in turn is likely to be a function of weed patch size and weed density within the patch, then these parameters can be built into the model and the optimum strategy for treatment identified and outputs to a treatment map. The structure of Figure 1 suggests that the decision making process would be made in the farm office with input data from sensors and databases being presented on-line or via data transfer systems. Developments in data communication may mean that in the future more information is transmitted from field equipment to the farm office by radio and telephone links operated automatically (Miller, 1999). Such an approach has important implications for traceability in terms of the security and reliability of data transfer but also for the way in which sensor data is used and analysed. Some current crop canopy sensor systems are being used to provide data for on-line analysis and the variable application of nitrogen (Wollring et al., 1998) and in this case the treatment map transformation is being made on the application. In future developments it is likely that this approach will need to include historical data as part of the analysis and this could be carried in an on-board computer or automatically called up from an office computer system. An important component of sensor development and the use of sensors to provide inputs to the decision making process relates to sampling strategies and methods of data analysis. A detailed discussion of possible approaches is beyond the scope of this paper but recent advances such as those reported by Lark and Webster (1999) will have major implications for extracting useful information out of noisy datasets and therefore the way in which sensor systems are deployed, interrogated and the output information used. Conclusions and future directions Although the subject of considerable research effort, there are relatively few sensor systems that have been commercially developed for monitoring arable crop production and for providing inputs to decision making processes. There is now strong evidence that this is changing and that the development and adoption of computing systems in the farm office and on farm vehicles will be a major driver.

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Future directions are likely to include: • • •

• •

the use of multiple sensors linked together with computer based area network systems that can more closely simulate the inputs to conventional agronomic decision making processes; an increased use of data analysis and transformation to extract useful information from noisy and difficult sensor environments; the development of some new sensing approaches particularly as spin-offs from other technologies – for example, the early development of spray book height control systems used ultra-sonic sensors that were originally developed for autofocusing cameras and that gave an output that was dependant upon crop canopy characteristics – for this application crop effects were damped out but this could potentially be a useful way of monitoring crop canopy characteristics; increasing pressure to monitor in-field activities as part of traceability approaches (Miller, 1999). The development of low cost, reliable sensors for determining crop nutrient and disease status is an important component of the future strategy of the HomeGrown Cereals Authority.

References Brooks D H (1998). Decision support system for arable crops (DESSAC): an integrated approach to decision support. Proceedings The Brighton Conference – Pests and Diseases, 239-246. Christensen S; Heisel T; Paice M E R (1999). Simulation of long term alopecurus myosaroides population using three patch spraying strategies. Proceedings 2nd European conference on Precision Agriculture, Precision Agriculture ’99, SCI, Sheffield Academic Press, 977-987. Dampney P M R; Bryson R; Clark W; Strang M; Smith A (1998). The use of sensor technologies in agricultural cropping systems. A scientific review and recommendations for cost-effective developments. A review report to the Ministry of Agriculture, Fisheries and Food. Dawson C J (1997). Management for spatial variability. Proceedings 1st European conference on Precision Agriculture, Precision Agriculture ’97, BIOS Scientific Publishers Ltd., 45-58. Felton W L (1995). Commercial progress is spot spraying weeds. Proceedings Brighton Crop Protection Conference – Weeds, 1087-1096. Feyaerts F; Pollet P; Van Gool L; Wambacqu P (1999). Vision system for weed detection using hyper-spectral imaging, structural field information and unsupervised training sample collection. Proceedings Brighton Crop Protection Conference – Weeds, 607-614. Haggar R J; Stent C J; Isaac S (1983). A prototype hand-held patch sprayer for killing weeds, activated by spectral differences in crop/weed canopies. Journal of Agricultural Engineering Research 28, 349-358. Lark R M; Stafford J V; Froment M A (1997). Exploratory analysis of yield maps of combinable crops. Proceedings, 1st European Conference on Precision Agriculture. Precision Agriculture ’97, BIOS Scientific Publishers Ltd., 887-894. _____________________________________________________________________ HGCA conference: Crop management into the Millennium

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Lark R M; Webster R (1999). Analysis and elucidation of soil variation using wavelets. European Journal of Soil Science, 50, 185-206. Larscheid G; Blackmore B S; Moore M (1997). Management decisions based on yield maps. Proceedings 1st European Conference on Precision Agriculture, Precision Agriculture ’97, BIOS Scientific Publishers Ltd., 895-903. Lutman P J W; Rew L J; Cussans G W; Miller P C H; Paice M E R; Stafford J V (1998). Development of a ‘Patch Spraying’ system to control weeds in winter wheat. Home Grown Cereals Authority Report No. 158. Legg B J; Stafford J V (1998). Precision Agriculture – new technologies. Proceedings, The Brighton Conference – Pests and Diseases, 1143-1150. Miller P C H (1999). Automatic recording by application machinery of rates and spatial distribution of field inputs. Proceedings No: 439, The International Fertiliser Society. Scarlett A J; Lowe J C; Semple D A (1997). Precision tillage; in-field, real-time control of seedbed quality. Proceedings 1st European conference on Precision Agriculture, Precision Agriculture ’97, BIOS Scientific Publishers Ltd. Stafford J V; Miller P C H (1993). Spatially selective application of herbicide. Computers and Electronics in Agriculture, 9, 217-229. Stafford J V; Bolam H C (1998). Near-ground and aerial radiometry imaging for assessing spatial variability in crop condition. Proceedings 4th International conference on Precision Agriculture, Minneapolis, USA, July 1998. Tillett N D; Hague T; Marchant J A (1998). A robotic system for plant-scale husbandry. Journal of Agricultural Engineering Research, 69, 169-178. Walklate P J; Richardson G M; Cross J V; Murray R A (2000). Relationship between orchard tree crop structure and the performance characteristics of an axial fan sprayer. Aspects of Applied Biology, 57. Pesticide Application. Wollring J; Reusch S; Karlsson C (1998). Variable nitrogen application based on crop sensing. Proceedings No. 423 International Fertiliser Society.

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