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Close-range air-assisted precision spot-spraying for robotic applications: Aerodynamics and spray coverage analysis
Abstract
Orchards and grapevines are currently sprayed overall. Most bush and tree crop sprayers use airflow assistance which generates movements in canopy exposing both sides of the leaves to the spray. Also, large coherent vortices are formed further contributing to improved spray coverage. A new close-range air-assisted spot-spraying method for the selective treatments of disease foci is evaluated here which is promising for reduction of pesticides. Targets structures are expected to have typical diameters around 150 mm and the size of the unit matches this. In contrast to conventional methods, this size of unit prevents the generation of large scale coherent turbulent structures in the airflow that could provide spray coverage of both sides of the target leaves. Therefore, to enhance the beneficial effects of local turbulence, and to induce leaf movement whilst retaining the small size of the spray unit, a rotating screen to generate airflow pulses with discrete peaks in velocity was added and tested. Experiments on the close-range spraying of young grapevine plants using the rotating airflow screen were performed. A high-speed camera, image analysis system and water sensitive papers were used for analysis of the spraying. Natural frequencies of individual leaves showed sharp fluctuations at discrete frequencies and single leaf fluctuations of root mean square velocity corresponded well to the pulsating airflow. Spraying was evaluated as percentage spray coverage and number of droplet impacts. Spray coverage of front side of leaves (facing the sprayer) was good, but coverage on the back of the leaves was limited.
... In Fig. 12, Fig. 14 and Fig. 16, the x-axis represents height of plant. However, in Fig. 12, the y-axis represents that size of probability that 80 measurement results are in the interval of (0-5), (5)(6)(7)(8)(9)(10). . . (295-300), in Fig. 14, the y-axis represents that size of probability that 80 measurement results are in the interval of (0-5), (5)(6)(7)(8)(9)(10). . . ...
... However, in Fig. 12, the y-axis represents that size of probability that 80 measurement results are in the interval of (0-5), (5)(6)(7)(8)(9)(10). . . (295-300), in Fig. 14, the y-axis represents that size of probability that 80 measurement results are in the interval of (0-5), (5)(6)(7)(8)(9)(10). . . (825-830), in Fig. 16, the y-axis represents that size of probability that 80 measurement results are in the interval of (0-5), (5)(6)(7)(8)(9)(10). . . ...
... (295-300), in Fig. 14, the y-axis represents that size of probability that 80 measurement results are in the interval of (0-5), (5)(6)(7)(8)(9)(10). . . (825-830), in Fig. 16, the y-axis represents that size of probability that 80 measurement results are in the interval of (0-5), (5)(6)(7)(8)(9)(10). . . (995-1000). ...
This paper investigate an adaptive pesticide spraying system according to the plants height. The system including a depth sensor and a spraying system with multiple nozzles at different height in vertical direction. The entire system is installed on an automatic guided vehicle (AGV). In order to implement precision spraying or automatic targeting, plant identification and plant height calculation are critical for the system with fixed height of nozzles. The designed system will open one or combination of the nozzles of the spraying system since the plant height is identified. To address the plant height-adaptive challenge, a pesticide spraying system with plant height-adaptive is proposed based on a camera sensor to dealt with the figure of the plant. Both of the depth and color data of the figure are obtained and analyzed, the open or close state of the spraying system is optimized for different plants with different height. Various experimental results have demonstrated that the proposed system is considered as well.
... The far end spraying unit consists of 3 parts as shown in Fig. 7, the far end controller, the spraying unit (gear pump, nozzle), and the wind controller (electronics speed controller, motor, propeller), so that the unit can simulate UAV aerial spraying movement, variable rate spraying, and wind field. It is open to fulfill other aerial spraying technology simulations, such as electrostatic spraying (Zhang et al., 2016), air assistant spraying (Malnersic et al., 2016), oil spraying (Malnersic et al., 2016), etc. This system can test different types of nozzles (Meyer et al., 2016), influence of wind field, spraying height, UAV speed on droplet distribution (de Araujo et al., 2016), residue analysis (Hanafi et al., 2016), etc. ...
... The far end spraying unit consists of 3 parts as shown in Fig. 7, the far end controller, the spraying unit (gear pump, nozzle), and the wind controller (electronics speed controller, motor, propeller), so that the unit can simulate UAV aerial spraying movement, variable rate spraying, and wind field. It is open to fulfill other aerial spraying technology simulations, such as electrostatic spraying (Zhang et al., 2016), air assistant spraying (Malnersic et al., 2016), oil spraying (Malnersic et al., 2016), etc. This system can test different types of nozzles (Meyer et al., 2016), influence of wind field, spraying height, UAV speed on droplet distribution (de Araujo et al., 2016), residue analysis (Hanafi et al., 2016), etc. ...
... An air-assisted precision spraying and effector was presented, and the percentage of the spray coverage and the number of droplet impacts were evaluated. The results showed that the leaves' front side's spraying was good, but on the backside, the spraying was limited [14]. ...
... Many researchers have emphasized that the quality and effectiveness of the spraying procedure depends on the uniformity of the spray liquid fall, the coverage of the sprayed objects [29,30], and the application of the spray liquid [31]. Both the degree of coverage and the application of utility liquid can be used to assess changes in spraying technique, verify selected parameters of the sprayer, and assess the work of nozzles, depending on technical and technological factors [32,33]. ...
The purpose of the research was to determine the influence of selected factors on the average degree of coverage and uniformity of liquid spray coverage using selected single and dual flat fan nozzles. The impact of nozzle type, spray pressure, driving speed, and spray angle on the average degree of coverage and coverage unevenness coefficient were studied. The research was conducted with special spray track machinery designed and constructed to control and change the boom height, spray angle, driving speed, and spray pressure. Based on the research results, it was found that the highest average coverage was obtained for single standard flat fan nozzles and dual anti-drift flat fan nozzles. At the same time, the highest values of unevenness were observed for these nozzles. Inverse relationships were obtained for air-induction nozzles. Maximization of coverage with simultaneous minimization of unevenness can be achieved by using a medium droplet size for single flat fan nozzles (volume median diameter (VMD) = 300 μm) and coarse droplet size for dual flat fan nozzles (VMD = 352 μm), with low driving speed (respectively 1.1 m∙s−1 and 1.6 m∙s−1) and angling of the nozzle by 20° in the opposite direction to the direction of travel.
... The robot successfully treated more than 99.5% of weeds identified, and roughly 0.5% of weeds could not be discovered, and poison consumption has economized with the same amount. Malnersic et al. (2016) used an accurate point spraying system using air in the robot applications. This spraying system showed that an airflow of 10 m/s is achieved at a distance of 0.3 m with a nozzle diameter of around 150 mm. ...
Spraying in farmlands and greenhouses is not only labor and time consuming, but also it has adverse effects on human health. The best solution is to use mechanized devices and robots to automate the spraying operation. In this study, a robot with four degrees of freedom performs spraying through calculating the plant bulk volume. The robot travels along the plant rows and stops when it detects plant stems and opens its manipulator step by step so that the Kinect v. 1 camera can capture deep RGB¹ colored images from the various sections. After calculating the bulk volume of the plant, it starts spraying. The results showed that the robot is capable of calculating the volume efficiently. The average operation time of the robot detecting plant, opening manipulator, imaging, estimating volume, and spraying liquid according to the estimated volume is 54 s for a plant of 1.7 m height. The detection error of the robot is estimated to be 19%, which is negligible. Assessment of liquid-sensitive papers located around the plants shows that the robot’s liquid-spraying quality is not reduced at high levels of the plant. The robot can detect the plant’s height and preserve its desired spraying quality in its total height.
... Various qualitative and quantitative methods are used in spray treatments to determine drop adsorption of the target surface, coverage ratios, drop density and spray penetration (Sayıncı and Bastaban, 2009). Among these methods, water sensitive papers (WSPs) are commonly used in assessment of different parameters (Foqué and Nuyttens, 2011;Malneršič et al., 2016;Guler et al., 2006;Salyani et al., 2013). In sampling practices, papers (WSPs) are placed parallel to ground plane or placed over the leaf surfaces of the plant canopy. ...
... To address these problems, scholars in China and abroad have conducted a great deal of studies, which mainly applied infrared detection technology [10,11] , ultrasonic ranging sensors [12,13] , machine vision and image processing technology for target recognition [14][15][16] , and integrated remote sensing technology, global positioning system and geographic information system in getting comprehensive information of the target, and applied the technologies in disaster prevention of fruit trees and crops to reduce pesticide abuse and insufficient drug use [17,18] , there are not many studies on inter-row walking-type plant protection machinery of maize and walking chassis structure. In this paper, a kind of self-propelled maize thermal fogger chassis was designed to realize inter-row pesticide spraying by holding remote control equipment. ...
... Malneršič et al. [12] evaluated a new technique for closerange air-assisted precision spot-spraying end effector. The spraying end effector was designed to enable connection to emerging agricultural robotic technology. ...
This article presents the most recent contributions to the field of agricultural robotics, with a focus on robot sprayers. The objective is to demonstrate the latest developments, specifically on robot sprayer systems for precision spraying (e.g. for weed control). The summary of the review shows that feasibility has been demonstrated in a number of projects with significant results especially in pesticide use reduction, with good spraying coverage for this complex agricultural task.
... They usually consist of a moving module which can take pictures from the plants and process them at the same time. The module is a tracking chassis which sometimes equipped with a navigation system, an endeffector, a camera and an image processing unit (Malneršič et al., 2016;Oberti et al., 2016;Bechar and Vigneault, 2017). Not only there is an increased attention to use online robotic systems for weed spraying (Montalvo et al., 2013;Peña et al., 2013;Gonzalez-de-Soto et al., 2016), yield estimation (Annamalai, 2004 Kondo, 2014), but these systems are becoming popular in unmanned plant health monitoring applications. ...
In this study, an autonomous machine vision-based system was developed for precise nitrogen fertilizing management to improve nitrogen use efficiency in greenhouse crops. Two scenarios were considered: in the first scenario, nitrogen fertilization was done based on available instructions and protocols provided by the seed producer. The second scenario was nitrogen fertilization using a machine vision-based robot which was moving between the crop rows to monitor plants’ needs and requirements. The scenarios were examined in a hydroponic greenhouse for four varieties of cucumber. The extracted plant image features, namely entropy, energy, and local homogeneity were the markers for precise timing of nitrogen fertilization in the cucumber crops. In scenario 2, nitrogen fertilizing was based on a signal transmitted from the robot to a wireless receiver when at least one of the mean values of normalized image textural features of cucumber crops had a difference more than a (a = 10, 15, and 20%) with the same feature in scenario 1. Experimental results showed that scenario 2 for a = 15% decreased the nitrogen fertilizer consumption about 18% without lowering the fruit yield or fruit quality parameters including firmness, total soluble solids, chlorophyll and ascorbic acid contents. This scenario can be considered as an efficient nitrogen fertilizing management method in commercial greenhouses using a low-cost machine vision-based robotic system.
Plant protection products play a strategic role in securing worldwide food production. Nevertheless, major societal concerns are raised about risks for the environment and humans, and are being addressed by policy actions for reducing the dependence of agriculture on pesticides. A primary contribution to this can come from precision crop protection approaches, with treatments tailored to the site and time-specific needs of protecting the crop from pest pressure and expected infestation spreading. Robotic systems can play a role in precision crop protection, both in accurate monitoring of plant conditions and in timely and selectively spraying the treatment targets. This chapter provides a comprehensive discussion of the state-of-art of robotic spraying, with a review of weed and disease sensing tasks, and of precision actuation of treatments. Despite the complexity of some of the problems ahead, the chapter shows how the building blocks of integrated robotic systems for precision crop protection are developing fast.
Agriculture is the backbone of society as it mainly functions to provide food, feed and fiber on which all human depends to live. Precision agriculture is implemented with a goal to apply sufficient treatments at the right place in the right time with the purpose to provide low-input, high efficiency and sustainable agricultural production. In precision agriculture, automation and robotics have become one of the main frameworks which focusing on minimizing environmental impact and simultaneously maximizing agricultural produce. The application of automation and robotics in precision agriculture is essentially implemented for precise farm management by using modern technologies. In the past decades, a significant amount of research has focused on the applications of mobile robot for agricultural operations such as planting, inspection, spraying and harvesting. This paper reviews the recent applications of automation and robotics in agriculture in the past five years. In this paper, the recent implementations are divided into four categories which indicates different operations executed for planting management starting from a seed until the product is ready to be harvested. Towards the end of this paper, several challenges and suggestions are described to indicate the opportunities and improvements that can be made in designing an efficient autonomous and robotics system for agricultural applications. Based on the conducted review, different operations have different challenges thus require diverse solutions to solve the specific operational problem. Therefore, the development process of an efficient autonomous agricultural robotic system must consider all possibilities and challenges in different types of agricultural operation to minimize system errors during future implementation. In addition, the development cost needs to be fully considered to ensure that the farmers will be able to invest their capital as a consumer. Therefore, it will become highly possible for the autonomous agricultural robotic system to be widely implemented throughout the world in the future.
Greenhouse crop production in China has in the recent years expanded widely. To minimize compromised pesticide use and adverse environmental impact in these structures, it is essential to optimize the working parameters and settings of sprayers. Experiments using an air assisted greenhouse sprayer and artificial plants with attached fruits were conducted to investigate the effects of volume rate, target location, and airflow rate on spray deposition and distribution uniformity on leaves, fruits and losses to the ground. Three spray volume rates (530, 368 and 220 l ha⁻¹) and three nozzle's outlet volumetric airflow rates (374.4, 289.4 and 234.7 m³ h⁻¹) were evaluated. Ground speeds were set at 0.36 and 0.31 m s⁻¹ for volume rate and airflow rate trials, respectively. Artificial targets (filter-paper collectors) were attached on either sides of leaves, fruits and the ground to collect deposits of a food colourant used as a tracer (Spray solution). Deposition was quantified through UV-VIS spectroscopy at a wavelength of 508 nm. Results indicated that volume rate significantly influenced deposition and distribution uniformity. Reducing the volume rate reduced deposition but improved distribution homogeneity. Quantified absolute deposits on upper leaf surfaces declined by 27.4% (MV) and 43.3% (LV). Normalized deposition displayed under-leaf to upper-leaf deposition ratios of between 16.1% (HV) to 20.0% (LV). Ground deposits were reduced by roughly 37.3% (MV) and 54.7% (LV). Similar to volume rate, airflow rate influenced spray deposition on leaves but followed an opposite trend. Reducing airflow rate improved deposition although the effect attained a threshold at medium airflow rate beyond which further reduction the deposition decreased though the reduction was not statistically significant (P = 0.069). Airflow rate did not significantly affect ground deposits (P = 0.315) it reduced the quantity of spray lost by 7% (MF) and 8.4% (HF).
Subject to narrow space and large planting density, many advanced crop protection machines are not suitable for Chinese heliogreenhouses. Under these conditions, a novel autonomous air-assisted sprayer is designed to realize automatic spraying and to improve droplet deposition uniformity. With this sprayer, this paper focuses on precise air-assisted spraying control, and a spraying optimization strategy is researched to obtain uniform deposition of droplets on crops. Firstly, the autonomous sprayer and its spraying sub-system are introduced, and the simplified airflow model is constructed and validated via spraying experiments. Furthermore, based on this model, the relationship between droplets deposition area and spraying mechanism posture is deduced, and an offline optimal spraying strategy based on Genetic Algorithm (GA) is proposed. The algorithm encodes 4 key parameters to control the spraying mechanism’s behavior, and the Coefficient of Variation (CV) of droplets deposition on various parts of crops is computed as the fitness value in algorithm iterations. Illustrative optimization processes are simulated, which show the characteristics of uniform spraying solution. With these optimized parameters pre-set in the autonomous sprayer, real spraying tests are carried out in a heliogreenhouse. The results show that the deposition volumes are all in the range of 1.2 ∼ 1.4 μL/cm². The final CV values of three sets of data are 9.9%, 10.4% and 11.2%, which are small enough for spraying operation. Simulation results and real tests data validated the effectiveness and convenience of the proposed optimal spraying strategy. Finally, some conclusions and future work are discussed. With this spraying strategy optimization, optimized spraying behavior can be planed for different crops with different planting patterns in Chinese heliogreenhouses.
Wild blueberry producers apply fungicide uniformly without considering significant bare spots in the field. The wrong or over use of fungicide in bare spots results in an increased cost of production and threatens the environment. An automated prototype variable rate (VR) sprayer was used for spot-application (SA) of chlorothalonil (Bravo�) fungicide in a wild blueberry field. Eighteen 6.1 m wide test tracks were selected in the
field and bare spots were mapped using a real-time kinematics-global positioning system (RTK-GPS). Six plots were selected randomly for three different application rates. Water sensitive papers (WSP) were placed in foliage and bare spots in SA and uniform-application (UA) tracks. The percent area coverage (PAC) of WSP with both SA and UA in foliage and bare spot areas were calculated. Plant growth parameters were measured from all 108 randomly selected plots in SA, UA and control (CN) tracks for comparison. Plant images were taken over six selected plots in each of the 18 tracks. Images were analyzed using custom developed software to calculate the percentage of green pixels (PGP) for determining the effect of Bravo� on plant health. Fruit yield parameters were also measured from selected plots for comparison. Non-significance of the t test for SA versus UA plant targets’ PAC indicated that there was no significant bias in the SA with saving (9.90–51.22 %) and SA was accurate. Bravo� did not show any significant difference on plant growth parameters among SA, UA and CN. However, PGP, floral bud and harvestable yield of SA and UA were significantly increased over CN. Therefore, a VR sprayer could be used for SA of fungicides in wild blueberry cropping system to reduce
chemical usage and maintain crop productivity.
Site-specific weed management requires sensing of the actual weed infestation levels in agricultural fields to adapt the management accordingly. However, sophisticated sensor systems are not yet in wider practical use, since they are not easily available for the farmers and their handling as well as the management practice requires additional efforts. A new sensor-based weed detection method is presented in this paper and its applicability to cereal crops is evaluated. An ultrasonic distance sensor for the determination of plant heights was used for weed detection. It was hypothesised that the weed infested zones have a higher amount of biomass than non-infested areas and that this can be determined by plant height measurements. Ultrasonic distance measurements were taken in a winter wheat field infested by grass weeds and broad-leaved weeds. A total of 80 and 40 circular-shaped samples of different weed densities and compositions were assessed at two different dates. The sensor was pointed directly to the ground for height determination. In the following, weeds were counted and then removed from the sample locations. Grass weeds and broad-leaved weeds were separately removed. Differences between weed infested and weed-free measurements were determined. Dry-matter of weeds and crop was assessed and evaluated together with the sensor measurements. RGB images were taken prior and after weed removal to determine the coverage percentages of weeds and crop per sampling point. Image processing steps included EGI (excess green index) computation and thresholding to separate plants and background. The relationship between ultrasonic readings and the corresponding coverage of the crop and weeds were assessed using multiple regression analysis. Results revealed a height difference between infested and non-infested sample locations. Density and biomass of weeds present in the sample influenced the ultrasonic readings. The possibilities of weed group discrimination were assessed by discriminant analysis. The ultrasonic readings permitted the separation between weed infested zones and non-infested areas with up to 92.8% of success. This system will potentially reduce the cost of weed detection and offers an opportunity to its use in non-selective methods for weed control.
Because of its complexity, orchard spray application is difficult to understand based solely on experimental work. For this reason an integrated research approach combining experiments and simulations with a continuous interaction between both was used for this study. Three commercially available, air-assisted orchard sprayers with different types of air discharge systems were characterised. Experiments were carried out to assess the spray nozzles, the spray liquid distribution and the produced air stream. Measurements were conducted under controlled laboratory conditions. Results indicated important differences in air flow patterns and liquid distribution between the different sprayers and demonstrated that the liquid distribution is directly linked to the generated air flow pattern. The architecture of the air discharge unit on the sprayer strongly influenced the air flow pattern. The results of these experiments were then compared to the results of a computational fluid dynamics (CFD) orchard spray model and the potential of using the CFD model for sprayer optimisation was evaluated.
There is increasing pressure to reduce the use of pesticides in modern crop production to decrease the environmental impact of current practice and to lower production costs. It is therefore imperative that sprays are only applied when and where needed. Since diseases in fields are frequently patchy, sprays may be applied unnecessarily to disease-free areas. Disease control could be more efficient if disease patches within fields could be identified and spray applied only to the infected areas. Recent developments in optical sensor technology have the potential to enable direct detection of foliar disease under field conditions. This review assesses recent developments in the use of optical methods for detecting foliar disease, evaluates the likely benefits of spatially selective disease control in field crops, and discusses practicalities and limitations of using optical disease detection systems for crop protection in precision pest management.
Powdery mildew is a major fungal disease for grapevine (Vitis vinifera L.) as well as for other important specialty crops, causing severe damage, including yield loss and depreciation of wine or produce quality. This disease is thoroughly controlled by uniform spraying of vineyards with agrochemicals according to a calendar, which can easily result in ten to fifteen fungicide applications in several grapevine-growing areas. Since primary infections are localized in discrete foci rather than being uniformly diffused, there are potential benefits linked to the development of systems able to detect initial infection foci and operate targeted treatments instead of the current homogenous and unselective sprayings.
When a pesticide is applied in a vineyard, the fraction can drift away from the target and affect the abundance of beneficial arthropods in adjacent crops or hedgerows. A field experiment was conducted in north-eastern Italy using an air-assisted sprayer in order to evaluate the spatial distribution of etofenprox drift in a vineyard-hedgerow system and its effect on the predatory mite Kampimodromus aberrans. Three scenarios of decreasing potential drift were compared, all in still wind conditions. In the worst case of free expansion of drift, 12% of the applied rate drifted for 6-7 m with minor effect on K. aberrans abundance, according to the dose-response assay results; the presence of a hedgerow reduced the drift by about 80%. The hedgerow was also effective when good agronomic practices were followed, and the effect of drift on K. aberrans was not significant, its abundance being mainly linked to the patchiness of the natural population. Because of lateral drift, i.e. not in the direction of air-flow from sprayer, spray was detected at very low concentration in the contiguous untreated vineyard. This had no effect on K. aberrans but had the capacity to contaminate organic crops and provide a risk to surface water and bystanders. This suggests that an environmental regulatory scheme taking hedgerows into account should be supported and implemented on a catchment or regional scale.
An axial-fan air-assisted sprayer, adapted with variable rate nozzles and adjustable air-assist flow control, for citrus tree-specific precision spraying, was tested. Static experiments were conducted to determine spray patterns at varied nozzle flow rates and air-assistance settings. Spray patterns confirmed that the sprayer delivered variable liquid flow rates and the air-diverting louvre controlled the air-assistance providing targeted spray delivery. Field experiments were conducted to determine suitable air-assistance and nozzle combinations needed in the future for sensor-controlled spraying in young (small-sized) citrus canopies. Experiments involved two nozzle treatments, a) nozzles 1-6, and b) nozzles 2-3 at 100% output rates and three (40%, 70%, and 100%) levels of air assistance selected based on spray patterns. The sprayer was operated at a travel speed of 4.5 km h(-1) and the water-soluble tracer deposition on biological and artificial targets, at 0.6 m and 1.4 m above ground and laterally at the canopy front, middle, and back, respectively were measured using fluorimetry. Results suggested that within nozzles 1-6 treatments with varied air-assist, 70% air-assistance was more effective for small sized canopies than 100% air-assistance; it appeared that the latter propelled the spray beyond the canopy and reduced the spray deposition efficiency. Also, for the citrus canopies studied, the use of two or three nozzles instead of the lower six nozzles (i.e. the control treatment) might be more suitable since they would result in 50% or less chemical usage while having comparable spray deposition to that of the control.
The aim of this study is to understand the turbulent flow structure within diverse canopy models dominated by progressive waves. A set of experimental conditions were considered in a laboratory flume: three vegetation models (submerged rigid, submerged flexible and emergent rigid), three canopy densities (128, 640 and 1280 stems/m2) and three wave frequencies (f=0.8, 1 and 1.4 Hz). The canopy morphology through both the plant flexibility and height and the canopy density were the characteristic parameters that exerted a control on wave induced turbulence within the canopy bed. In the flexible canopy model, sheltering at the bed was observed and was associated with the movement of the blades. In contrast, in the rigid canopy model larger TKE was found as compared with the case without canopy. The increase of TKE was associated with the production of the stem-wake turbulence. Sheltering in the submerged rigid canopy model was found at the lower layer for the largest canopy density and highest wave frequency because of a great loss of wave velocity, confined below the top of the canopy. Sweeps and ejections were the predominant events, enhancing the transfer of momentum at the top of the canopy. Therefore, below the top of the submerged rigid canopy was characterized by a vertical energy exchange zone. Unlike the submerged model, sheltering was always found for emergent rigid vegetation, and attributed to the inhibition of the wave energy at all depths.
Precision agriculture dates back to the middle of the 1980’s. Remote sensing applications in
precision agriculture began with sensors for soil organic matter, and have quickly diversified
to include satellite, aerial, and hand held or tractor mounted sensors. Wavelengths of
electromagnetic radiation initially focused on a few key visible or near infrared bands.
Today, electromagnetic wavelengths in use range from the ultraviolet to microwave
portions of the spectrum, enabling advanced applications such as light detection and
ranging (LiDAR), fluorescence spectroscopy, and thermal spectroscopy, along with more
traditional applications in the visible and near infrared portions of the spectrum. Spectral
bandwidth has decreased dramatically with the advent of hyperspectral remote sensing,
allowing improved analysis of specific compounds, molecular interactions, crop stress, and
crop biophysical or biochemical characteristics. A variety of spectral indices now exist for
various precision agriculture applications, rather than a focus on only normalised difference
vegetation indices. Spatial resolution of aerial and satellite remote sensing imagery
has improved from 100’s of m to sub-metre accuracy, allowing evaluation of soil and crop
properties at fine spatial resolution at the expense of increased data storage and processing
requirements. Temporal frequency of remote sensing imagery has also improved
dramatically. At present there is considerable interest in collecting remote sensing data at
multiple times in order to conduct near real time soil, crop and pest management.
The main way of controlling grapevine pests and diseases is by spraying them with crop protection products. The aim is to deposit as many droplets as possible on potential or already established infection/infestation points. A prototype sprayer was constructed with its nozzle holder booms forming a kind of tunnel that travels over the plants. Spraying is thus performed in an enclosed space, preventing the shower of droplets from being blown away by the wind. A low evaporation rate is also achieved. Further, since the droplets have to travel only a very short distance between the nozzles and the plant surface, the coverage achieved approaches the ideal for crop protection purposes. The efficiency of the applications was determined using water-sensitive paper attached to the leaves of the plants during spraying; after spraying, these were examined using an artificial vision system. The results show that, at the maximum pressure assayed (0.6 MPa), the mean coverage percentage exceeded 54% - sometimes reaching up to 79%. This indicates that ideal product application conditions were approached, the product forming a nearly continuous film on the plant surface. spraying / phytosanitary copper / deposit / water-sensitive paper / pixel
An automated prototype variable rate (VR) sprayer was developed for control of 8 individual nozzles on a 6.1 m sprayer boom for in-season, site-specific application of agrochemicals on weeds. The sprayer boom was divided into 8 sections and mounted behind an all-terrain vehicle (ATV) at 76.2 cm above the ground. The variable-rate control system consisted of 8 ultrasonic sensors (one per spray section) mounted on a separate boom in front of the ATV, DICKEY-john Land Manager II controller and flow valve, solenoid valves and an 8-channel variable rate controller interfaced to a Pocket PC (PPC) using wireless Bluetooth® radio with Windows Mobile® compatible software. This type of VR sprayer does not use prescription maps, but relies on sensors to provide real-time weed detection information which is used to dispense correct agrochemical rates for the weeds. The sprayer can be used for in-season, spot application (SA) of agrochemicals by activating specific boom sections where the weeds have been detected.
There have been no previous risk assessment studies on citrus pesticides in Spain. The aim of this work
was to estimate the risks caused by worst-case drift scenarios of the principal pesticides used on the
crop, assessing possible damage to the environment and human health. A field survey was carried out,
characterizing the specific conditions of plant protection product applications to citrus crops in Spain. Six
targets were identified as being the most affected by droplet spray drift in Spain, and more broadly in
most Mediterranean conditions: aquatic organisms, earthworms, bees, adult bystanders, child
bystanders and residents. Three drift estimation models were used to assess the amount of drift at
specific distances downwind of a field in order to calculate Risk Indicators. These showed safe conditions
for earthworms and residents, but also indicated that some pesticides may pose a risk to aquatic
organisms, even with a 20 m buffer zone, and also to bees, and adult and child bystanders. In general,
results generated similar consequences of hazard risk independent of the drift prediction model used,
indicating that toxicological data are more relevant for predicting risks.
The aim of this work was to evaluate spray deposition and losses to the soil in a greenhouse tomato crop during the use of a manually pulled trolley, equipped with two vertical spray booms, each with four nozzles spaced 0.50 m apart and oriented 15� upwards, in comparison with a reference treatment with a manual spray gun. The test conditions for the spray gun were established taking into account the routine practiced by farmers, consisting of treating high crops at spray volumes of nearly 1500 l ha�1 at pressures of about 2000 kPa. For the trolley, two types of nozzles were tested (standard flat fan nozzles and air-induction flat fan nozzles), at a pressure of 1200 kPa, distributing approximately the same volume as with the spray gun. Artificial targets (strips of filter-paper) fixed to upper and lower leaf surfaces in 12 zones of the tomato plants (3 heights and 4 depths) were used to collect deposits of the tracer Tartrazine.
The results indicated that the deposits with the spray trolley were significantly greater than those with the spray gun. The trolley resulted in an overall deposition 40.6% and 33.9% greater than with the gun, using the standard flat fan nozzles and air-induction flat fan nozzles, respectively. Also, the spray boom reduced losses to the soil by roughly 54% compared to the spray gun. With respect to the tests made with the manually pulled trolley, the results show that the standard flat fan nozzles and air-induction flat fan nozzles behaved similarly. No significant differences were found between nozzles in the mean deposition on the canopy nor in losses to the soil.
A computational fluid dynamics (CFD) model to simulate airflow from air-assisted orchard sprayers through pear canopies was validated for three different sprayers; single-fan (Condor V), two-fan (Duoprop) and four-fan sprayers (AirJet Quatt). The first two sprayers are widely used in Belgium and the latter one is a new design. Validation experiments were carried out in an experimental orchard (pcfruit, Velm, Belgium) in spring 2008. Ultrasonic anemometers were used to measure the time-averaged velocity components at different vertical positions before the tree and after the tree when the sprayers were driven through the orchard. The model was able to predict accurately the peak jet velocity, Um from all the sprayers considered at all distances from the sprayer centre and vertical positions. More than 95% of the local relative errors of Um were below 20%. Average relative errors, E, and root mean square errors, ERMS, were all less than 11.04% and 1.68 m s−1, respectively. The regions of high- (up to 18.0 m s−1 upstream) and low (down to 2.8 m s−1 downstream)-air velocity zones for all the sprayers were accurately predicted. The simulation results showed that the Condor V sprayer had a highly disturbed vertical jet velocity profile, especially at higher heights. The Duoprop sprayer had high jet velocities at the two-fan positions and lower jet velocity in between the two fans. Within the canopy height the AirJet Quatt sprayer showed a more uniform distribution of air than the other two sprayers except the minor peaks at the fan positions. These situations were all confirmed by the measurements.
The performance of several commercial and experimental software packages (Gotas, StainMaster, ImageTool, StainAnalysis, AgroScan, DropletScan and Spray_imageI and II) that produce indicators of crop spraying quality based on the image processing of water-sensitive papers used as artificial targets were compared against known coverage, droplet size spectra and class size distribution verified through manual counting. A number of artificial targets used to test the software were obtained by controlled spray applications and given droplet density between 14 and 108 drops cm−2 and a wide range of droplet size spectra. The results showed that artificial targets coupled with an appropriate image system can be an accurate technique to compute spray parameters. The between-methods differences were 6.7% for droplet density, 11.5% for volume median diameter, <3% for coverage (%) and <3% coverage density. For the 16 droplet class size distribution tested the between-methods differences were all <15%. However, most of the image analysis systems were not effective in accurately measuring coverage density when coverage rate is greater than about 17%. The Spray_imageII software estimated the coverage density with a mean absolute error of 2% and the absolute error is below 10%, even with about 43% of coverage rate. This software, when compared to the other programmes tested, provided the best accuracy for coverage and droplet size spectrum as well as for droplet class size distribution.