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This study was aimed to reduce the amount of the sprayed solution lost during trees spraying.  At the same time, the concentration of the sprayed solution on the target (tree or bush) must be ensured and to find the best combination of treatments. Two factors controls the spraying process: (i) spraying speed (1.2 km/h, 2.4 km/h, 3.6 km/h), and (ii) the type of sensor. The test results showed a significant loss reduction percentage. It reached (6.05%, 5.39% and 2.05%) at the speed (1.2 km/h, 2.4 km/h, 3.6 km/h), respectively. It was noticed that when the speed becomes higher the loss becomes less accordingly. The interaction between the 3.6 km/h speed and the type of Ultrasonic sensor led to a decrease in the percentage of the spray losses reached to 1.69. For the coverage percentage, the increase in the spraying speed from 1.2 km/h to 2.4 km/h, and then to 3.6 km/h led to a significant decrease in the percentage of coverage (from 17.73% to 13.14%, and then to 11.12%), respectively. The interaction between the type of sensor and the speed has significantly affected the spray density. The speed was 3.6 km/h, and the type of Ultrasonic sensor was superior in obtaining the highest spray density of 83.2 drops/cm2.

The pulse width modulation (PWM) spray system is the most advanced technology to obtain variable rate spray application without varying the operative sprayer parameters (e.g. spray pressure, nozzle size). According to the precision agriculture principles, PWM is the prime technology that allows to spray the required amount where needed without varying the droplet size spectra which benefits both the uniformity of spray quality and the spray drift reduction. However, some concerns related to the effect of on–off solenoid valves and the alternating on/off action of adjacent nozzles on final uneven spray coverage (SC) have arisen. Further evaluations of PWM systems used for spraying 3D crops under field conditions are welcomed. A tower-shaped airblast sprayer equipped with a PWM was tested in a vineyard. Twelve configurations, combining duty cycles (DC: 30, 50, 70, 100%) and forward speeds (FS: 4, 6, 8 km h−1), were tested. Two methodologies, namely field-standardized and real field conditions, were adopted to evaluate the effect of DC and FS on (1) SC variability (CV%) along both the sprayer travel direction and the vertical spray profile using long water sensitive papers (WSP), and (2) SC uniformity (IU, index value) within the canopy at different depths and heights, respectively. Furthermore, the SC (%) and deposit density (Nst, no stains cm−2), determined using short WSP, were used to evaluate the spray application performances taking into account the spray volumes applied. Under field-controlled conditions, the pulsing of the PWM system affects both the SC variability measured along the sprayer travel direction and along the vertical spray profile. In contrast, under real field conditions, the PWM system does not affect the uniformity of SC measured within the canopy. The relationship between SC and Nst allowed identification of the ranges of 200–250 and 300–370 l ha−1 as the most suitable spray volumes to be applied for insecticide and fungicide plant protection products, respectively.

Precision and efficient pesticide spraying is an important part of precision agriculture, banana is a large broad-leaved plant, with pests and diseases, has a high demand for spraying and pest control. The purpose of this study was to clarify the wettability of different pesticides on the banana leaf surface, and the effects of nozzle type and working parameters on the deposition distribution performance under air-assisted spray conditions. The wettability test results of different pesticides on banana leaf surfaces showed that the wettability of the adaxial side was always stronger than that of the abaxial side, the smaller the surface tension of the droplets, the better the wettability on the surface. The spray experiment was carried out on the previously developed air-assisted sprayer with the latest developed intelligent variable spray control system. Three types of nozzles were used to spray with different combinations of working parameters. The deposition distribution performance on the banana leaf surface was obtained by image processing using a self-compiled program. The experimental results show that the nozzle type, wind speed, and spray pressure have significant effects on the deposition distribution performance. Through the study of the interaction and coupling effect of nozzle type and working parameters on the spray droplet deposition distribution on both sides of banana leaves, the results show that under the conditions of hollow cone nozzle, 0.5Mpa spray pressure and 3-5 m/s wind speed, the spray coverage and droplet density are in the optimal state. This is mainly due to the low spray pressure and/or wind speed is not enough to make the banana leaves vibrate and improve the performance of pesticide deposition. excessive spray pressure and/or wind speed will cause large deformation of banana leaves and make them airfoil stable, which reduces the surface deposition performance. It is of great significance for promoting sustainable and intelligent phytoprotection.

Plant protection drone spraying technology is widely used to prevent and control crop diseases and pests due to its advantages of being unaffected by crop growth patterns and terrain restrictions, high operational efficiency, and low labor requirements. The operational parameters of plant protection drones significantly impact the distribution of spray droplets, thereby affecting pesticide utilization. In this study, a field experiment was conducted to determine the working modes of two representative plant protection drones and an electric backpack sprayer as a control to explore the characteristics of droplet deposition with different spray volumes in the citrus canopy. The results showed that the spraying volume significantly affected the number of droplets and the spray coverage. The number of droplets and the spray coverage area on the leaf surface were significantly increased by increasing the spray volume from 60 L/ha to 120 L/ha in plant protection drones. Particularly for the DJI T30, the mid-lower canopy showed a spray coverage increase of 52.5%. The droplet density demonstrated the most significant variations in the lower inner canopy, ranging from 18.7 droplets/cm 2 to 41.7 droplets/cm 2 by XAG V40. From the deposition distribution on fruit trees, the plant protection drones exhibit good penetration ability, as the droplets can achieve a relatively even distribution in different canopy layers of citrus trees. The droplet distribution uniformity inside the canopy is similar for XAG V40 and DJI T30, with a variation coefficient of approximately 50%-100%. Compared to the plant protection drones, the knapsack electric sprayer is suitable for pest and disease control in the mid-lower canopy, but they face challenges of insufficient deposition capability in the upper canopy and overall poor spray uniformity. The distribution of deposition determined in this study provides data support for the selection of spraying agents for fruit trees by plant protection drones and for the control of different pests and diseases.

Pesticide spraying is one of the most significant processes in agricultural production and one of the most complicated, risky agricultural operations. Side effects of pesticides can cause acute poisoning and serious chronic diseases in humans. Robotic spraying in agriculture is one solution to avoid human intervention. However, there has been little research on the distribution of droplets and unwanted spray drift when spraying with ground spraying robots equipped with jet spraying systems. This study analyses the downwind spray drift of three drift reduction agents (DRAs) depending on the lateral wind velocity using a ground spraying robot equipped with a jet spraying system in the field under conditionally controlled conditions. The three DRAs investigated were: DRA1 (100% anionic polymer dispersion), DRA2 (calcium dodecylbenzene sulfonate 50%, butanol 18%), and DRA3 (C10-13-alkyl derivatives, calcium salt 37%, butanol 15%). DRA solutions at a concentration of 0.1% (water as control) were sprayed with a jet spraying system and analyzed at four different droplet diameter levels ranging from VMD preset =60 μm to 120 μm, with a change every 20 μm. The study showed that the atomization level of droplets had a significant effect on the impact of spray drift: the smaller the droplets are sprayed ( VMD preset =60–80 μm), the lower the effectiveness of DRA (spray drift can be reduced by about 2.5-fold) while spraying larger droplets ( VMD preset =100–120 μm) with DRA reduces drift by about 3.5-fold (at the lateral wind of 4 m s −1 ). The use of DRAs also significantly impacted the reduction of spray drift. All DRA solutions were significantly more effective at low lateral winds (2–4 m s −1 ). Moreover, the difference between the effectiveness of DRA solutions decreases with increasing lateral wind velocity from 2 to 10 m s −1 . In summary, the following management measures can be used to control droplet drift using a robotic jet spraying system, in order of importance: lateral wind velocity, selection of the level of droplet atomization, and the use of DRAs. This can help to find the optimal solution to ensure optimal coverage of plants with plant protection products and to minimize losses and negative environmental impacts.

Fungicide application technology to control soybean rust (SBR) is lacking and requires optimization to improve spray coverage in the lower part of the crop canopy as well as the spray distribution uniformity. The goal of this study was to evaluate the effects of flat fan nozzles with different angles and spray volumes on the optimization of fungicide application in soybean, as well as on SBR and its effect on crop yield. For this purpose, a 2-year (2016–2018) field experiment was conducted in Botucatu, SP, Brazil. Three spray nozzles were evaluated: flat fan, double flat fan and angled flat fan, with two spray volumes. A quantitative analysis of the spray deposition was conducted, assessing the spray deposits on the lower and upper part of the plants with Brilliant Blue tracer. Furthermore, SBR severity was evaluated based on the number of pustules cm−2 and on the AUDPC, as well as the establishment of treatment control efficacy and its effect on soybean yield. Irrespective of the spray nozzles and volumes, the penetration of the droplets into the crop canopy was impaired at the reproductive stage, with less deposition in the lower part of the plant, although the larger spray volume provided greater spray deposition. All the treatments promoted effective control of the disease, with no changes in efficacy due to a larger spray volume or angled nozzles. The correct spray volume, especially with respect to the different growth stages, greatly influences spray deposition and penetration.

Automated technologies in precision agriculture enable unmanned systems to precisely target areas with chemicals through controlled nozzle movements. Quantitative assessment of these sprayers can enhance spraying strategies, catering to different canopy sizes, row spacing and coverage objectives. This research assessed an unmanned sprayer equipped with pan-tilt nozzles for targeted area control and spray coverage adjustment. The spray cloud path on the canopy, as the nozzles moved vertically and the sprayer advanced, was simulated mathematically. A model was developed to determine the swing angle based on orchard/vineyard geometrical parameters. This model was then applied in field tests in a vineyard and an apple orchard. Various nozzle-heading angles, driving speeds, and flow rates were experimented with, using average coverage and droplet density as the evaluation criterion. The findings showed that the developed model offered an effective method for determining the swing angles. Lowering driving speeds and increasing flow rates were found to notably enhance coverage. A 45º nozzle-heading angle proved more effective in vineyards, whereas a 90º angle yielded better results in apple orchards, reflecting the variations in canopy size and row spacing. The unmanned sprayer demonstrated great potential for autonomous spraying in vineyards and orchards.

Pesticide spray drift has been a worldwide concern in terms of potential environmental pollution and ecosystem damage. This study defined the main drift reduction agent (DRA) characteristics that help to understand the drift formation process in agricultural spraying. Seven various DRAs and water were evaluated. Three solutions were created based on the following materials: calcium dodecylbenzenesulfonate, benzenesulfonic acid, C10-13-alkyl derivatives, and calcium salt. Drift measurements were performed by means of the open circuit-type wind tunnel and in the field under conditionally controlled conditions. Air-injector flat spray nozzles and standard flat spray nozzles were used during trials. The spray pressure was 4.0 bar. Solutions were sprayed at different wind speeds (from 2 m s−1 to 10 m s−1, increasing every 2 m s−1). Studies have shown that wind speed and nozzle design have the greatest influence on spray drift. For all DRA solutions studied, the standard flat spray nozzles resulted in ground spray drift, both in the wind tunnel and in the field, which was about two times higher than that of air-injector flat spray nozzles. The spraying of water and all DRA solutions with the air-injector flat spray nozzle showed that all new solutions statistically significantly reduced the drift both in the tunnel and in the field. Ground-drift studies in the wind tunnel showed a trend towards a less intense drift reduction in DRA droplets with increasing wind speed. With DRA7e, the drift can be reduced by up to 56% (at a wind speed of 4 m s−1) and up to 30% (at 10 m s−1). The effect of the solutions on the reduction in spray drift is significantly lower when spraying with standard flat spray nozzles. Spray drift can then be reduced by up to 30% (at a wind speed of 4 m s−1) and up to 12% (at 10 m s−1) for DRA7e.

To optimize pesticide applications to the canopies of deciduous perennial crops, spray volume should be adjusted throughout the year to match the changes in canopy volume and density. Machine-vision, computer-controlled, variable-rate sprayers are now commercially available and claim to provide adequate coverage with decreased spray volumes compared with constant-rate sprayers. However, there is little research comparing variable- and constant-rate spray applications as crop characteristics change throughout a growing season. This study evaluated spray volume, spray quality (e.g., coverage and deposit density), and off-target spray losses of variable- and constant-rate sprayers across multiple phenophases in an apple ( Malus domestica ) orchard and a grape ( Vitis vinifera ) vineyard. The variable-rate sprayer mode applied 67% to 74% less volume in the orchard and 61% to 80% less volume in the vineyard. Spray coverage (percent), measured by water-sensitive cards (WSC), was consistently greater in the constant-rate mode compared with the variable-rate mode, but in many cases, excessive coverage (i.e., over-spray) was recorded. The variable-rate sprayer reduced off-target losses, measured by WSC coverage, up to 40% in the orchard and up to 33% in the vineyard. Spray application deposit densities (droplets per square centimeter) on target canopies were typically greater in variable-rate mode. However, the deposit densities were confounded in over-spray conditions because droplets coalesced on the WSC resulting in artificially low values (i.e., few, very large droplets). Spray efficiencies were most improved early in the growing season, when canopy density was lowest, demonstrating the importance of tailoring spray volume to plant canopy characteristics.

Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of Things (IoT) UAV systems, especially when used as sprayer UAVs, where data needs to be captured and preprocessed for robust real-time obstacle detection and collision avoidance. Moreover, because of the objective and operational difference between general UAVs and sprayer UAVs, not every obstacle detection and collision avoidance method will be sufficient for sprayer UAVs. In this regard, this article seeks to review the most relevant developments on all correlated branches of the obstacle avoidance scenarios for agricultural sprayer UAVs, including a UAV sprayer’s structural details. Furthermore, the most relevant open challenges for current UAV sprayer solutions are enumerated, thus paving the way for future researchers to define a roadmap for devising new-generation, affordable autonomous sprayer UAV solutions. Agricultural UAV sprayers require data-intensive algorithms for the processing of the images acquired, and expertise in the field of autonomous flight is usually needed. The present study concludes that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions.