Impaction of spray droplets on leaves: influence of formulation and leaf character on shatter, bounce and adhesion

This paper combines experimental data with simple mathematical models to investigate the influence of spray formulation type and leaf character (wettability) on shatter, bounce and adhesion of droplets impacting with cotton, rice and wheat leaves. Impaction criteria that allow for different angles of the leaf surface and the droplet impact trajectory are presented; their predictions are based on whether combinations of droplet size and velocity lie above or below bounce and shatter boundaries. In the experimental component, real leaves are used, with all their inherent natural variability. Further, commercial agricultural spray nozzles are employed, resulting in a range of droplet characteristics. Given this natural variability, there is broad agreement between the data and predictions. As predicted, the shatter of droplets was found to increase as droplet size and velocity increased, and the surface became harder to wet. Bouncing of droplets occurred most frequently on hard to wet surfaces with high surface tension mixtures. On the other hand, a number of small droplets with low impact velocity were observed to bounce when predicted to lie well within the adhering regime. We believe this discrepancy between the predictions and experimental data could be due to air layer effects that were not taken into account in the current bounce equations. Other discrepancies between experiment and theory are thought to be due to the current assumption of a dry impact surface, whereas, in practice, the leaf surfaces became increasingly covered with fluid throughout the spray test runs.Comment: 19 pages, 6 figures, accepted for publication by Experiments in Fluid

The influence of Unmanned Agricultural Aircraft System design on spray drift

Es wurden Feldversuche durchgeführt, um den Einfluss der Bauart von unbemannten Luftfahrzeugen (Unmanned Agricultural Aircraft Systems, UAAS) auf das Bodense­diment der Abdrift im Ackerbau festzustellen. Zudem wurde die Verteilung der Spritzflüssigkeit auf der Behand­lungsfläche ermittelt. Zusätzlich wurde als mögliche Alternative zur Messung des Bodensediments auch das luftgetragene Abdriftpotenzial am Rand der Behandlungsfläche bestimmt.Vier verschiedene UAAS dreier unterschiedlicher Bauarten, ein 1-Rotor-, ein 6-Rotor- und zwei 8-Rotor-UAAS wurden untersucht. Alle UAAS hatten unterschiedliche Spritzgestänge, wurden aber jeweils mit gleichen Düsen bestückt: Lechler TR 80–0067 und Lechler IDK 120–015, mit denen jeweils 40 l ha–1 bzw. 75 l ha–1 appliziert wurden.Weder für die UAAS-Bauart noch für die Düse konnte ein Einfluss auf die Verteilung der Spritzflüssigkeit auf der Behandlungsfläche festgestellt werden; der Variationskoeffizient der Querverteilung lag generell zwischen 40% und 50%.Die Untersuchungsergebnisse zeigen, dass der Einfluss der UAAS-Bauart gegenüber dem Einfluss der Düse vernachlässigbar ist. Wie bei anderen Pflanzenschutzgeräten verursachte die Hohlkegeldüse TR 80–0067 wesentlich mehr Abdrift als die Luftinjektor-Flachstrahldüse IDK 120–015. Bei beiden Düsentypen lag das Bodensediment wesentlich über den in Deutschland für die Risikobewertung im Ackerbau verwendeten Abdrifteckwerten.Zwischen dem Bodensediment und dem am Rand der Behandlungsfläche ermittelten luftgetragenen Abdriftpotenzial wurde eine enge Korrelation gefunden. Somit scheint das Abdriftpotenzial eine brauchbare Alternative, zumindest für den Vergleich unterschiedlicher Appli­kationstechniken, darzustellen. Für gesicherte Aussagen hierzu sind jedoch weitere Untersuchungen notwendig.Field experiments were conducted to determine the influence of the Unmanned Agricultural Aircraft Systems (UAAS) design on spray drift sediment during a common arable field application in consideration of the spray deposit distribution. In addition, airborne drift collectors were used to determine the initial drift potential as a possible alternative for characterising the spray drift.Four models of UAAS representing three different designs, one single rotor, one 6-rotor and two 8-rotor designs, were involved in the study. All UAASs where equipped with individual spraying systems but the same nozzles were used: Lechler TR 80–0067 and Lechler IDK 120–015, providing nominal application rates of 40 l ha–1 and 75 l ha–1, respectively.There was no influence of the UAAS design or the nozzle type on the spray distribution quality on the treated area. In general, the coefficient of spray deposit variation was 40% to 50%.The results of the study show that the effect of the UAAS design on spray drift was relatively low compared to the influence of the type of nozzles used. As for other application techniques, the conventional hollow cone nozzle TR 80–0067 produced much more spray drift compared to the air induction flat fan nozzle IDK 120–015. With both types of nozzles, the ground sediment of spray drift was much higher than the standard drift values used by German authorities for drift risk assessments for boom sprayers in arable crops.A good correlation was found between drift sediment and airborne drift potential. As the latter seems to be a suitable alternative, at least for comparing different spraying systems, further studies should be conducted also for other application techniques

Spray losses study of two pesticides by UASS in integrated rice–crayfish farming system and acute toxicity evaluation on Procambarus clarkii

IntroductionWhile the integrated rice-crayfish (Procambarus clarkii) farming system (IRCFS) is widely developing in China, the widespread use of Unmanned Aerial Spraying Systems (UASS) to protect rice from pests has led to potential pesticide risk for the crayfish in IRCFS. Therefore, it is crucial to examine UASS’s spray deposition and drift in IRCFS.MethodIn this study, we used the oligonucleotide sequence-tracking / dot-blotting (OSTDB) method to trace pesticide spraying. We collected detailed data not only on spray loss in the paddy fields, but also on spray drift in the breeding ditches caused by upwind and downwind spray areas. Additionally, pesticide residues in the breeding ditches were measured using LC-MS/MS by collecting water samples after pesticide application.ResultsThe data analysis indicated that the spray loss in the paddy field was significantly greater than that in the breeding ditches. The spray drift in the breeding ditches, caused by the upwind spray area, was seven times higher than that originating from the downwind spray area. Furthermore, the results also revealed that the bulk flow between the paddy fields and the breeding ditches contributed a substantial amount of pesticide residue to the water body in the breeding ditches. In addition, we investigated the acute toxicities of common insecticides using in paddy fields, including thiamethoxam (THI), chlorantraniliprole (CHI), THI·CHI-Mix and THI·CHI-WG.DiscussionThe results demonstrated that the spray losses and spray drift from UASS spray applications of these pesticides in IRCFS would not cause acute toxicity or death in crayfish. These findings provide important materials for establishing pesticide application standards and guiding the field testing of droplet deposition and drift in IRCFS

Development of multifunctional unmanned aerial vehicles versus ground seeding and outplanting: What is more effective for improving the growth and quality of rice culture?

The agronomic processes are complex in rice production. The mechanization efficiency is low in seeding, fertilization, and pesticide application, which is labor-intensive and time-consuming. Currently, many kinds of research focus on the single operation of UAVs on rice, but there is a paucity of comprehensive applications for the whole process of seeding, fertilization, and pesticide application. Based on the previous research synthetically, a multifunctional unmanned aerial vehicle (mUAV) was designed for rice planting management based on the intelligent operation platform, which realized three functions of seeding, fertilizer spreading, and pesticide application on the same flight platform. Computational fluid dynamics (CFD) simulations were used for machine design. Field trials were used to measure operating parameters. Finally, a comparative experimental analysis of the whole process was conducted by comparing the cultivation patterns of mUAV seeding (T1) with mechanical rice direct seeder (T2), and mechanical rice transplanter (T3). The comprehensive benefit of different rice management processes was evaluated. The results showed that the downwash wind field of the mUAV fluctuated widely from 0 to 1.5 m, with the spreading height of 2.5 m, and the pesticide application height of 3 m, which meet the operational requirements. There was no significant difference in yield between T1, T2, and T3 test areas, while the differences in operational efficiency and input labor costs were large. In the sowing stage, T1 had obvious advantages since the working efficiency was 2.2 times higher than T2, and the labor cost was reduced by 68.5%. The advantages were more obvious compared to T3, the working efficiency was 4 times higher than in T3, and the labor cost was reduced by 82.5%. During the pesticide application, T1 still had an advantage, but it was not a significant increase in advantage relative to the seeding stage, in which operating efficiency increased by 1.3 times and labor costs were reduced by 25%. However, the fertilization of T1 was not advantageous due to load and other limitations. Compared to T2 and T3, operational efficiency was reduced by 80% and labor costs increased by 14.3%. It is hoped that this research will provide new equipment for rice cultivation patterns in different environments, while improving rice mechanization, reducing labor inputs, and lowering costs

Study on Spray Evaluation: The Key Role of Droplet Collectors

Droplet collectors are commonly utilized to gauge the effect of pesticide deposition on crops. However, the varying surface characteristics of these collectors can lead to disparate data outcomes. Notably, water-sensitive paper is limited in humid environments, hindering rapid droplet deposition evaluation. Consequently, the selection of appropriate droplet collectors based on the environmental conditions is imperative. This study involved the use of five typical droplet collectors to establish a method for the swift and accurate evaluation of spray effectiveness, employing various spray liquids. It was observed that the surface free energy of five widely used droplet collectors was measured as follows: 35.11 mN m−1 for semigloss paper, 33.81 mN m−1 for coated paper laminated with polyvinyl chloride, 48.38 mN m−1 for kromekote paper (KP), 33.90 mN m−1 for polyvinyl chloride cards, and 39.95 mN m−1 for water-sensitive paper. When comparing the outcomes of deposition tests across these five collectors, it was noted that the results pertaining to droplet density were minimally influenced by the surface properties of the collectors with droplet coverage following. The volume of deposition was found to be the most susceptible to the surface characteristics of the collectors. Therefore, in the context of collecting and processing droplets, prioritizing droplet density as the metric for evaluation proved to be more reliable than using the other indicators

A Monocular Vision Obstacle Avoidance Method Applied to Indoor Tracking Robot

The overall safety of a building can be effectively evaluated through regular inspection of the indoor walls by unmanned ground vehicles (UGVs). However, when the UGV performs line patrol inspections according to the specified path, it is easy to be affected by obstacles. This paper presents an obstacle avoidance strategy for unmanned ground vehicles in indoor environments. The proposed method is based on monocular vision. Through the obtained environmental information in front of the unmanned vehicle, the obstacle orientation is determined, and the moving direction and speed of the mobile robot are determined based on the neural network output and confidence. This paper also innovatively adopts the method of collecting indoor environment images based on camera array and realizes the automatic classification of data sets by arranging cameras with different directions and focal lengths. In the training of a transfer neural network, aiming at the problem that it is difficult to set the learning rate factor of the new layer, the improved bat algorithm is used to find the optimal learning rate factor on a small sample data set. The simulation results show that the accuracy can reach 94.84%. Single-frame evaluation and continuous obstacle avoidance evaluation are used to verify the effectiveness of the obstacle avoidance algorithm. The experimental results show that an unmanned wheeled robot with a bionic transfer-convolution neural network as the control command output can realize autonomous obstacle avoidance in complex indoor scenes

A Comprehensive Review of the Research of the “Eye–Brain–Hand” Harvesting System in Smart Agriculture

Smart agricultural harvesting robots’ vision recognition, control decision, and mechanical hand modules all resemble the human eye, brain, and hand, respectively. To enable automatic and precise picking of target fruits and vegetables, the system makes use of cutting-edge sensor technology, machine vision algorithms, and intelligent control and decision methods. This paper provides a comprehensive review of international research advancements in the “eye–brain–hand” harvesting systems within the context of smart agriculture, encompassing aspects of mechanical hand devices, visual recognition systems, and intelligent decision systems. Then, the key technologies used in the current research are reviewed, including image processing, object detection and tracking, machine learning, deep learning, etc. In addition, this paper explores the application of the system to different crops and environmental conditions and analyzes its advantages and challenges. Finally, the challenges and prospects for the research on picking robots in the future are presented, including further optimization of the algorithm and improvement of flexibility and reliability of mechanical devices. To sum up, the “eye–brain–hand” picking system in intelligent agriculture has great potential to improve the efficiency and quality of crop picking and reduce labor pressure, and it is expected to be widely used in agricultural production

Development and Application of an Intelligent Plant Protection Monitoring System

Facing the need of modern agriculture to accurately grasp the information of farmland diseases and pests, this paper proposes an intelligent plant protection system. The system is composed of a wireless lens, temperature and humidity sensor, intelligent information terminal, and probe rod to realize the collection of plant images and meteorological information. At the same time, a software based on the mobile terminal and the computer terminal was developed. The plant images and meteorological data are transmitted to the server through Wi-Fi transmission. Combined with the expert knowledge model, a solution is generated, and the user can identify the current diseases and pests and obtain solutions at any time. The system can remotely and automatically monitor and warn of mainstream diseases and pests of field crops such as rice and wheat and provide support for fine plant protection management

Method of 3D Voxel Prescription Map Construction in Digital Orchard Management Based on LiDAR-RTK Boarded on a UGV

Precision application of pesticides based on tree canopy characteristics such as tree height is more environmentally friendly and healthier for humans. Offline prescription maps can be used to achieve precise pesticide application at low cost. To obtain a complete point cloud with detailed tree canopy information in orchards, a LiDAR-RTK fusion information acquisition system was developed on an all-terrain vehicle (ATV) with an autonomous driving system. The point cloud was transformed into a geographic coordinate system for registration, and the Random sample consensus (RANSAC) was used to segment it into ground and canopy. A 3D voxel prescription map with a unit size of 0.25 m was constructed from the tree canopy point cloud. The height of 20 trees was geometrically measured to evaluate the accuracy of the voxel prescription map. The results showed that the RMSE between tree height calculated from the LiDAR obtained point cloud and the actual measured tree height was 0.42 m, the relative RMSE (rRMSE) was 10.86%, and the mean of absolute percentage error (MAPE) was 8.16%. The developed LiDAR-RTK fusion acquisition system can generate 3D prescription maps that meet the requirements of precision pesticide application. The information acquisition system of developed LiDAR-RTK fusion could construct 3D prescription maps autonomously that match the application requirements in digital orchard management