Effects of operation height and tree shape on droplet deposition in citrus trees using an unmanned aerial vehicle

Tang, Yu; Hou, Chao; Luo, Shaoming; Lin, Jintian; Yang, Zhengang; Huang, W. F.

doi:10.1016/j.compag.2018.02.026

Cited by 100 publications

(75 citation statements)

References 10 publications

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“…Then, the number of this kind of study highly increased from 2016 as well as expanded to other sprayed targets. For instance, Zhang et al started to focus on UAV application for citrus and found the variation of droplet distribution [12] , while Tang et al concentrated on the deposition changes affected by tree shapes [13] . Similar studies regarding UAVs and spray factors include T10 UAV for wheat scab control [14] , Response Surface Method for the test of P20 UAV parameters [15] , tree shapes and flight heights for the deposition of 3W-LWS-Q60S [16] , the comparison of four types of UAVs [17] , the method of detection of effective spray swath of 3WQF120-12 [18] , different operation parameters for several stages of corns [19] , low-attitude droplets deposition [20] , drift caused by different types of UAVs [21] , deposition on pineapple trees impacted by operation parameters [22] and the control effect of wheat powdery mildew using different operation parameters [23] .…”

Section: Key Technology Of Caused Results Of Uav Downwash Acting On Smentioning

confidence: 99%

Research status and trends of downwash airflow of spray UAVs in agriculture

Yang

Zheng

Liu

2019

International Journal of Precision Agricultural Aviation

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Downwash airflow of spray UAVs in agriculture has been gradually focused on and a few studies have already been involved in it. Thus, this paper firstly reviewed the current research of downwash airflow of UAVs for agricultural spray. It is found that Chinese scholars have published the majority of the studies in this area. Furthermore, two aspects of this kind of research, one, the work related to the caused results of UAV downwash acting on spray, the other, that regarding downwash itself for investigation, were analysed. It is illustrated that the present research usually uses water-sensitive paper, electronic devices, like wind-speed arrays and PIV, and CFD simulation. However, the existed electronic devices have some limitations, such as lacking of the ability of showing and demonstration of downwash in big enough areas in detail during recording. Moreover, the studies directly related to downwash have been just launched around the world and the examination for multi-rotor UAVs is even seriously not enough. Therefore, in terms of research trends, it is significant to develop specialised sensors for downwash measurement. The general and reliable CFD models of a class of UAVs are also needed. In addition, the combination of UAV downwash with real applications and actual production is important for the clarification of the details in different situations.

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Section: Key Technology Of Caused Results Of Uav Downwash Acting On Smentioning

confidence: 99%

Research status and trends of downwash airflow of spray UAVs in agriculture

Yang

Zheng

Liu

2019

International Journal of Precision Agricultural Aviation

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“…The images of the droplet deposition states were processed using MATLAB, which could determine the spray coverage of the target leaf. The ratio of the number of pixels occupied by the red fog droplets in the captured image to the total number of pixels in the image was taken as the result of the droplet coverage rate C dro [38]. The target leaf surface droplet deposition state was recorded, and the surface droplet drop coverage was calculated.…”

Section: Spray Parameters Valuementioning

confidence: 99%

Orchard Spray Study: A Prediction Model of Droplet Deposition States on Leaf Surfaces

Cui

et al. 2020

Agronomy

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During air-assisted spraying operations in orchards, the interaction between the droplets and the target leaves has a decisive influence on the retention of the droplets on the leaves and the final deposition state. Based on the observation of the final deposition effect of the droplets in the spray test, the retention state of the droplets on the leaves is divided into three categories: uniform distribution (hereinafter referred to as uniform), accumulation, and loss. During the initial interaction between the droplets and the leaves, the adhesion or sliding state of the droplets has an important influence on the final deposition state of the droplets, which is determined by the target leaf adhesion work in this paper. Based on obtaining the characteristic parameters of the leaf surface, a theoretical model of adhesion work related to parameters such as the contact angle, rough factor, and initial tilt angle of the leaf is established. Afterward, through the connection of the droplet coverage on the macro level, the establishment of the deposition state model of the droplet group on the leaf is completed. By conducting the experiment test based on the Box-Behnken design of response surface methodology (RSM), the droplet deposition states under the influence of the spray distance, fan outlet wind speed and droplet size were studied and compared with the predicted values. The test results show that the prediction accuracies of the three states of uniform, accumulation, and loss were 87.5%, 80%, and 100%, respectively. The results of the study indicate that the established prediction model can effectively predict the deposition states of droplets on leaves and provide a reference for the selection of spray operation parameters.

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“…An overall image of the orchard is shown in Figure 1a. Considering the spraying height [32], the topographic relief of the orchard, and the height of the fruit trees, the camera distance for the UAV was set to 10-15 m. During each image acquisition process, the image collection section was set vertically downward, and the UAV was suspended steadily in the air until each image was fully collected. The data were stored as joint photographic expert group (JPEG) format to implement a debugging algorithm on a personal computer (PC).…”

Section: The Study Area and The Uav Equipmentmentioning

confidence: 99%

Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment

Chen

Hou

Tang

et al. 2019

Sensors

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The segmentation of citrus trees in a natural orchard environment is a key technology for achieving the fully autonomous operation of agricultural unmanned aerial vehicles (UAVs). Therefore, a tree segmentation method based on monocular machine vision technology and a support vector machine (SVM) algorithm are proposed in this paper to segment citrus trees precisely under different brightness and weed coverage conditions. To reduce the sensitivity to environmental brightness, a selective illumination histogram equalization method was developed to compensate for the illumination, thereby improving the brightness contrast for the foreground without changing its hue and saturation. To accurately differentiate fruit trees from different weed coverage backgrounds, a chromatic aberration segmentation algorithm and the Otsu threshold method were combined to extract potential fruit tree regions. Then, 14 color features, five statistical texture features, and local binary pattern features of those regions were calculated to establish an SVM segmentation model. The proposed method was verified on a dataset with different brightness and weed coverage conditions, and the results show that the citrus tree segmentation accuracy reached 85.27% ± 9.43%; thus, the proposed method achieved better performance than two similar methods.

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Effects of operation height and tree shape on droplet deposition in citrus trees using an unmanned aerial vehicle

Cited by 100 publications

References 10 publications

Research status and trends of downwash airflow of spray UAVs in agriculture

Research status and trends of downwash airflow of spray UAVs in agriculture

Orchard Spray Study: A Prediction Model of Droplet Deposition States on Leaf Surfaces

Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment

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