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.
Due to the change of illumination environment and overlapping conditions caused by the neighboring fruits and other background objects, the simple application of the traditional machine vision method limits the detection accuracy of lychee fruits in natural orchard environments. Therefore, this research presented a detection method based on monocular machine vision to detect lychee fruits growing in overlapped conditions. Specifically, a combination of contrast limited adaptive histogram equalization (CLAHE), red/blue chromatic mapping, Otsu thresholding and morphology operations were adopted to segment the foreground regions of the lychees. A stepwise method was proposed for extracting individual lychee fruit from the lychee foreground region. The first step in this process was based on the relative position relation of the Hough circle and an equivalent area circle (equal to the area of the potential lychee foreground region) and was designed to distinguish lychee fruits growing in isolated or overlapped states. Then, a process based on the three-point definite circle theorem was performed to extract individual lychee fruits from the foreground regions of overlapped lychee fruit clusters. Finally, to enhance the robustness of the detection method, a local binary pattern support vector machine (LBP-SVM) was adopted to filter out the false positive detections generated by background chaff interferences. The performance of the presented method was evaluated using 485 images captured in a natural lychee orchard in Conghua (Area), Guangzhou. The detection results showed that the recall rate was 86.66%, the precision rate was greater than 87% and the F1-score was 87.07%.
A spraying system for a plant-protection unmanned aerial vehicle (UAV) was designed to reduce spray drift. A custom low-speed wind tunnel was constructed to generate a wind speed ranging from 0 to 5.92 m/s. The results showed that the wind speed was attenuated with an increase in distance. To compensate for the attenuation, a linear-fitting model was adopted. Then, the relationship between the spraying pressure and atomization rate was analyzed, and a fuzzy algorithm was adopted to adjust the spraying angle and pressure according to the wind speed and its changing rate. Finally, an evaluation of the proposed system in the compensated wind tunnel was conducted, and the drift distance was reduced by 33.7% compared with the system without adjustment of the spraying angle and pressure.
Although high quantum efficiency has been achieved in large-sized InGaN/GaN LEDs operating at relatively high current densities (above 35 A/cm 2 ), the operating current density of mini-LEDs (around 1 A/cm 2 ) is far less than that of traditional large-sized LEDs. The low external quantum efficiency (EQE) of mini-LEDs at small current densities seriously hinders their practical applications, highlighting the importance of investigating the radiative recombination mechanisms of mini-LEDs at small current densities. By using microscopic hyperspectral imaging, the cryogenic electroluminescence (EL) of GaN-based green mini-LEDs mainly originating from localized excitons was demonstrated experimentally. Based on the dependence of electron−phonon coupling on current and temperature, Coulomb screening of the polarization field weakens the electron−phonon coupling, whereas the band-filling effect enhances the coupling. Coulomb screening of the polarization field can also reduce the deviation of the localization. The EL from edge regions of the mesa adjacent to the sidewall possesses relatively higher peak energy due to the strain relaxation. Results of this work also suggest that optimization of the chromatic characteristics and efficiency can be achieved by strain engineering of GaN-based green mini-LEDs.
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