In order to solve the problem of insufficient adsorption rate of droplets on the target back via aerial electrostatic spray, this study proposed a high-voltage electrostatic generator to charge the liquids in two isolated water tanks with positive and negative charges respectively. A charge transfer loop was developed in space between the aerial electrostatic spray system and the ground. This method greatly enhanced the adsorption performance under outdoor conditions that 16.7% droplets density increased on the target front, a nearly fourfold destiny increased on the target back compared with the conventional UAV spray system. The target back-to-front ratio of droplet density was improved from 6.1% to 25.7%, which validated the satisfactory performance of the developed system.

The fast and precise detection of dense litchi fruits and the determination of their maturity is of great practical significance for yield estimation in litchi orchards and robot harvesting. Factors such as complex growth environment, dense distribution, and random occlusion by leaves, branches, and other litchi fruits easily cause the predicted output based on computer vision deviate from the actual value. This study proposed a fast and precise litchi fruit detection method and application software based on an improved You Only Look Once version 5 (YOLOv5) model, which can be used for the detection and yield estimation of litchi in orchards. First, a dataset of litchi with different maturity levels was established. Second, the YOLOv5s model was chosen as a base version of the improved model. ShuffleNet v2 was used as the improved backbone network, and then the backbone network was fine-tuned to simplify the model structure. In the feature fusion stage, the CBAM module was introduced to further refine litchi’s effective feature information. Considering the characteristics of the small size of dense litchi fruits, the 1,280 × 1,280 was used as the improved model input size while we optimized the network structure. To evaluate the performance of the proposed method, we performed ablation experiments and compared it with other models on the test set. The results showed that the improved model’s mean average precision (mAP) presented a 3.5% improvement and 62.77% compression in model size compared with the original model. The improved model size is 5.1 MB, and the frame per second (FPS) is 78.13 frames/s at a confidence of 0.5. The model performs well in precision and robustness in different scenarios. In addition, we developed an Android application for litchi counting and yield estimation based on the improved model. It is known from the experiment that the correlation coefficient R2 between the application test and the actual results was 0.9879. In summary, our improved method achieves high precision, lightweight, and fast detection performance at large scales. The method can provide technical means for portable yield estimation and visual recognition of litchi harvesting robots.

Coffee production and marketing is one of the main global commercial activities, but crop yields depend on several factors, among which plant health. The objective of this study was to evaluate the efficiency of spray droplet deposition in coffee crops grown in a mountain region, associated to the efficacy of the control of fungal diseases. The application efficiency, using an unmanned aerial vehicle (UAV), and the efficacy of the products applied were tested. Water-sensitive paper tags were used to analyze the application efficiency; agronomic efficiency, vegetative vigor, yield, and physiological parameters were used to determine the fungicide efficacy. Droplet coverage in the upper canopy layer using a pneumatic sprayer (28.70%) was 4.11-fold higher than that found in the same layer for application using a UAV (6.98%) at the rate of 15 L ha−1. The highest droplet depositions by using a UAV were found for the rate of 15 L ha−1: 1.60, 1.04, and 0.43 µL cm−2 in the upper, middle, and lower layers, respectively; the deposition in the upper layer with application using a pneumatic sprayer was 42.67 µL cm−2, and therefore, a 26.7-fold higher deposition. The results denote that the control of fungal diseases through fungicide applications using a UAV is efficient for mountain coffee crops.

Objective This study analyzed changes in granulocyte-colony stimulating factor (G-CSF) and its correlation with leukocyte and neutrophil counts in patients after traumatic brain injury (TBI). Methods Sixty TBI patients were included retrospectively. The serum levels of G-CSF, tumor necrosis factor-α (TNF-α), and peripheral leukocyte and neutrophil counts at different time points were measured and analyzed, and the 6-month functional outcomes were monitored. Results The levels of G-CSF in mild and moderate TBI groups were higher than the control at the first three time points. G-CSF in the severe TBI group increased slowly and peaked at day 7, and was only significantly different from the control at day 7 and 14. The leukocyte and neutrophil counts of the mild group gradually decreased, but a second increase after day 4 was observed in the severe group. The cell counts were higher in the severe group compared to other groups. A positive correlation between G-CSF and leukocyte and neutrophil counts was observed in the severe group at day 1. G-CSF positively correlated with TNF-α in the severe group at day 4 and 7. In severe patients with a good outcome, G-CSF level at day 7 was significantly higher than those with a poor outcome. Conclusion The G-CSF levels in the severe TBI group exhibited a different pattern from those in the mild and moderate TBI groups, and these levels positively correlated with inflammatory biomarkers. Higher G-CSF levels in severe TBI at day 7 indicated a good outcome at 6 months.

In this study research was conducted to evaluate the feasibility of characterizing genetic variation within camellia species using random amplified polymorphic DNAs (RAPD) markers. Eight varieties of species Camellia japonica and four varieties of species Camellia reticulata, provided by the America Camellia Society, Fort Valley, Ga., were investigated. RAPD profiles generated by five selected 10-based random primers (out of 20 primers) exhibited distinct patterns of amplified bands for all 12 tested varieties. A total of 344 bands were produced among the eight varieties of species C. japonica, with an average of 8.6 bands, ranging from 220 to 2072 bp in size, scored per primer. Among the 344 amplified bands, 74.4% of the bands presented polymorphic. The four varieties of species C. reticulata produced a total of 180 markers, with an average percentage of 57.8% polymorphisms. The amplified bands were in the range of 236–1760 bp. An average of nine amplified bands was generated per primer. The large percentages of polymorphisms displayed among 12 varieties within the two different species indicate that the expected genetic diversity among varieties within camellia species existed. It was concluded that the RAPD molecular markers are capable of revealing appreciable levels of genetic variation within camellia species.

It is a common understanding in the civilized world that we should protect our environment from further damage, no matter upon what kind of political or ideological background this understanding is based or upon what kind of development level. 1 In the academic field, people are more interested in discussing HOW to protect our environment. We all generally agree that this is also a very important issue, because the methods and ways people take in environmental protection do not only reflect their perspectives in their understanding in protecting environment, but also provide us with an efficient and economical ways to do so. In order to achieve this academic object, the exchange of academic ideas and information and a comparative study is of great significance. This article, therefore, is an attempt promote this kind of exchange and comparison.

The multi-rotor micro-UAV has become an important platform for assessing crop information promptly given its high flexibility, compact size, low cost, and high spatial resolution. However, considering the limits of the stability of the micro-UAV control system and the precision of automatic navigation systems, how to timely adjust the position and attitude of UAVs to ensure the target within the scope of monitoring is one of the key techniques which determines whether micro-UAVs can be widely used in precision agriculture as a remote sensing platform. In this study, the integrated navigation system of INS/GPS (Inertial Navigation System/Global Positioning System) and EKF (Extended Kalman Filter) was adopted as the navigation system and fusion algorithm for simulation analysis respectively, to monitor the position and attitude of UAVs more accurately and thus improve the estimation accuracy and control precision. An autonomous flight experiment was designed and carried out, and experimental data collected by commercially available UAVs. LabVIEW was used to analyze and process all experimental data and outputted flight state graphs, which reflected the optimization effect of EKF algorithm and control precision visually.

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