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On-line detection of fault characteristic gases released by the insulation oil of transformers is developed as a scientific basis for estimating explosion accidents. However, the performance of most gas sensors, such as H2 and C2H2 sensors, is usually affected by temperature, and there exists large detection error when the temperature of transformer oil changes in operation. And the existing temperature compensation methods suffer from problems such as low integration, large volume, and high costs. Herein, a sensor array based on three triple-electrode sensors with carbon nanotubes was fabricated. Each of the three sensors has three vertically stacked electrodes with various interelectrode distances. The first and second sensors were used to detect H2 and C2H2, respectively, and the third sensor was used to measure temperature. All the detection mechanisms are based on the dependence of discharge current on gas concentration and temperature. The sensor array could detect H2/C2H2 mixture and temperature due to the exponential function between the output currents and electrode separations. The proposed sensor exhibited low relative and repeatability error values less than 5.1% and 4.9%, respectively, and displayed humidity compensation potential like temperature. High integration makes the sensor array superior among the other mechanism sensors.

Rotor unmanned aerial vehicles (UAVs) for pesticide spraying have been widely used in China during the past three years. In order to improve the effectiveness of pesticide application and reduce environmental risk caused by spray drift, it is important to clarify the spatiotemporal distribution characteristics of airflow field of the drone. The airflow field produced by the UAV plays a key role in droplets delivery during the spraying. In this study, the lattice Boltzmann method (LBM) based on a mesoscopic kinetic model was used to simulate the airflow field of a six–rotor plant protection drone. The airflow field of drone in hover and at varied flight speeds (1.0–5.0 m s−1) and various altitudes (1.5–3.5 m) was investigated. The characteristics of airflow separation, airflow coverage equivalent area and “steep” effect were investigated numerically. The peak value of vertical downward velocity (V–Y) on the detection surface was analysed. Results indicate that the flight speed and altitude had a significant effect on the distribution of the airflow field. The predicted values in the vertical direction using the average velocity attenuation model (Y–DAVA) corresponded well with experimental measurements. The wake of airflow field had a significant backward tilt when the drone was flying forwards, thus when the flight speed was 4.0 m s−1 and 5.0 m s−1, the wake of the airflow field lifted off the ground, whereas the transverse separation appeared as horseshoe vortices. For flight speeds of 3.0 m s−1 and an altitude of 3.0 m the distribution of V–Y was the most uniform.

Accurate acquisition of the canopy information of fruit trees is very important for the precise variable spray of modern orchards. Using drones to obtain fruit tree canopy images is a better nondestructive method, but the lighting conditions in the orchard are complex. It is difficult to quickly extract the canopy of fruit trees from the aerial images of orchard by UAV, which can be used to guide UAV to apply pesticide in real time and accurately. Therefore, we propose a method for canopy segmentation of fruit tree canopy images using SegNet network model. We use images obtained from modern orchards to verify the accuracy and real time of the network, and use four indicators to compare our network model with Unet and FCN-8s network models: accuracy, precision, recall and harmonic average. After that, we optimize the SegNet network model structure from three aspects: input method, network training parameters and neural network structure. The results show that SegNet has achieved satisfactory results in segmenting the canopy. The optimized SegNet model has an average recognition accuracy of 95.30%, and the recognition time of a single image is as low as 0.045 s, and it has good robustness in both strong and weak light environments. This shows that using SegNet network segmentation to extract fruit tree canopy information is a promising method, and it can provide a reference for real-time and accurate spray of UAVs.

Highly effective pesticide applications require a continual adjustment of the pesticide spray flow rate that attends to different canopy characterizations. Real-time image processing with rapid target detection and data-processing technologies is vital for precision pesticide application. However, the extant studies do not provide an efficient and reliable method of extracting individual trees with irregular tree-crown shapes and complicated backgrounds. This paper on our study proposes a Mahalanobis distance and conditional random field (CRF)-based segmentation model to extract cherry trees accurately in a natural orchard environment. This study computed Mahalanobis distance from the image’s color, brightness and location features to acquire an initial classification of the canopy and background. A CRF was then created by using the Mahalanobis distance calculations as unary potential energy and the Gaussian kernel function based on the image color and pixels distance as binary potential energy. Finally, the study completed image segmentation using mean-field approximation. The results show that the proposed method displays a higher accuracy rate than the traditional algorithms K-means and GrabCut algorithms and lower labeling and training costs than the deep learning algorithm DeepLabv3+, with 92.1%, 94.5% and 93.3% of the average P, R and F1-score, respectively. Moreover, experiments on datasets with different overlap conditions and image acquisition times, as well as in different years and seasons, show that this method performs well under complex background conditions, with an average F1-score higher than 87.7%.

To minimize pesticide dosage and its adverse environmental impact, Unmanned Aerial Vehicle (UAV) spraying requires precise individual canopy information. Branches from neighboring trees may overlap, preventing image-based artificial intelligence analysis from correctly identifying individual trees. To solve this problem, this paper proposes a segmentation and evaluation method for mingled fruit tree canopies with irregular shapes. To extract the individual trees from mingled canopies, the study fitted the projection curve distribution of the interlacing tree with Gaussian Mixture Model (GMM) and solved the matter of segmentation by estimating the GMM parameters. For the intermingling degree assessment, the Gaussian parameters were used to quantify the characteristics of the mingled fruit trees and then as the input for Extreme Gradient Boosting (XGBoost) model training. The proposed method was tested on the aerial images of cherry and apple trees. Results of the experiments show that the proposed method can not only accurately identify individual trees, but also estimate the intermingledness of the interlacing canopies. The root mean squares (R) of the over-segmentation rate (Ro) and under-segmentation rate (Ru) for individual trees counting were less than 10%. Moreover, the Intersection over Union (IoU), used to evaluate the integrity of a single canopy area, was greater than 88%. An 84.3% Accuracy (ACC) with a standard deviation of 1.2% was achieved by the assessment model. This method will supply more accurate data of individual canopy for spray volume assessments or other precision-based applications in orchards.

The three kinds of gases such as NO 2 , NO, and SO 2 produced by industrial combustion process are the main sources of atmospheric pollution. Almost all the available technologies of measuring three components in mixed gases are limited by detection range, operating temperature, and integration, etc. Here we report an array with three triple-electrode carbon nanotube sensors and its sensitivity to the three flue gas components. The three sensors operate at the same gas ionization mechanism and are comprised of a common long cathode, three extracting electrodes and three collecting electrodes, and are set by different electrode separations with $75\ \boldsymbol{\mu} \mathbf{m}, 100\ \boldsymbol{\mu} \mathbf{m}$ , and $120\ \boldsymbol{\mu} \mathbf{m}$ , respectively. We explored and obtained distinct single-valued sensitive characteristics to the three-component gases at given voltages applied on electrodes, which should be higher than a critical voltage to obtain single-valued sensitivities of the three sensors. The standard deviation of repeatability experiment is less than 0.133 nA, which indicates that the sensor has a good repeatability. The array displays a high level of integration and small size, and also has the potential to detect the concentrations of different components of gas mixtures.

Measuring the concentration of oxynitride NOx (including NO and NO2) can provide a scientific basis for determining its potential harm to the human body. The existing metal oxide semiconductor sensor arrays are greatly limited by high operating temperature and low integration. In this article, we report on a high-integration carbon nanotubes sensor array with two different electrode separations sensitive to oxynitride mixture at low temperature. The triple-electrode sensor array is comprised of a large carbon nanotube array cathode, two extracting electrodes and two collecting electrodes and worked at non-self-sustaining discharge state, which was determined by the study on current-voltage characteristic of a double-electrode structure sensor. Through studying the relationship between gas concentration and discharge current at 1 atm, we obtained the distinct sensitivities of the binary mixture in the concentration ranges of 0–1166 ppm NO and 0–712 ppm NO2 using the sensor array at low operating voltages and 60 °C. Collecting currents of the two sensors in the array decreased with increasing NO and NO2 concentration in the gas mixture. The repeatable characteristics and dynamical response tests of the sensors were also conducted. The proposed sensor array has potential for the direct detection of a NO-NO2 mixture without separating the mixed gases.