Search

On September 5, 2022, an Ms6.8 earthquake struck Luding County, Kardze Prefecture, Sichuan Province—an area that is particularly vulnerable to geological changes. The earthquake caused considerable damage along the highway, leading to road disruptions and blockages, further isolating earthquake-stricken areas. This mandated investigations on the traffic conditions and road damage. Accordingly, a rapid survey of the main highways in this area was conducted, and 507 damage points were identified. Roadbed damage accounted for >70% of the total damage. Co-seismic disasters were primarily distributed along the highways on both sides of the Dadu River in the reservoir area of the Dagangshan Hydropower Station, Caoke Township, and Detuo Township. Further, six factors under three categories of the spatial distribution of highway damage in the earthquake-stricken areas were analyzed. The rate of highway damage was positively correlated with the seismic intensity but negatively correlated with the fault and river distances. The earthquake intensity had the most significant impact: 37.5% of road disruptions were found in areas with an intensity of IX; this percentage was 1.6 and 5.8 times greater than those found in areas with intensities of VIII and VII, respectively. The roads with the most significant damage were in regions with intensities above VIII, faults within 5 km, slopes within 30°–70°, rivers within 100 m, and the presence of granite. This indicated that these factors aggravated highway disruption, resulting in >90% of damaged highways in strongly shaken regions. Our findings may provide guidance for efficient highway recovery following earthquakes.

Chicken is at risk of contaminated foodborne pathogens in the production process. Timely and nondestructive detection of foodborne pathogens in chicken is essential for food security. The study aims to explore the feasibility of developing efficient classification models for qualitative detection of Staphylococcus aureus in chicken breast using the hyperspectral imaging technique. Principal component analysis was used to process the full spectral information and three wavelength selection methods (competitive adaptive reweighted sampling, genetic algorithm, and successive projections algorithm) were applied to extract effective wavelengths. These methods were combined with the support vector machine algorithm to develop conventional classification models, respectively. In addition, a convolutional neural network model based on deep learning was designed and trained for comparison. The performance of the convolutional neural network model was significantly better than that of conventional classification models. The overall accuracy for chicken sample classifications was improved from 83.88% to 91.38%. The results demonstrated that deep learning can effectively extract spectral features and promote the application of hyperspectral imaging in foodborne pathogens detection of chicken products.

Pure fishmeal (PFM) from whole marine-origin fish is an expensive and indispensable protein ingredient in most aquaculture feeds. In China, the supply shortage of domestically produced PFM has caused frequent PFM adulteration with low-cost protein sources such as feather meal (FTM) and fishmeal from by-products (FBP). The aim of this study was to develop a rapid and nondestructive detection method using near-infrared hyperspectral imaging (NIR-HSI) combined with machine learning algorithms for the identification of PFM adulterated with FTM, FBP, and the binary adulterant (composed of FTM and FBP). A hierarchical modelling strategy was adopted to acquire a better classification accuracy. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) coupled with four spectral preprocessing methods were employed to construct classification models. The SVM with baseline offset (BO-SVM) model using 20 effective wavelengths selected by successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) achieved classification accuracy of 100% and 99.43% for discriminating PFM from the adulterants (FTM, FBP) and adulterated fishmeal (AFM), respectively. This study confirmed that NIR-HSI offered a promising technique for feed mills to identify AFM containing FTM, FBP, or binary adulterants.

To find a convenient and non-destructive way to detect the water stress status of maize plant, hyperspectral technology was used to detect maize leaf water content (LWC) in this paper. Experiments were conducted on maize at the seedling stage in Beijing, China. Firstly, hyperspectral images of 85 maize plant leaves were obtained. To remove redundant information, two methods were used for dimensionality reduction, which are principal component analysis (PCA) and kullback-leibler divergence (KLD) methods, resulting in 10 and 6 wavebands, respectively. These wavebands were then used to establish water stress detection model based on support vector regress (SVR) method. Particle swarm optimization (PSO) algorithm was applied to optimize the parameters for the support vector regression (SVR), including the penalty factor C and kernel function parameter g. Finally, the SVR model with the optimized parameters were trained with 65 pretreatment samples, and the generalization ability of the model was tested with the remaining 20 samples. Experimental results show the model built by bands selected by KLD method (R=0.7684) is better than PCA method(R=0.3513).Future research should aim at testing the model generation for different growing stage of maize.

The failure mechanism is discussed numerically and experimentally in a low-energy-triggered bulk gallium arsenide avalanche semiconductor switch with ultrafast switching. The characteristics of current density distribution along contacts are investigated, and the density at the contact neighboring the active region is found to be higher than that of the average of the structure. In particular, the peak amplitude of the current density by cathode triggering is higher than that by anode triggering. The ultrahigh current density first damages the contacting metal neighboring the active region, and the switch is vulnerable to degrade by cathode triggering. Longevity improvement was achieved experimentally in the anode-triggered switches in contrast with cathode-triggered switches, which validates the deduction of numerical analysis.

Generation of ultra-wideband microwave utilizing a low-energy-triggered bulk gallium arsenide (GaAs) avalanche semiconductor switch with ultrafast switching is presented. The switch biased at 5.2 kV is able to reach the conducting state with a time as low as ~200 ps, and a 5.6-nJ laser is required. The switching transient features such as delay, ultrafast switching, and voltage locking are described and explained by multiple powerful avalanche domains, and a comparison with previous discussions and findings available in the literatures is provided. The application of the bulk GaAs avalanche semiconductor switches for generating ultra-wideband microwave is demonstrated. We produce a bipolar pulse with peak-to-peak voltage amplitude of ~5 kV and a unipolar pulse with peak amplitude of ~10 kV.

Global warming and water resource shortage greatly influence the crop growth and negatively affect the crop yield. Breeding water–stress resistant crop varieties is one of the effective ways to handle this problem. The surface temperatures of the crop are essential for assessing their water stress resistance. Thermal imaging has been widely used to acquire the crop surface temperatures. However, most studies focus on 2D measurement. A 3D thermal imaging system is designed, and a method is developed by using the thermal and RGB-D data to acquire 3D thermal information of maize under water stress conditions. First, thermal and RGB-D cameras were used to collect the thermal and color images, and depth data of maize at the jointing stage and the thermal and color images were processed to extract the edge images of maize. Second, the KAZE feature was selected to register the edge images. Testing results showed that the KAZE feature has better performance than the SURF and BRISK features in the thermal and color image registration of maize. On the basis of the thermal and color image registration, the depth data of maize were assigned with temperature values. Third, the depth data of maize were further processed with denoising, maize extraction, amplification, smoothing, and temperature correction steps to improve the data qualities. Finally, the crop water stress index and canopy–air temperature difference values of each point were calculated. The results demonstrated that the system and the proposed method can effectively detect the water stress characteristics of maize in 3D, which can be combined with the morphological traits of leaves to synthetically analyze the water stress resistance of the crop.

Fusarium head blight (FHB) disease is extensively distributed worldwide. This disease damages grain quality and reduces yield. The detection of this disease in a high throughput way is crucial to planters and breeders. Our study focused on developing a method for processing wheat color images and accurately detecting disease areas using deep learning and image processing techniques. The color images of wheat at the milky stage were collected and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model can detect spikes, and the coefficient of determination of the number of spikes between the manual count and the detection was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results of spike detection, a new color feature was applied to obtain the gray image of each spike. Then, a modified region growing algorithm was implemented to segment and detect the diseased areas of each spike. Results show that the region growing algorithm performs better than K-means and Otsu’s method in segmenting the FHB disease. Overall, this study demonstrates that deep learning techniques enable the accurate detection of FHB in wheat using color images, and the proposed method can effectively detect spikes and diseased areas, thereby improving the efficiency of FHB detection.

HIS color space was proved suitable to image processing in open field.A new method based on linear scanning was designed to detect guidance line.Fuzzy controller was developed for the lateral control of agricultural implement.The guidance system was tested at three different speeds in the corn field. To achieve accurate mechanical inter-rows weeding, an agricultural implement guidance system based on machine vision was designed. The guidance system consists of a color video camera, an industrial panel PC, a lateral displacement controller, a GPS receiver, a hydraulic system, and an agricultural implement. To improve the accuracy and reliability of the guidance system, the choice of color space, the method of guidance line detection, and the method of controlling the implement were investigated. First, considering the adverse effect of illumination variation on image processing, the HIS (hue, saturation, intensity) color model was used to process images, and a threshold algorithm based on the H component was used to produce grayscale images. Second, according to the characteristics of the crop rows in the image, a method of crop line identification based on linear scanning was proposed. To approximate the trend of a crop row in the image to a line, pixels at the bottom and top edges of the image were selected as two endpoints of the line. Candidate lines were created by moving the position of these endpoints. The line with the most target points was regarded as the crop line. Finally, fuzzy control was used to control the agricultural implement. This algorithm can effectively control the agricultural implement tracking the guidance line. Path tracing experiments were conducted at three different speeds of 0.6, 1.0 and 1.4m/s in the corn field on a sunny day. The maximum lateral errors were 4.5cm, 5.5cm and 6.8cm at the three speeds. The average lateral errors were less than 2.7cm for all speeds. The experimental results demonstrated that the guidance system successfully adapted to changes in natural light and had good dynamic performance at all speeds.

Fusarium head blight (FHB) is a devastating disease of wheat worldwide. In addition to reducing the yield of the crop, the causal pathogens also produce mycotoxins that can contaminate the grain. The development of resistant wheat varieties is one of the best ways to reduce the impact of FHB. To develop such varieties, breeders must expose germplasm lines to the pathogen in the field and assess the disease reaction. Phenotyping breeding materials for resistance to FHB is time-consuming, labor-intensive, and expensive when using conventional protocols. To develop a reliable and cost-effective high throughput phenotyping system for assessing FHB in the field, we focused on developing a method for processing color images of wheat spikes to accurately detect diseased areas using deep learning and image processing techniques. Color images of wheat spikes at the milk stage were collected in a shadow condition and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model detected spikes very accurately in the images since the coefficient of determination for the number of spikes tallied by manual count and the model was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results for spike detection, a new color feature was applied to obtain the gray image of each spike and a modified region-growing algorithm was implemented to segment and detect the diseased areas of each spike. Results showed that the region growing algorithm performed better than the K-means and Otsu’s method in segmenting diseased areas. We demonstrated that deep learning techniques enable accurate detection of FHB in wheat based on color image analysis, and the proposed method can effectively detect spikes and diseased areas, which improves the efficiency of the FHB assessment in the field.