Probabilistic method for the size design of energy piles considering the uncertainty in soil parameters

Energy piles have attracted increasing attention as profitable solutions for the utilization of shallow geothermal energy.Although various complex geotechnical design models of energy piles have been proposed,a simplified sizing method that considers the uncertainty propagation in the soil is required.In this study,a Monte Carlo Simulation-based method was proposed for the size design of energy piles considering the uncertainty of parameters in the soil,such as thermal conductivity and friction angle.The small-sample analysis method of Markov chain Monte Carlo simulation combined with the Bayesian theoretical framework was developed to generate equiv-alent samples.Subsequently,the thermal response function and thermomechanical load transfer methods were employed to address the failure probability of the energy pile in the serviceability limit state and ultimate limit state.In addition,a case study was presented to illustrate the implementation of the proposed probabilistic sizing method.The analysis results of the case study confirm the necessity of modeling the soil uncertainty in the energy pile size design.

Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting

Green apple targets are difficult to identify for having similar color with backgrounds such as leaves.The primary goal of this study was to detect green apples in natural scenes by applying saliency detection and Gaussian curve fitting algorithm.Firstly,the image was represented as a close-loop graph with superpixels as nodes.These nodes were ranked based on the similarity to background and foreground queries to generate the final saliency map.Secondly,Gaussian curve fitting was carried out to fit the V-component in YUV color space in salient areas,and a threshold was selected to binarize the image.To verify the validity of the proposed algorithm,55 images were selected and compared with the common used image segmentation algorithms such as k-means clustering algorithm and FCM(Fuzzy C-means clustering algorithm).Four parameters including recognition ratio,FPR(false positive rate),FNR(false negative rate)and FDR(false detection rate)were used to evaluate the results,which were 91.84%,1.36%,8.16%and 4.22%,respectively.The results indicated that it was effective and feasible to apply this method to the detection of green apples in nature scenes.

Automatic detection of ruminant cows’ mouth area during rumination based on machine vision and video analysis technology

In order to realize the automatic monitoring of ruminant activities of cows,an automatic detection method for the mouth area of ruminant cows based on machine vision technology was studied.Optical flow was used to calculate the relative motion speed of each pixel in the video frame images.The candidate mouth region with large motion ranges was extracted,and a series of processing methods,such as grayscale processing,threshold segmentation,pixel point expansion and adjacent region merging,were carried out to extract the real area of cows’mouth.To verify the accuracy of the proposed method,six videos with a total length of 96 min were selected for this research.The results showed that the highest accuracy was 87.80%,the average accuracy was 76.46%and the average running time of the algorithm was 6.39 s.All the results showed that this method can be used to detect the mouth area automatically,which lays the foundation for automatic monitoring of cows’ruminant behavior.

Automatic monitoring method of cow ruminant behavior based on spatio-temporal context learning

Automatic monitoring of cow rumination has great significance in the development of modern animal husbandry.In order to solve the problem of high real-time requirement of ruminant behavior monitoring,a tracking method based on STC(Spatio-Temporal Context)learning was carried out.On the basis of cow’s mouth region extraction,the spatial context model between target object and its local surrounding background was built based on their spatial correlations by solving the deconvolution problem,and the learned spatial context model was used to update the STC learning model for the next frame.Tracking in the next frame was formulated by computing a confidence map as a convolution problem that integrates the STC learning information,and the best object location could be estimated by maximizing the confidence map.Then the target scale was estimated based on the confidence evaluation.Finally,accurate tracking of the mouth movement trajectory was realized.To verify the effectiveness of the proposed method,the performance of the algorithm was tested using 20 video sequences.Besides,the tracking results were compared with the Mean-shift algorithm.The results showed that the average success rate of STC learning monitoring algorithm was 85.45%,which was 9.45%higher than the Mean-shift algorithm,the detection rate of STC learning monitoring algorithm was 18.56 s per video,which was 22.08%higher than that of the Mean-shift algorithm.The results showed that the fast tracking method based on STC learning monitoring algorithm is effective and feasible.

An adaptive segmentation method combining MSRCR and mean shift algorithm with K-means correction of green apples in natural environment

During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be solved.In this study,the multi-scale Retinex with color restoration(MSRCR)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light conditions.The enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent fruit.After that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold segmentation.Finally,the poorly segmented images were corrected using the K-means algorithm according to the shape,color and texture features.The users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this study.Compared with the manifold ranking algorithm,the K-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,respectively.Additionally,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared algorithms.The proposed method accurately recognized the green apples under complex illumination conditions and growth environments.Additionally,it provided effective references for intelligent growth monitoring and yield estimation of fruits.

Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues

Pesticide residue is an important factor that affects food safety.In order to achieve effective detection of pesticide residues in apples,a machine-vision-based segmentation algorithm and hyperspectral techniques were used to segment the foreground and background regions of the apple image.By calculating the roundness value and extracting the region with the highest roundness value in the connected region,a region of interest(ROI)maskwas created for the apple.Four pesticides(chlorpyrifos,carbendazimand two mixed pesticides)and an inactive control were used at the same concentration of 100 ppm(except for the control group),and the hyperspectral region of the corresponding sample image was extracted by obtaining the different types of pesticide residues in the ROI masks.To increase the diversity of the samples and to expand the dataset,Gaussianwhite noise with a varying signal-to-noise ratio was added to each of the hyperspectral images of the apple.The number of samples was increased from four types of 12 samples to four types of 72 samples,giving 4608 hyperspectral data images in each category.The structure and parameters of a convolutional neural network(CNN)were determined using theoretical analysis and experimental verification.All the extracted hyperspectral images of apples were normalized to 227×227×3 pixels as the input of the CNN network for pesticide residue detection.There were 18,432 sample data of four types for 72 samples.Of these,12,288 images were selected using a bootstrap sampling method as the training set,and 6144 as the test set,with no overlap.The test results showthatwhen the number of training epochswas 10,the accuracy of the test set detectionwas 99.09%,and the detection accuracy of the single-band average imagewas 95.35%.A comparison with traditional k-nearest neighbor(KNN)and support vectormachine classification algorithms showed that the detection accuracy for KNNwas 43.75%and the average time was 0.7645 s.These results demonstrate that our method is a small-sample,noncontact,fast,effective and low-cost technique that can provide effective pesticide residue detection in postharvest apples.

Corn ear test using SIFT-based panoramic photography and machine vision technology

Corn ear test is important to modern corn breeding.The test indexesmainly include lengths,radiuses,rows and numbers of corn ears and the kernels they bear,which can benefit the study on breeding new and fine corn varieties.These corn traits are often collected by traditional manual measurement,which is difficult to meet the needs of high throughput corn ear test.In this study,image sequences of corn ear samples were captured by building a panoramic photography collecting system.And then,to get the lengths and radiuses indexes,the corn area images were processed based on Lab color space and adaptive threshold segmentation.The sequence images were then matched and the panoramic image of a corn surface were extracted using Scale-invariant feature transform(SIFT).Finally,by using Exponential transformation(ETR)and Sobel-Hough algorithm,ears and rows indexes were acquired.Test results showed that the accuracy of the radiuses and lengths were 93.84%and 94.53%,respectively.Meanwhile,the accuracy of kernels and rows indexes were 98.12%and 96.14%,whichwere 4.03%and 7.25%higher than that of common mosaiced panoramic image.And the accuracy of kernel area and length-width ratio were 95.36%and 97.42%,respectively.All the results showed that the proposed method can be used for corn ear test effectively.

Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Obtaining clear and true images is a basic requirement for agricultural monitoring.However,under the influence of fog,haze and other adverse weather conditions,captured images are usually blurred and distorted,resulting in the difficulty of target extraction.Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion.In order to address the above-mentioned problems caused by traditional image dehazing methods,an improved image dehazing method based on dark channel prior(DCP)was proposed.By enhancing the brightness of the hazed image and processing the sky area,the dim and un-natural problems caused by traditional image dehazing algorithms were resolved.Ten different test groups were selected from different weather conditions to verify the effectiveness of the proposed algorithm,and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method.Three image evaluation indicators including mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were used to evaluate the dehazing performance.Results showed that the PSNR and entropy with the proposed method increased by 21.81%and 5.71%,and MSE decreased by 40.07%compared with the original DCP method.It performed much better than the histogram equalization dehazing method with an increase of PSNR by 38.95%and entropy by 2.04%and a decrease of MSE by 84.78%.The results from this study can provide a reference for agricultural field monitoring.