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

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International Journal of Agricultural and Biological Engineering 2018年第1期11卷 192-198页

作者： Bairong Li Yan Long huaibo song College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Key Laboratory of Agricultural Internet of Things Ministry of AgricultureYangling 712100ShaanxiChina Key Laboratory of Agricultural Information Perception and Intelligent Services Yangling 712100ShaanxiChina

Green apple targets are difficult to identify for having similar color with backgrounds such as *** primary goal of this study was to detect green apples in natural scenes by applying saliency detection and Gaussian curve fitting ***,the image was represented as a close-loop graph with superpixels as *** nodes were ranked based on the similarity to background and foreground queries to generate the final saliency ***,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 *** 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%,*** results indicated that it was effective and feasible to apply this method to the detection of green apples in nature scenes.

关键词： image processing green apple natural scene machine vision object detection saliency theory Gaussian curve fitting

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

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International Journal of Agricultural and Biological Engineering 2018年第4期11卷 179-185页

作者： Yujuan Chen Dongjian He huaibo song College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Key Laboratory of Agricultural Internet of Things Ministry of AgricultureYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China

Automatic monitoring of cow rumination has great significance in the development of modern animal *** 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 *** 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 *** 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 *** the target scale was estimated based on the confidence ***,accurate tracking of the mouth movement trajectory was *** verify the effectiveness of the proposed method,the performance of the algorithm was tested using 20 video ***,the tracking results were compared with the Mean-shift *** 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 *** results showed that the fast tracking method based on STC learning monitoring algorithm is effective and feasible.

关键词： dairy cow rumination intelligent monitoring STC learning Mean-shift

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

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Artificial Intelligence in Agriculture 2020年第1期4卷 162-171页

作者： Xinyi Zhang Jiexin Liu huaibo song College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYangling 712100ShaanxiChina Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China

Corn ear test is important to modern corn *** 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 *** corn traits are often collected by traditional manual measurement,which is difficult to meet the needs of high throughput corn ear *** this study,image sequences of corn ear samples were captured by building a panoramic photography collecting *** then,to get the lengths and radiuses indexes,the corn area images were processed based on Lab color space and adaptive threshold *** 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 *** results showed that the accuracy of the radiuses and lengths were 93.84%and 94.53%,***,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 *** the accuracy of kernel area and length-width ratio were 95.36%and 97.42%,*** the results showed that the proposed method can be used for corn ear test effectively.

关键词： Corn ear Panoramic photography Image segmentation Image stitching Image rectification

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

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Information Processing in Agriculture 2019年第2期6卷 200-215页

作者： Sashuang Sun huaibo song Dongjian He Yan Long College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China

During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be *** 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 *** enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent *** that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold ***,the poorly segmented images were corrected using the K-means algorithm according to the shape,color and texture *** users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this *** 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,***,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared *** proposed method accurately recognized the green apples under complex illumination conditions and growth ***,it provided effective references for intelligent growth monitoring and yield estimation of fruits.

关键词： Green fruit Adaptive segmentation MSRCR algorithm Mean shift algorithm K-means clustering algorithm Manifold ranking algorithm

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

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International Journal of Agricultural and Biological Engineering 2019年第1期12卷 186-191页

作者： Yanru Mao Dongjian He huaibo song College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Key Laboratory of Agricultural Internet of Things Ministry of AgriculturalYangling 712100ShaanxiChina Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling 712100ShaanxiChina

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 *** flow was used to calculate the relative motion speed of each pixel in the video frame *** 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’*** verify the accuracy of the proposed method,six videos with a total length of 96 min were selected for this *** 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 *** 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.

关键词： ruminant cows mouth area automatic detection machine vision video analysis technology ruminant behavior optical flow

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

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International Journal of Agricultural and Biological Engineering 2018年第2期11卷 170-176页

作者： Xiuyuan Wang Chenghai Yang Jian Zhang huaibo song College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100ShaanxiChina Key Laboratory of Agricultural Internet of Things Ministry of AgricultureYangling 712100ShaanxiChina Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China USDA-ARS Southern Plains Agricultural Research Center 3103 F and B RoadCollege StationTexas 77845USA College of Resource and Environment Huazhong Agricultural UniversityWuhan 430070China

Obtaining clear and true images is a basic requirement for agricultural ***,under the influence of fog,haze and other adverse weather conditions,captured images are usually blurred and distorted,resulting in the difficulty of target *** image dehazing methods based on image enhancement technology can cause the loss of image information and image *** 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 *** 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 *** 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 *** image evaluation indicators including mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were used to evaluate the dehazing *** 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 *** 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.

关键词： agricultural monitoring image dehazing monitoring image dark channel prior(DCP) brightness promoting

Intelligent monitoring method of cow ruminant behavior based on video analysis technology

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International Journal of Agricultural and Biological Engineering 2017年第5期10卷 194-202页

作者： Chen Yujuan He Dongjian Fu Yinxi song huaibo College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Ministry of Agriculture Key Laboratory for Agricultural Internet of Things YanglingShaanxi 712100China Department of Foreign Languages Northwest A&F UniversityYanglingShaanxi 712100China

To overcome the limitations of traditional dairy cow's rumination detection methods,a video-based analysis on the intelligent monitoring method of cow ruminant behavior was proposed in this *** Mean Shift algorithm was used to track the jaw motion of dairy cows *** centroid trajectory curve of the cow mouth motion was subsequently extracted from the *** this way,the monitoring of the ruminant behavior of dairy cows was *** verify the accuracy of the method,six videos,a total of 99'00",24000 frames were *** test results demonstrated that the success rate of this method was 92.03%,despite the interference of behaviors,such as raising or turning of the cow’s *** results demonstrate that this method,which monitors the ruminant behavior of dairy cows,is effective and feasible.

关键词： dairy cow rumination intelligent monitoring video analysis animal bahavior

Using an improved lightweight YOLOv8 model for real-time detection of multi-stage apple fruit in complex orchard environments

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Artificial Intelligence in Agriculture 2024年第1期11卷 70-82页

作者： Baoling Ma Zhixin Hua Yuchen Wen Hongxing Deng Yongjie Zhao Liuru Pu huaibo song College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Key Laboratory of Agricultural Internet of Things Ministry of Agricultureand Rural AffairsYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services YanglingShaanxi 712100China

For the purpose of monitoring apple fruits effectively throughout the entire growth period in smart orchards.A lightweight model named YOLOv8n-ShuffleNetv2-Ghost-SE was *** ShuffleNetv2 basic modules and down-sampling modules were alternately connected,replacing the Backbone of YOLOv8n *** Ghost modules replaced the Conv modules and the C2fGhost modules replaced the C2f modules in the Neck part of the ***2 reduced the memory access cost through channel splitting *** Ghost module combined linear and non-linear convolutions to reduce the network computation *** Wise-IoU(WIoU)replaced the CIoU for calculating the bounding box regression loss,which dynamically adjusted the anchor box quality threshold and gradient gain allocation strategy,optimizing the size and position of predicted bounding *** Squeeze-and-Excitation(SE)was embedded in the Backbone and Neck part of YOLOv8n to enhance the representation ability of feature *** algorithm ensured high precision while having small model size and fast detection speed,which facilitated model migration and *** 9652 images validated the effectiveness of the *** YOLOv8n-ShuffleNetv2-Ghost-SE model achieved Precision of 94.1%,Recall of 82.6%,mean Average Precision of 91.4%,model size of 2.6 MB,parameters of 1.18 M,FLOPs of 3.9 G,and detection speed of 39.37 *** detection speeds on the Jetson Xavier NX development board were 3.17 *** with advanced models including Faster R-CNN,SSD,YOLOv5s,YOLOv7‑tiny,YOLOv8s,YOLOv8n,MobileNetv3_small-Faster,MobileNetv3_small-Ghost,ShuflleNetv2-Faster,ShuflleNetv2-Ghost,ShuflleNetv2-Ghost-CBAM,ShuflleNetv2-Ghost-ECA,and ShuflleNetv2-Ghost-CA demonstrated that the method achieved smaller model and faster detection *** research can provide reference for the development of smart devices in apple orchards.

关键词： Multi-stage apple fruit Deep learning Real-time detection Lightweight model

基于ResNeXt单目深度估计的幼苗植株高度测量方法

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农业工程学报 2022年第3期38卷 155-163页

作者：宋磊李嵘焦义涛宋怀波西北农林科技大学机械与电子工程学院杨凌712100 农业农村部农业物联网重点实验室杨凌712100 陕西省农业信息感知与智能服务重点实验室杨凌712100

幼苗高度是幼苗培育过程中的重要性状,是幼苗生长状况和优良性状筛选的重要参考指标。针对目前研究多选用专业测量工具、使用带有标记的测量手段这一现状,该研究提出了一种基于单目图像深度估计技术的幼苗高度无参测量方法。首先以NYU D... 详细信息

幼苗高度是幼苗培育过程中的重要性状,是幼苗生长状况和优良性状筛选的重要参考指标。针对目前研究多选用专业测量工具、使用带有标记的测量手段这一现状,该研究提出了一种基于单目图像深度估计技术的幼苗高度无参测量方法。首先以NYU Depth Dataset V2深度数据集为基础,以ResNeXt 101网络为深度估计网络主体实现植株图像深度估计。通过深度信息计算出拍摄点到植株的真实距离,结合图像中幼苗植株的像素高度和标定好的视场角实现幼苗高度的测量。为验证该方法的有效性,通过采集不同距离下的番茄幼苗图像1728幅,辣椒幼苗图像160幅,甘蓝幼苗图像160幅进行植株高度测量试验。试验结果表明,在拍摄距离为105cm内番茄幼苗平均绝对误差(Mean Absolute Error,MAE)为0.569 cm,均方根误差(Root Mean Square Error,RMSE)为0.829 cm,平均植株高度比例为1.005。辣椒,甘蓝幼苗的MAE为0.616和0.326cm,RMSE为0.672和0.389cm。每株幼苗高度的平均计算时间为2.01s。试验结果表明该方法具有较好的可行性和普适性。不同光照强度下植株高度测量结果表明,在感光度小于160时,植株高度测试结果的MAE为0.81 cm,仍具有较好的测量准确度。当单幅图像中植株个数处于5以内时,MAE和RMSE的平均值分别为0.652和0.829cm。研究结果表明,该模型可以较准确地从单幅图像中检测出多株植株高度,且在不同距离和一定光照强度变化内均可完成多种幼苗植株高度的精确测量。可为幼苗培育和成长时期判断等研究提供一种无损的植株高度测量方法。

关键词：幼苗植株高度测量单目图像深度估计 ResNeXt