Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting.Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce.This study aims to demonstra...Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting.Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce.This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models,i.e.Multiple Linear Regression(MLR),K-Nearest Neighbor(KNN),and Support Vector Machine(SVM).One-way analysis of variance was applied to reduce RGB,HSV,and L*a*b*features number of hydroponic lettuce images.Image binarization,image mask,and image filling methods were employed to segment hydroponic lettuce from an image for models testing.Results showed that G,H,and a*were selected from RGB,HSV,and L*a*b*for training models.It took about 20.25 s to detect an image with 30244032 pixels by KNN,which was much longer than MLR(0.61 s)and SVM(1.98 s).MLR got detection accuracies of 89.48%and 99.29%for yellow and rotten leaves,respectively,while SVM reached 98.33%and 97.91%,respectively.SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic.Thus,it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.展开更多
As a simple,fast,and non-destructive measuring technology,dielectric spectroscopy is usually used to analyze the dielectric properties of agricultural products and food,and then to predict the main components of mater...As a simple,fast,and non-destructive measuring technology,dielectric spectroscopy is usually used to analyze the dielectric properties of agricultural products and food,and then to predict the main components of materials.However,the large and expensive vector network analyzers(VNA)with expensive analysis software applied in measuring dielectric properties make research limited to the laboratory.To acquire dielectric spectra in situ,a model for solving relative complex permittivity was derived,and its performance was validated.Then,a low-cost portable dielectric spectrometer with a mini VNA,a Raspberry Pi,and a coaxial probe as core parts was developed over the frequency range of 100-3000 MHz.The stability and accuracy of the developed spectrometer were tested using milk and juice.The results indicated that the relative errors of the model were within±5%for dielectric constant(ε′)and loss factor(ε″).The coefficients of variation of measuredε′andε″by the developed spectrometer at 100-3000 MHz were less than 1%and 2%,respectively.Compared with the dielectric properties obtained by using a commercial dielectric measurement system,the relative errors of measuredε′andε″were within±3.4%and±6.0%,respectively.This study makes fast,non-destructive,and on-site food quality detection using dielectric spectra possible.展开更多
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 Gau...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.展开更多
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 cal...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.展开更多
The harvesting of fresh kiwifruit is a labor-intensive operation that accounts for more than 25%of annual production costs.Mechanized harvesting technologies are thus being developed to reduce labor requirements for h...The harvesting of fresh kiwifruit is a labor-intensive operation that accounts for more than 25%of annual production costs.Mechanized harvesting technologies are thus being developed to reduce labor requirements for harvesting kiwifruit.To improve the efficiency of a harvesting robot for picking kiwifruit,we designed an end-effector,which we report herein along with the results of tests to verify its operation.By using the established method of automated picking discussed in the literature and which is based on the characteristics of kiwifruit,we propose an automated method to pick kiwifruit that consists of separating the fruit from its stem on the tree.This method is experimentally verified by using it to pick clustered kiwifruit in a scaffolding canopy cultivation.In the experiment,the end-effector approaches a fruit from below and then envelops and grabs it with two bionic fingers.The fingers are then bent to separate the fruit from its stem.The grabbing,picking,and unloading processes are integrated,with automated picking and unloading performed using a connecting rod linkage following a trajectory model.The trajectory was analyzed and validated by using a simulation implemented in the software Automatic Dynamic Analysis of Mechanical Systems(ADAMS).In addition,a prototype of an end-effector was constructed,and its bionic fingers were equipped with fiber sensors to detect the best position for grabbing the kiwifruit and pressure sensors to ensure that the damage threshold was respected while picking.Tolerances for size and shape were incorporated by following a trajectory groove from grabbing and picking to unloading.The end-effector separates clustered kiwifruit and automatically grabs individual fruits.It takes on average 4–5 s to pick a single fruit,with a successful picking rate of 94.2%in an orchard test featuring 240 samples.This study shows the grabbing–picking–unloading robotic end-effector has significant potential to facilitate the harvesting of kiwifruit.展开更多
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 restorati...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.展开更多
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...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.展开更多
Considering the diversity of soil contents,quality and usability,a systematic scientific study on the elemental and chemical composition(major and minor nutrients elements,trace elements,heavy metals,etc.)of soil is v...Considering the diversity of soil contents,quality and usability,a systematic scientific study on the elemental and chemical composition(major and minor nutrients elements,trace elements,heavy metals,etc.)of soil is very important.Rapid and accurate detection and prevention of soil contamination(mainly in pollutants of heavy metals)is deemed to be a concerned and serious central issue inmodern agriculture and agricultural sustainable development.In order to study the chemical composition of soil,laser induced breakdown spectroscopy(LIBS)has been applied recently.LIBS technology,a kind of atomic emission spectroscopy,is regarded as a future“Superstar”in the field of chemical analysis and green analytical techniques.In this work,the research achievements and trends of soil elements detection based on LIBS technology were reviewed.The structural composition and operating principle of LIBS systemwas briefly introduced.The paper offered a reviewof LIBS applications,including detection and analysis of major element,minor nutrient element and heavy metal element.Simultaneously,LIBS applications to analysis of the soil related materials,plants-related issues(nutrients,pesticide residues,and plants disease)were briefly summarized.The research tendency and developing prospects of LIBS in agriculture were presented at last.展开更多
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,resulti...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.展开更多
To design an automatic harvesting machine for hydroponic lettuce(Lactuca sativa L.),physical and mechanical properties of hydroponic lettuce were investigated and analyzed.Moisture content of stem,root and leaf,geomet...To design an automatic harvesting machine for hydroponic lettuce(Lactuca sativa L.),physical and mechanical properties of hydroponic lettuce were investigated and analyzed.Moisture content of stem,root and leaf,geometric characteristics,pulling force,and root cutting force were studied for harvesting hydroponic lettuce.The pulling force was examined by a tensile experiment,while the root cutting force was investigated by a shear experiment on the electronic universal testing machine.The moisture content of hydroponic lettuce was obtained by direct drying.Experiment data were processed using regression analysis and mathematical statistics method.A regression equation and the law of numerical distribution were obtained.The results showed that the geometric size of different hydroponic lettuce had little difference,and the distribution of physical parameters was concentrated.Moisture content was found statistically similar in stem and root(around 91%),while the highest moisture content was found in the leaf of 95.73%.The root cutting force decrease with the increase of cutting speed and decrease with the cutting position move downward.The minimum average root cutting force in the experiment was 1.41 N.The average pulling force was 13 N.This study provides adequate theoretical support for the design of the automatic harvesting machine of hydroponic lettuce.展开更多
Potato late blight,which is caused by Phytophthorainfestans(Mont.)de Bary,is a worldwide devastating disease for potato.It decreased yields of potato and caused unpredictable losses all over the world.Various simple s...Potato late blight,which is caused by Phytophthorainfestans(Mont.)de Bary,is a worldwide devastating disease for potato.It decreased yields of potato and caused unpredictable losses all over the world.Various simple statistical methods and forecasting models have been developed to predict and manage potato late blight.Meanwhile,there is a rising need to develop prediction models reflecting peroxidase(POD)activity,which is an important health index that varies with infection and correlated with stress resistance in plants.Thus,the aim of this research was to develop kinetic models to predict POD activity.Infection-induced changes in potato leaves stored in an artificial climate chest at 25°C were analyzed using hyperspectroscopy.Four prediction models were developed by using linear partial least squares(PLS)and nonlinear support vector machine(SVM)methods based on the full spectrum and effective wavelengths.The effective wavelengths were selected by the successive projection algorithm(SPA).In this study,the prediction model developed by means of SPA-SVM method obtained the best performance,with a Rp(correlation coefficient of prediction)value of 0.923 and a RMSEp(root mean square error of prediction)value of 24.326.Five-order kinetics models according to the prediction model were developed,and late blight disease can be predicted using this model.This study provided a theoretical basis for the prediction of latencies of late blight.展开更多
The identification of Chinese medicinal plants was conducted to rely on ampelographic manual assessment by experts.More recently,machine learning algorithms for pattern recognition have been successfully applied to le...The identification of Chinese medicinal plants was conducted to rely on ampelographic manual assessment by experts.More recently,machine learning algorithms for pattern recognition have been successfully applied to leaf recognition in other plant species.These new tools make the classification of Chinese medicinal plants easier,more efficient and cost effective.This study showed comparative results between machine learning models obtained from two methods:i)a morpho-colorimetric method and ii)a visible(VIS)/Near Infrared(NIR)spectral analysis from sampled leaves of 20 different Chinese medicinal plants.Specifically,the automated image analysis and VIS/NIR spectral based parameters obtained from leaves were used separately as inputs to construct customized artificial neural network(ANN)models.Results showed that the ANN model developed using the morpho-colorimetric parameters as inputs(Model A)had an accuracy of 98.3%in the classification of leaves for the 20 medicinal plants studied.In the case of the model based on spectral data from leaves(Model B),the ANN model obtained using the averaged VIS/NIR spectra per leaf as inputs showed 92.5%accuracy for the classification of all medicinal plants used.Model A has the advantage of being cost effective,requiring only a normal document scanner as measuring instrument.This method can be adapted for non-destructive assessment of leaves in-situ by using portable wireless scanners.Model B combines the fast,non-destructive advantages of VIS/NIR spectroscopy,which can be used for rapid and non-invasive identification of Chinese medicinal plants and other applications by analyzing specific light spectra overtones from leaves to assess concentration of pigments such as chlorophyll,anthocyanins and others that are related active compounds from the medicinal plants.展开更多
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...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.展开更多
Information about canopy vigor and growth are critical to assess the potential impacts of biotic or abiotic stresses on plant development.By implementing a Digital Surface Model(DSM)to imagery obtained using Unmanned ...Information about canopy vigor and growth are critical to assess the potential impacts of biotic or abiotic stresses on plant development.By implementing a Digital Surface Model(DSM)to imagery obtained using Unmanned Aerial Vehicles(UAV),it is possible to filter canopy information effectively based on height,which provides an efficient method to discriminate canopy from soil and lower vegetation such as weeds or cover crops.This paper describes a method based on the DSM to assess canopy growth(CG)as well as missing plants from a kiwifruit orchard on a plant-by-plant scale.The DSM was initially extracted from the overlapping RGB aerial imagery acquired over the kiwifruit orchard using the Structure from Motion(SfM)algorithm.An adaptive threshold algorithm was implemented using the height difference between soil/lower plants and kiwifruit canopies to identify plants and extract canopy information on a non-regular surface.Furthermore,a customized algorithm was developed to discriminate single kiwifruit plants automatically,which allowed the estimation of individual canopy cover fractions(fc).By applying differential fc thresholding,four categories of the CG were determined automatically:(i)missing plants;(ii)low vigor;(iii)moderate vigor;and(iv)vigorous.Results were validated by a detailed visual inspection on the ground,which rendered an overall accuracy of 89.5%for the method proposed to assess CG at the plant-by-plant level.Specifically,the accuracies for CG category(i)-(iv)were 94.1%,85.1%,86.7%,and 88.0%,respectively.The proposed method showed also to be appropriate to filter out weeds and other smaller non-plant materials which are extremely difficult to be distinguished by common colour thresholding or edge identification methods.展开更多
It is important for intelligent orchards to be able to achieve automatic monitoring of fruit growth information within a natural growing environment.The issue of how to track green and oscillating fruits under the inf...It is important for intelligent orchards to be able to achieve automatic monitoring of fruit growth information within a natural growing environment.The issue of how to track green and oscillating fruits under the influence of wind and farming operations is a key aspect of monitoring of the growth state of the fruit.In order to realize the accurate tracking of green fruit targets,a new method based on target tracking is proposed.First,an optical flow method is applied to realize the automatic detection of green fruit targets,and this lays the foundation for the accurate and automatic tracking of these targets.Then,Kalman and kernelized correlation filter(KCF)algorithms are applied to achieve multi-target tracking and prediction.In order to verify the performance of these different algorithms on various types of green fruit targets,experiments were carried out based on nine video sequences.The experimental results for the tracking of single,double and triple green fruit targets show that the average tracking success rates of the Kalman algorithm are 88.15%,82.30%and 53.10%,respectively,and those of the KCF algorithm are 94.07%,87.35%and 61.46%,respectively,meaning that the average tracking results from KCF are 5.92%,5.05%and 8.36%higher than those from the Kalman algorithm.The time consumed is also reduced by 35.40%,36.27%and 40.86%,respectively.The results show that it is feasible to apply the KCF algorithm to the tracking of green fruit targets.展开更多
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 vari...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.展开更多
To investigate the optimal parameters combination of reciprocating cutter for harvesting hydroponic lettuce automatically,a shear fixture was designed for cutting lettuce stems on a universal materials tester.Effects ...To investigate the optimal parameters combination of reciprocating cutter for harvesting hydroponic lettuce automatically,a shear fixture was designed for cutting lettuce stems on a universal materials tester.Effects of blade distance,sliding cutting angle,skew cutting angle,and shearing angle on shearing stress were investigated in this study.The orders of the significance of a single factor and double factors were analyzed using the response surface methodology(RSM).A scanning electron microscope was used to observe the microstructure of the lettuce stem to analyze the shearing characteristics at the microscopic level.The RSM results showed that the order of significance for single factors was(i)sliding cutting angle,(ii)shearing angle,(iii)skew cutting angle,and(iv)blade distance.The sliding cutting angle had a highly significant influence on the shearing stress.The order of significance for double factors was(i)blade distance and shearing angle,(ii)sliding cutting angle and skew cutting angle,and(iii)the sliding cutting angle and shearing angle.A quadratic model of the factors and shearing stress was built according to the response-surface results.The optimized combination of factors that gives the minimum shearing stress was observed that it reduced 69.9%of the maximum shearing stress value.This research can provide a reference for designing lettuce-cutting devices.展开更多
With the continuous expansion of wine grape planting areas,the mechanization and intelligence of grape harvesting have gradually become the future development trend.In order to guide the picking robot to pick grapes m...With the continuous expansion of wine grape planting areas,the mechanization and intelligence of grape harvesting have gradually become the future development trend.In order to guide the picking robot to pick grapes more efficiently in the vineyard,this study proposed a grape bunches segmentation method based on Pyramid Scene Parsing Network(PSPNet)deep semantic segmentation network for different varieties of grapes in the natural field environments.To this end,the Convolutional Block Attention Module(CBAM)attention mechanism and the atrous convolution were first embedded in the backbone feature extraction network of the PSPNet model to improve the feature extraction capability.Meanwhile,the proposed model also improved the PSPNet semantic segmentation model by fusing multiple feature layers(with more contextual information)extracted by the backbone network.The improved PSPNet was compared against the original PSPNet on a newly collected grape image dataset,and it was shown that the improved PSPNet model had an Intersection-over-Union(IoU)and Pixel Accuracy(PA)of 87.42%and 95.73%,respectively,implying an improvement of 4.36%and 9.95%over the original PSPNet model.The improved PSPNet was also compared against the state-of-the-art DeepLab-V3+and U-Net in terms of IoU,PA,computation efficiency and robustness,and showed promising performance.It is concluded that the improved PSPNet can quickly and accurately segment grape bunches of different varieties in the natural field environments,which provides a certain technical basis for intelligent harvesting by grape picking robots.展开更多
Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest season.Accurate detection and localization of target fruit is necessary for robotic app...Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest season.Accurate detection and localization of target fruit is necessary for robotic apple picking.Detection accuracy has a great influence on localization results.Although current researches on detection and localization of apples using traditional image algorithms can obtain good results under laboratory conditions,it is difficult to accurately detect and locate objects in natural field with complex environments.With the rapid development of artificial intelligence,accuracy of apple detection based on deep learning has been significantly improved.Therefore,a deep learningbased method was developed to accurately detect and locate the position of fruit.For different localization methods,binocular localization is a widely used localization method for its bionic principle and lower equipment cost.Hence,this paper proposed an improved binocular localization method for apple based on fruit detection using deep learning.First,apples of binocular images were detected by Faster R-CNN.After that,a segmentation based on chromatic aberration and chromatic aberration ratio was applied to segment apple and background pixels in bounding box of detected fruit.Furthermore,template matching with parallel polar line constraint was used to match apples in left and right images.Finally,two feature points on apples were selected to directly calculate three dimensional coordinates of feature points with the binocular localization principle.In this study,Faster R-CNN achieved an AP of 88.12%with an average detection speed of 0.32 s for an image.Meanwhile,standard deviation and localization precision of depth of two feature points on each apple were calculated to evaluate localization.Results showed that the average standard deviation and the average localization precision of 76 groups of datasets were 0.51 cm and 99.64%,respectively.Results indicated that the proposed improved binocular localization method is promising for fruit localization。展开更多
The success of organic and green agricultural fruit production depends on quality and cost.As the kiwifruit industry becomes ever more commercialized,it is in the interests of the industry to mechanize production,whic...The success of organic and green agricultural fruit production depends on quality and cost.As the kiwifruit industry becomes ever more commercialized,it is in the interests of the industry to mechanize production,which can promote industrialization and improve industrial value and market prospects.Currently,New Zealand,Italy,Chile,and China carry out research into the mechanism of kiwifruit production.This review describes in detail the current state of the art of pollination,harvesting and grading equipment,including detection and identification,non-destructive end effector,harvesting robots and grading devices.Process technologies that include artificial pollination,harvest mechanization,grading and standardization of production problems are analysed and compared.These problems directly affect the quality of kiwifruit products.Finally,to solve the various problems that the kiwifruit industry experiences,it is necessary to accelerate the development of mechanized kiwifruit production,realize the mechanization of information acquisition and standardization in order to advance precision agriculture and agricultural wisdom for the future.Mechanization of the kiwifruit industry must adapt to adjustments in how China’s economic structure develops.展开更多
基金the Science and Technology Program in Yulin City of China(CXY-2020-076,CXY-2019-129)Youth Science and Technology Nova Program in Shaanxi Province of China(2021KJXX-94)+1 种基金Key Research and Development Program of Shaanxi(2021NY-135)Recruitment Program of High-End Foreign Experts of the State Administration of Foreign Experts Affairs,Ministry of Science and Technology,China(G20200027075)。
文摘Accurate and fast detection of abnormal hydroponic lettuce leaves is primary technology for robotic sorting.Yellow and rotten leaves are main types of abnormal leaves in hydroponic lettuce.This study aims to demonstrate a feasibility of detecting yellow and rotten leaves of hydroponic lettuce by machine learning models,i.e.Multiple Linear Regression(MLR),K-Nearest Neighbor(KNN),and Support Vector Machine(SVM).One-way analysis of variance was applied to reduce RGB,HSV,and L*a*b*features number of hydroponic lettuce images.Image binarization,image mask,and image filling methods were employed to segment hydroponic lettuce from an image for models testing.Results showed that G,H,and a*were selected from RGB,HSV,and L*a*b*for training models.It took about 20.25 s to detect an image with 30244032 pixels by KNN,which was much longer than MLR(0.61 s)and SVM(1.98 s).MLR got detection accuracies of 89.48%and 99.29%for yellow and rotten leaves,respectively,while SVM reached 98.33%and 97.91%,respectively.SVM was more robust than MLR in detecting yellow and rotten leaves of hydroponic.Thus,it was possible for abnormal hydroponic lettuce leaves detection by machine learning methods.
基金financial support provided by the National Natural Science Foundation of China(Grant No.32172308)Startup Foundation for Doctors of Yan'an University(No.YDBK2022-79).
文摘As a simple,fast,and non-destructive measuring technology,dielectric spectroscopy is usually used to analyze the dielectric properties of agricultural products and food,and then to predict the main components of materials.However,the large and expensive vector network analyzers(VNA)with expensive analysis software applied in measuring dielectric properties make research limited to the laboratory.To acquire dielectric spectra in situ,a model for solving relative complex permittivity was derived,and its performance was validated.Then,a low-cost portable dielectric spectrometer with a mini VNA,a Raspberry Pi,and a coaxial probe as core parts was developed over the frequency range of 100-3000 MHz.The stability and accuracy of the developed spectrometer were tested using milk and juice.The results indicated that the relative errors of the model were within±5%for dielectric constant(ε′)and loss factor(ε″).The coefficients of variation of measuredε′andε″by the developed spectrometer at 100-3000 MHz were less than 1%and 2%,respectively.Compared with the dielectric properties obtained by using a commercial dielectric measurement system,the relative errors of measuredε′andε″were within±3.4%and±6.0%,respectively.This study makes fast,non-destructive,and on-site food quality detection using dielectric spectra possible.
基金This study was supported by the National High Technology Research and Development Program of China(“863”Program)(No.2013AA10230402)Agricultural science and technology project of Shaanxi Province(No.2016NY-157)Fundamental Research Funds Central Universities(2452016077).
文摘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.
基金This work was supported by the National Key Research and Development Program of China(2017YFD0701603)Natural Science Foundation of China(61473235).
文摘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.
基金This research was conducted in the College of Mechanical and Electronic Engineering,Northwest A&F University,and was supported by research grants from the General Program of the National Natural Science Foundation of China(61175099).
文摘The harvesting of fresh kiwifruit is a labor-intensive operation that accounts for more than 25%of annual production costs.Mechanized harvesting technologies are thus being developed to reduce labor requirements for harvesting kiwifruit.To improve the efficiency of a harvesting robot for picking kiwifruit,we designed an end-effector,which we report herein along with the results of tests to verify its operation.By using the established method of automated picking discussed in the literature and which is based on the characteristics of kiwifruit,we propose an automated method to pick kiwifruit that consists of separating the fruit from its stem on the tree.This method is experimentally verified by using it to pick clustered kiwifruit in a scaffolding canopy cultivation.In the experiment,the end-effector approaches a fruit from below and then envelops and grabs it with two bionic fingers.The fingers are then bent to separate the fruit from its stem.The grabbing,picking,and unloading processes are integrated,with automated picking and unloading performed using a connecting rod linkage following a trajectory model.The trajectory was analyzed and validated by using a simulation implemented in the software Automatic Dynamic Analysis of Mechanical Systems(ADAMS).In addition,a prototype of an end-effector was constructed,and its bionic fingers were equipped with fiber sensors to detect the best position for grabbing the kiwifruit and pressure sensors to ensure that the damage threshold was respected while picking.Tolerances for size and shape were incorporated by following a trajectory groove from grabbing and picking to unloading.The end-effector separates clustered kiwifruit and automatically grabs individual fruits.It takes on average 4–5 s to pick a single fruit,with a successful picking rate of 94.2%in an orchard test featuring 240 samples.This study shows the grabbing–picking–unloading robotic end-effector has significant potential to facilitate the harvesting of kiwifruit.
基金This work was supported by the National High Technology Research and Development Program of China(863 Program)[Grant number 2013AA10230402]Agricultural Science and Technology Project of Shaanxi Province[Grant number 2016NY-157]Fundamental Research Funds of Central Universities[Grant number 2452016077].The authors appreciate the above funding organizations for their financial supports.The authors would also like to thank the helpful comments and suggestions provided by all the authors cited in this article and the anonymous reviewers.
文摘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.
基金This work was supported by the National Natural Science Foundation of China(Grant No.31501228)the Yangling Demonstration Zone Science and Technology Plan Project(Grant No.2016NY-31).
文摘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.
基金This work was supported by the National Natural Science Foundation of China(Program No:61705188)China Postdoctoral Science Foundation(2017M613218)+2 种基金Shaanxi Province Postdoctoral Science Foundation(2017BSHYDZZ61)the Fundamental Research Funds for the Central Universities(2452017125)the Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs of the People's Republic of China.
文摘Considering the diversity of soil contents,quality and usability,a systematic scientific study on the elemental and chemical composition(major and minor nutrients elements,trace elements,heavy metals,etc.)of soil is very important.Rapid and accurate detection and prevention of soil contamination(mainly in pollutants of heavy metals)is deemed to be a concerned and serious central issue inmodern agriculture and agricultural sustainable development.In order to study the chemical composition of soil,laser induced breakdown spectroscopy(LIBS)has been applied recently.LIBS technology,a kind of atomic emission spectroscopy,is regarded as a future“Superstar”in the field of chemical analysis and green analytical techniques.In this work,the research achievements and trends of soil elements detection based on LIBS technology were reviewed.The structural composition and operating principle of LIBS systemwas briefly introduced.The paper offered a reviewof LIBS applications,including detection and analysis of major element,minor nutrient element and heavy metal element.Simultaneously,LIBS applications to analysis of the soil related materials,plants-related issues(nutrients,pesticide residues,and plants disease)were briefly summarized.The research tendency and developing prospects of LIBS in agriculture were presented at last.
基金supported by the National High Technology Research and Development Program of China(863 Program)(No.2013AA10230402)Agricultural Science and Technology Project of Shaanxi Province(No.2016NY-157)Fundamental Research Funds Central Universities(2452016077).
文摘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.
基金supported by the Key Research and Development Program in Shaanxi Province of China[grant number 2018TSCXL-NY-05-04,2019ZDLNY02-04].
文摘To design an automatic harvesting machine for hydroponic lettuce(Lactuca sativa L.),physical and mechanical properties of hydroponic lettuce were investigated and analyzed.Moisture content of stem,root and leaf,geometric characteristics,pulling force,and root cutting force were studied for harvesting hydroponic lettuce.The pulling force was examined by a tensile experiment,while the root cutting force was investigated by a shear experiment on the electronic universal testing machine.The moisture content of hydroponic lettuce was obtained by direct drying.Experiment data were processed using regression analysis and mathematical statistics method.A regression equation and the law of numerical distribution were obtained.The results showed that the geometric size of different hydroponic lettuce had little difference,and the distribution of physical parameters was concentrated.Moisture content was found statistically similar in stem and root(around 91%),while the highest moisture content was found in the leaf of 95.73%.The root cutting force decrease with the increase of cutting speed and decrease with the cutting position move downward.The minimum average root cutting force in the experiment was 1.41 N.The average pulling force was 13 N.This study provides adequate theoretical support for the design of the automatic harvesting machine of hydroponic lettuce.
基金This research was supported by the Natural Science Foundation of China(31671965)the project of Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture,China(2017001).
文摘Potato late blight,which is caused by Phytophthorainfestans(Mont.)de Bary,is a worldwide devastating disease for potato.It decreased yields of potato and caused unpredictable losses all over the world.Various simple statistical methods and forecasting models have been developed to predict and manage potato late blight.Meanwhile,there is a rising need to develop prediction models reflecting peroxidase(POD)activity,which is an important health index that varies with infection and correlated with stress resistance in plants.Thus,the aim of this research was to develop kinetic models to predict POD activity.Infection-induced changes in potato leaves stored in an artificial climate chest at 25°C were analyzed using hyperspectroscopy.Four prediction models were developed by using linear partial least squares(PLS)and nonlinear support vector machine(SVM)methods based on the full spectrum and effective wavelengths.The effective wavelengths were selected by the successive projection algorithm(SPA).In this study,the prediction model developed by means of SPA-SVM method obtained the best performance,with a Rp(correlation coefficient of prediction)value of 0.923 and a RMSEp(root mean square error of prediction)value of 24.326.Five-order kinetics models according to the prediction model were developed,and late blight disease can be predicted using this model.This study provided a theoretical basis for the prediction of latencies of late blight.
基金This study was supported by the National Key Research and Development Program of China(No.2017YFD0700402)the Key Science and Technology Program of Shaanxi Province,China(No.S2016YFNY0066)+1 种基金the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry,Tibet Natural Science Foundation-The Study of Tibet Crop Condition Monitoring Based on Crop Growth Model and Multi-Source Remote Sensing Data(2016-ZR-15-18)Part of this research was supported by the Digital Viticulture program funded by the University of Melbourne’s Networked Society Institute,Australia.
文摘The identification of Chinese medicinal plants was conducted to rely on ampelographic manual assessment by experts.More recently,machine learning algorithms for pattern recognition have been successfully applied to leaf recognition in other plant species.These new tools make the classification of Chinese medicinal plants easier,more efficient and cost effective.This study showed comparative results between machine learning models obtained from two methods:i)a morpho-colorimetric method and ii)a visible(VIS)/Near Infrared(NIR)spectral analysis from sampled leaves of 20 different Chinese medicinal plants.Specifically,the automated image analysis and VIS/NIR spectral based parameters obtained from leaves were used separately as inputs to construct customized artificial neural network(ANN)models.Results showed that the ANN model developed using the morpho-colorimetric parameters as inputs(Model A)had an accuracy of 98.3%in the classification of leaves for the 20 medicinal plants studied.In the case of the model based on spectral data from leaves(Model B),the ANN model obtained using the averaged VIS/NIR spectra per leaf as inputs showed 92.5%accuracy for the classification of all medicinal plants used.Model A has the advantage of being cost effective,requiring only a normal document scanner as measuring instrument.This method can be adapted for non-destructive assessment of leaves in-situ by using portable wireless scanners.Model B combines the fast,non-destructive advantages of VIS/NIR spectroscopy,which can be used for rapid and non-invasive identification of Chinese medicinal plants and other applications by analyzing specific light spectra overtones from leaves to assess concentration of pigments such as chlorophyll,anthocyanins and others that are related active compounds from the medicinal plants.
基金This work was financially supported by the National Key Technology R&D Program of China(No.2017YFD0701603)the Natural Science Foundation of China(No.61473235).
文摘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.
基金This study was supported by the National Key Research and Development Program of China(No.2017YFD0700402)the Key Science and Technology Program of Shaanxi Province,China(No.S2016YFNY0066)+1 种基金the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education MinistryPart of this research was supported by the Digital Viticulture program funded by the University of Melbourne’s Networked Society Institute,Australia.
文摘Information about canopy vigor and growth are critical to assess the potential impacts of biotic or abiotic stresses on plant development.By implementing a Digital Surface Model(DSM)to imagery obtained using Unmanned Aerial Vehicles(UAV),it is possible to filter canopy information effectively based on height,which provides an efficient method to discriminate canopy from soil and lower vegetation such as weeds or cover crops.This paper describes a method based on the DSM to assess canopy growth(CG)as well as missing plants from a kiwifruit orchard on a plant-by-plant scale.The DSM was initially extracted from the overlapping RGB aerial imagery acquired over the kiwifruit orchard using the Structure from Motion(SfM)algorithm.An adaptive threshold algorithm was implemented using the height difference between soil/lower plants and kiwifruit canopies to identify plants and extract canopy information on a non-regular surface.Furthermore,a customized algorithm was developed to discriminate single kiwifruit plants automatically,which allowed the estimation of individual canopy cover fractions(fc).By applying differential fc thresholding,four categories of the CG were determined automatically:(i)missing plants;(ii)low vigor;(iii)moderate vigor;and(iv)vigorous.Results were validated by a detailed visual inspection on the ground,which rendered an overall accuracy of 89.5%for the method proposed to assess CG at the plant-by-plant level.Specifically,the accuracies for CG category(i)-(iv)were 94.1%,85.1%,86.7%,and 88.0%,respectively.The proposed method showed also to be appropriate to filter out weeds and other smaller non-plant materials which are extremely difficult to be distinguished by common colour thresholding or edge identification methods.
基金Supported by the National Key R&D Program of China(Grant No.SQ2019YFD100072)Supported by the National High Technology Research and Development Program of China(863 Program)(No.2013AA10230402)Shaanxi Province Natural Science Foundation(No.2014JQ3094).
文摘It is important for intelligent orchards to be able to achieve automatic monitoring of fruit growth information within a natural growing environment.The issue of how to track green and oscillating fruits under the influence of wind and farming operations is a key aspect of monitoring of the growth state of the fruit.In order to realize the accurate tracking of green fruit targets,a new method based on target tracking is proposed.First,an optical flow method is applied to realize the automatic detection of green fruit targets,and this lays the foundation for the accurate and automatic tracking of these targets.Then,Kalman and kernelized correlation filter(KCF)algorithms are applied to achieve multi-target tracking and prediction.In order to verify the performance of these different algorithms on various types of green fruit targets,experiments were carried out based on nine video sequences.The experimental results for the tracking of single,double and triple green fruit targets show that the average tracking success rates of the Kalman algorithm are 88.15%,82.30%and 53.10%,respectively,and those of the KCF algorithm are 94.07%,87.35%and 61.46%,respectively,meaning that the average tracking results from KCF are 5.92%,5.05%and 8.36%higher than those from the Kalman algorithm.The time consumed is also reduced by 35.40%,36.27%and 40.86%,respectively.The results show that it is feasible to apply the KCF algorithm to the tracking of green fruit targets.
基金Thisworkwas supported by theNational Key Research and Development Program of China(2019YFD1002401)the National High Technology Research and Development Program of China(863 Program)(No.2013AA10230402).
文摘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.
基金This research was supported by the Key Research and Development Program in Shaanxi Province of China(Grant No.2018TSCXL-NY-05-04,2019ZDLNY02-04)Science and Technology Program in Yulin City of China(Grant No.CXY-2020-076).
文摘To investigate the optimal parameters combination of reciprocating cutter for harvesting hydroponic lettuce automatically,a shear fixture was designed for cutting lettuce stems on a universal materials tester.Effects of blade distance,sliding cutting angle,skew cutting angle,and shearing angle on shearing stress were investigated in this study.The orders of the significance of a single factor and double factors were analyzed using the response surface methodology(RSM).A scanning electron microscope was used to observe the microstructure of the lettuce stem to analyze the shearing characteristics at the microscopic level.The RSM results showed that the order of significance for single factors was(i)sliding cutting angle,(ii)shearing angle,(iii)skew cutting angle,and(iv)blade distance.The sliding cutting angle had a highly significant influence on the shearing stress.The order of significance for double factors was(i)blade distance and shearing angle,(ii)sliding cutting angle and skew cutting angle,and(iii)the sliding cutting angle and shearing angle.A quadratic model of the factors and shearing stress was built according to the response-surface results.The optimized combination of factors that gives the minimum shearing stress was observed that it reduced 69.9%of the maximum shearing stress value.This research can provide a reference for designing lettuce-cutting devices.
基金supported by the Key R&D Project of Ningxia Hui Autonomous Region(Grant No.2019BBF02013)Guangxi Key R&D Program Project(Grant No.Gui Ke AB21076001).
文摘With the continuous expansion of wine grape planting areas,the mechanization and intelligence of grape harvesting have gradually become the future development trend.In order to guide the picking robot to pick grapes more efficiently in the vineyard,this study proposed a grape bunches segmentation method based on Pyramid Scene Parsing Network(PSPNet)deep semantic segmentation network for different varieties of grapes in the natural field environments.To this end,the Convolutional Block Attention Module(CBAM)attention mechanism and the atrous convolution were first embedded in the backbone feature extraction network of the PSPNet model to improve the feature extraction capability.Meanwhile,the proposed model also improved the PSPNet semantic segmentation model by fusing multiple feature layers(with more contextual information)extracted by the backbone network.The improved PSPNet was compared against the original PSPNet on a newly collected grape image dataset,and it was shown that the improved PSPNet model had an Intersection-over-Union(IoU)and Pixel Accuracy(PA)of 87.42%and 95.73%,respectively,implying an improvement of 4.36%and 9.95%over the original PSPNet model.The improved PSPNet was also compared against the state-of-the-art DeepLab-V3+and U-Net in terms of IoU,PA,computation efficiency and robustness,and showed promising performance.It is concluded that the improved PSPNet can quickly and accurately segment grape bunches of different varieties in the natural field environments,which provides a certain technical basis for intelligent harvesting by grape picking robots.
基金the National Natural Science of China(32171897)Youth Science and Technology Nova Program in Shaanxi Province of China(2021KJXX-94)+1 种基金Science and Technology Promotion Program of Northwest A&F University(TGZX2021-29)Recruitment Program of High-End Foreign Experts of the State Administration of Foreign Experts Affairs,Ministry of Science and Technology,China(G20200027075).
文摘Apple picking robot is now being developed as an alternative to hand picking due to a great demand for labor during apple harvest season.Accurate detection and localization of target fruit is necessary for robotic apple picking.Detection accuracy has a great influence on localization results.Although current researches on detection and localization of apples using traditional image algorithms can obtain good results under laboratory conditions,it is difficult to accurately detect and locate objects in natural field with complex environments.With the rapid development of artificial intelligence,accuracy of apple detection based on deep learning has been significantly improved.Therefore,a deep learningbased method was developed to accurately detect and locate the position of fruit.For different localization methods,binocular localization is a widely used localization method for its bionic principle and lower equipment cost.Hence,this paper proposed an improved binocular localization method for apple based on fruit detection using deep learning.First,apples of binocular images were detected by Faster R-CNN.After that,a segmentation based on chromatic aberration and chromatic aberration ratio was applied to segment apple and background pixels in bounding box of detected fruit.Furthermore,template matching with parallel polar line constraint was used to match apples in left and right images.Finally,two feature points on apples were selected to directly calculate three dimensional coordinates of feature points with the binocular localization principle.In this study,Faster R-CNN achieved an AP of 88.12%with an average detection speed of 0.32 s for an image.Meanwhile,standard deviation and localization precision of depth of two feature points on each apple were calculated to evaluate localization.Results showed that the average standard deviation and the average localization precision of 76 groups of datasets were 0.51 cm and 99.64%,respectively.Results indicated that the proposed improved binocular localization method is promising for fruit localization。
基金supported by research grants from the General Program of the National Natural Science Foundation of China(61175099).
文摘The success of organic and green agricultural fruit production depends on quality and cost.As the kiwifruit industry becomes ever more commercialized,it is in the interests of the industry to mechanize production,which can promote industrialization and improve industrial value and market prospects.Currently,New Zealand,Italy,Chile,and China carry out research into the mechanism of kiwifruit production.This review describes in detail the current state of the art of pollination,harvesting and grading equipment,including detection and identification,non-destructive end effector,harvesting robots and grading devices.Process technologies that include artificial pollination,harvest mechanization,grading and standardization of production problems are analysed and compared.These problems directly affect the quality of kiwifruit products.Finally,to solve the various problems that the kiwifruit industry experiences,it is necessary to accelerate the development of mechanized kiwifruit production,realize the mechanization of information acquisition and standardization in order to advance precision agriculture and agricultural wisdom for the future.Mechanization of the kiwifruit industry must adapt to adjustments in how China’s economic structure develops.