Internet of Things (IoT) has emerged as one of the new use cases in the 5th Generation wireless networks. However, the transient nature of the data generated in IoT networks brings great challenges for content caching...Internet of Things (IoT) has emerged as one of the new use cases in the 5th Generation wireless networks. However, the transient nature of the data generated in IoT networks brings great challenges for content caching. In this paper, we study a joint content caching and updating strategy in IoT networks, taking both the energy consumption of the sensors and the freshness loss of the contents into account. In particular, we decide whether or not to cache the transient data and, if so, how often the servers should update their contents. We formulate this content caching and updating problem as a mixed 0–1 integer non-convex optimization programming, and devise a Harmony Search based content Caching and Updating (HSCU) algorithm, which is self-learning and derivativefree and hence stipulates no requirement on the relationship between the objective and variables. Finally, extensive simulation results verify the effectiveness of our proposed algorithm in terms of the achieved satisfaction ratio for content delivery, normalized energy consumption, and overall network utility, by comparing it with some baseline algorithms.展开更多
[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the cha...[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.展开更多
Estimating spatial variation in crop transpiration coefficients(CTc) and aboveground biomass(AGB)rapidly and accurately by remote sensing can facilitate precision irrigation management in semiarid regions. This study ...Estimating spatial variation in crop transpiration coefficients(CTc) and aboveground biomass(AGB)rapidly and accurately by remote sensing can facilitate precision irrigation management in semiarid regions. This study developed and assessed a novel machine learning(ML) method for estimating CTc and AGB using time-series unmanned aerial vehicle(UAV)-based multispectral vegetation indices(VIs)of maize under several irrigation treatments at the field scale. Four ML regression methods: multiple linear regression(MLR), support vector regression(SVR), random forest regression(RFR), and adaptive boosting regression(ABR), were used to address the complex relationship between CTcand VIs. AGB was then estimated using exponential, logistic, sigmoid, and linear equations because of their clear mathematical formulations based on the optimal CTcestimation model. The UAV VIs-derived CTcusing the RFR estimation model yielded the highest accuracy(R^(2)= 0.91, RMSE = 0.0526, and n RMSE = 9.07%). The normalized difference red-edge index, transformed chlorophyll absorption in reflectance index, and simple ratio contributed significantly to the RFR-based CTcmodel. The accuracy of AGB estimation using nonlinear methods was higher than that using the linear method. The exponential method yielded the highest accuracy(R^(2)= 0.76, RMSE = 282.8 g m, and n RMSE = 39.24%) in both the 2018 and 2019 growing seasons. The study confirms that AGB estimation models based on cumulative CTcperformed well under several irrigation treatments using high-resolution time-series UAV multispectral VIs and can support irrigation management with high spatial precision at a field scale.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
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 proposed.The ShuffleNetv2 basic modules and down...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 proposed.The ShuffleNetv2 basic modules and down-sampling modules were alternately connected,replacing the Backbone of YOLOv8n model.The Ghost modules replaced the Conv modules and the C2fGhost modules replaced the C2f modules in the Neck part of the YOLOv8n.ShuffleNetv2 reduced the memory access cost through channel splitting operations.The Ghost module combined linear and non-linear convolutions to reduce the network computation cost.The 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 boxes.The Squeeze-and-Excitation(SE)was embedded in the Backbone and Neck part of YOLOv8n to enhance the representation ability of feature maps.The algorithm ensured high precision while having small model size and fast detection speed,which facilitated model migration and deployment.Using 9652 images validated the effectiveness of the model.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 fps.The detection speeds on the Jetson Xavier NX development board were 3.17 fps.Comparisons 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 speed.The research can provide reference for the development of smart devices in apple orchards.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
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
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
Soybean leaf morphology is one of the most important morphological and biological characteristics of soybean.The germplasm gene differences of soybeans can lead to different phenotypic traits,among which soybean leaf ...Soybean leaf morphology is one of the most important morphological and biological characteristics of soybean.The germplasm gene differences of soybeans can lead to different phenotypic traits,among which soybean leaf morphology is an important parameter that directly reflects the difference in soybean germplasm.To realize the morphological classification of soybean leaves,a method was proposed based on deep learning to automatically detect soybean leaves and classify leaf morphology.The morphology of soybean leaves included lanceolate,oval,ellipse and round.First,an image collection platform was designed to collect images of soybean leaves.Then,the feature pyramid networks–single shot multibox detector(FPN-SSD)model was proposed to detect the top leaflets of soybean leaves on the collected images.Finally,a classification model based on knowledge distillation was proposed to classify different morphologies of soybean leaves.The obtained results indicated an overall classification accuracy of 0.956 over a private dataset of 3200 soybean leaf images,and the accuracy of classification for each morphology was 1.00,0.97,0.93 and 0.94.The results showed that this method could effectively classify soybean leaf morphology and had great application potential in analyzing other phenotypic traits of soybean.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
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
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
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
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
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
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
Bovine mastitis is the most complex and costly disease in the dairy industry worldwide.Somatic cell count(ScC)is accepted as an international standard for diagnosing mastitis in cows,but most instruments used to detec...Bovine mastitis is the most complex and costly disease in the dairy industry worldwide.Somatic cell count(ScC)is accepted as an international standard for diagnosing mastitis in cows,but most instruments used to detect scC are expensive,or the detection speed is very low.To develop a rapid method for identifying mastitis degree,the dielectric spectra of 301 raw milk samples at three mastitis grades,i.e.,negative,weakly positive,and positive grades based on ScC,were obtained in the frequency range of 20-450o MHz using coaxial probe technology.Variable im-portance in the projection method was used to select characteristic variables,and principal component analysis(PCA)and partial least squares(PLS)were used to reduce data dimension.Linear discriminant analysis,support vector classification(SvC),and feed-forward neural network models were established to predict the mastitis degrees of cows based on 22 principal components and 24 latent variables obtained by PCA and PLS,respectively.The results showed that the SvC model with PCA had the best classification performance with an accuracy rate of 95.8%for the prediction set.The research indicates that dielectric spectroscopy technology has great potential in developing a rapid detector to diagnose mastitisincowsinsituoronline.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
基金National Natural Science Foundation of China(61701372)Talents Special Foundation of Northwest A&F University(Z111021801).
文摘Internet of Things (IoT) has emerged as one of the new use cases in the 5th Generation wireless networks. However, the transient nature of the data generated in IoT networks brings great challenges for content caching. In this paper, we study a joint content caching and updating strategy in IoT networks, taking both the energy consumption of the sensors and the freshness loss of the contents into account. In particular, we decide whether or not to cache the transient data and, if so, how often the servers should update their contents. We formulate this content caching and updating problem as a mixed 0–1 integer non-convex optimization programming, and devise a Harmony Search based content Caching and Updating (HSCU) algorithm, which is self-learning and derivativefree and hence stipulates no requirement on the relationship between the objective and variables. Finally, extensive simulation results verify the effectiveness of our proposed algorithm in terms of the achieved satisfaction ratio for content delivery, normalized energy consumption, and overall network utility, by comparing it with some baseline algorithms.
文摘[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.
基金funded by the National Natural Science Foundation of China (51979233)the Natural Science Basic Research Plan in Shaanxi Province of China (2022JQ-363)。
文摘Estimating spatial variation in crop transpiration coefficients(CTc) and aboveground biomass(AGB)rapidly and accurately by remote sensing can facilitate precision irrigation management in semiarid regions. This study developed and assessed a novel machine learning(ML) method for estimating CTc and AGB using time-series unmanned aerial vehicle(UAV)-based multispectral vegetation indices(VIs)of maize under several irrigation treatments at the field scale. Four ML regression methods: multiple linear regression(MLR), support vector regression(SVR), random forest regression(RFR), and adaptive boosting regression(ABR), were used to address the complex relationship between CTcand VIs. AGB was then estimated using exponential, logistic, sigmoid, and linear equations because of their clear mathematical formulations based on the optimal CTcestimation model. The UAV VIs-derived CTcusing the RFR estimation model yielded the highest accuracy(R^(2)= 0.91, RMSE = 0.0526, and n RMSE = 9.07%). The normalized difference red-edge index, transformed chlorophyll absorption in reflectance index, and simple ratio contributed significantly to the RFR-based CTcmodel. The accuracy of AGB estimation using nonlinear methods was higher than that using the linear method. The exponential method yielded the highest accuracy(R^(2)= 0.76, RMSE = 282.8 g m, and n RMSE = 39.24%) in both the 2018 and 2019 growing seasons. The study confirms that AGB estimation models based on cumulative CTcperformed well under several irrigation treatments using high-resolution time-series UAV multispectral VIs and can support irrigation management with high spatial precision at a field scale.
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supported by the National Key Research and Development Program of China(2019YFD1002401)the National Natural Science Foundation of China(31701326).
文摘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 proposed.The ShuffleNetv2 basic modules and down-sampling modules were alternately connected,replacing the Backbone of YOLOv8n model.The Ghost modules replaced the Conv modules and the C2fGhost modules replaced the C2f modules in the Neck part of the YOLOv8n.ShuffleNetv2 reduced the memory access cost through channel splitting operations.The Ghost module combined linear and non-linear convolutions to reduce the network computation cost.The 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 boxes.The Squeeze-and-Excitation(SE)was embedded in the Backbone and Neck part of YOLOv8n to enhance the representation ability of feature maps.The algorithm ensured high precision while having small model size and fast detection speed,which facilitated model migration and deployment.Using 9652 images validated the effectiveness of the model.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 fps.The detection speeds on the Jetson Xavier NX development board were 3.17 fps.Comparisons 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 speed.The research can provide reference for the development of smart devices in apple orchards.
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 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 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Supported by Heilongjiang Province Philosophy and Social Science Research Planning Project(17TQB059)。
文摘Soybean leaf morphology is one of the most important morphological and biological characteristics of soybean.The germplasm gene differences of soybeans can lead to different phenotypic traits,among which soybean leaf morphology is an important parameter that directly reflects the difference in soybean germplasm.To realize the morphological classification of soybean leaves,a method was proposed based on deep learning to automatically detect soybean leaves and classify leaf morphology.The morphology of soybean leaves included lanceolate,oval,ellipse and round.First,an image collection platform was designed to collect images of soybean leaves.Then,the feature pyramid networks–single shot multibox detector(FPN-SSD)model was proposed to detect the top leaflets of soybean leaves on the collected images.Finally,a classification model based on knowledge distillation was proposed to classify different morphologies of soybean leaves.The obtained results indicated an overall classification accuracy of 0.956 over a private dataset of 3200 soybean leaf images,and the accuracy of classification for each morphology was 1.00,0.97,0.93 and 0.94.The results showed that this method could effectively classify soybean leaf morphology and had great application potential in analyzing other phenotypic traits of soybean.
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 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 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 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 Natural Science Foundation of China(No.32172308 and No.31671935).
文摘Bovine mastitis is the most complex and costly disease in the dairy industry worldwide.Somatic cell count(ScC)is accepted as an international standard for diagnosing mastitis in cows,but most instruments used to detect scC are expensive,or the detection speed is very low.To develop a rapid method for identifying mastitis degree,the dielectric spectra of 301 raw milk samples at three mastitis grades,i.e.,negative,weakly positive,and positive grades based on ScC,were obtained in the frequency range of 20-450o MHz using coaxial probe technology.Variable im-portance in the projection method was used to select characteristic variables,and principal component analysis(PCA)and partial least squares(PLS)were used to reduce data dimension.Linear discriminant analysis,support vector classification(SvC),and feed-forward neural network models were established to predict the mastitis degrees of cows based on 22 principal components and 24 latent variables obtained by PCA and PLS,respectively.The results showed that the SvC model with PCA had the best classification performance with an accuracy rate of 95.8%for the prediction set.The research indicates that dielectric spectroscopy technology has great potential in developing a rapid detector to diagnose mastitisincowsinsituoronline.
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.
