• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

    2023-12-12 15:50:26UzairAslamBhattiSibghatUllahBazaiShumailaHussainShariqaFakharChinSoonKuShahMarjanPorLipYeeandLiuJing
    Computers Materials&Continua 2023年10期

    Uzair Aslam Bhatti,Sibghat Ullah Bazai,Shumaila Hussain,Shariqa Fakhar,Chin Soon Ku,Shah Marjan,Por Lip Yee and Liu Jing

    1College of Information and Communication Engineering,Hainan University,Haikou,570228,China

    2Department of Computer Engineering,Balochistan University of Information Technology,Engineering,and Management Sciences(BUITEMS),Quetta,Pakistan

    3Department of Computer Science,Sardar Bahadur Khan Women’s University,Quetta,Pakistan

    4Department of Computer Science,Universiti Tunku Abdul Rahman,Kampar,31900,Malaysia

    5Department of Software Engineering,Balochistan University of Information Technology,Engineering,and Management Sciences(BUITEMS),Quetta,Pakistan

    6Faculty of Computer Science and Information Technology,University of Malaya,Kuala Lumpur,50603,Malaysia

    ABSTRACT Crop diseases have a significant impact on plant growth and can lead to reduced yields.Traditional methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and knowledge.To address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification.In this paper,we propose a novel approach that utilizes a convolutional neural network (CNN) model in conjunction with Inception v3 to identify plant leaf diseases.The research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific diseases.The models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct classes.Through rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural environments.The significance of this research lies in its potential to revolutionize plant disease detection and management practices.By automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual expertise.The integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

    KEYWORDS Plant disease;Inception v3;CNN;crop diseases

    1 Introduction

    Agriculture is the backbone of Pakistan’s economy [1].In terms of potential,this sector can produce both for the internal market and export.However,the contribution of agriculture to GDP has gradually declined to 19.3 percent in the last decades due to frequently occurring plant diseases and a lack of awareness about preventive and protective measures against diseases.This can have a detrimental influence on the economies of nations like Pakistan,where agriculture is the primary source of income.To prevent crop damage and increase harvesting quality,detecting,identifying,and acknowledging the infection from the initial stage is essential.In 2020,Pakistan’s agricultural sector contributed 22.69 percent to the GDP.The agricultural sector’s contribution to the GDP in 2020 decreased from 22.04 percent to 19.3 percent due to conventional farming practices and a lack of awareness regarding preventing and protecting plants from the disease[2].

    Many crops are cultivated in Pakistan during different seasons.According to the UN,almost 2,000 tons of cherry are produced annually in Pakistan.On a commercial basis,export-quality cherries are grown on about 897 hectares in Balochistan (mainly in Quetta,Ziarat,and Kalat),resulting in an annual production of 1,507 tonnes [3].Similarly,potatoes (Solanum tuberosumL.) are one of the world’s most extensively grown and consumed tuberous crops,and around 1300 kha of potato is planted in Pakistan[4].

    Approximately 75.4 million tons of apples were produced globally in 2013.Pakistan is one of the largest apple producers,primarily concentrated in Khyber Pakhtunkhwa,Punjab,and Baluchistan.Balochistan has the largest apple crop,covering 45,875 hectares of land annually,producing 589,281 tons of apples[5].Similarly,in 2013–2014,strawberry was grown on 236 ha in Pakistan[6].Berry is an emerging exotic fruit crop in subtropical regions of Pakistan.It remained unnoticed until it began to be produced commercially in Khyber Pakhtunkhwa.Its unique,desirable traits and profit potential have attracted attention [7].The capsicum is cultivated over 61,600 ha in Pakistan,yielding 110,500 tons per year[8].

    The world manufacturing of tomatoes experienced a consistent and non-stop increase in the 20th Century.Pakistan is one of the thirty-five biggest producers of potatoes [9].Grapes (Vitis vinifera)of the family Vitaceae part of the most well-liked fruit in the world.In Pakistan,the province of Balochistan contributes 98 percent of the country’s grape production.Grapes of several sorts are produced in the province’s upland areas.The vast majority of well-known and famous commercial types are grown in the districts of Quetta,Pishin,Killa Abdulla,Masting,Kalat,Loralai,and Zhob[10].Pakistan’s central peach-growing region is Swat.It has a total area of 14700 acres and produces 55800 tons yearly[11].

    A key issue in Pakistan is farmers’limited knowledge about crop diseases.Farmers are still using the traditional and outdated method of discovering the crops’conditions by personally and physically inspecting the produce.Farmers utilize their experience to monitor and analyze their harvests with their naked eyes.This traditional system has severe flaws and obstacles.If the farmer is unaware of disease types of crop infections,the crops will either go undiagnosed or be treated with the incorrect disease control method that can affect the crop’s yield and ruin the entire crop.Disease control is an important guarantee to ensure the safety of plant production,and it can also effectively improve the yield and quality of crops.The premise of prevention and control is to be able to detect diseases in a timely and accurate manner and to identify their types and severity[12].

    In plant disease identification,research objects are generally taken from parts of plants,such as stems,leaves,fruits,branches,and other parts with apparent characteristics.Plant leaves are easier to obtain than other parts and have prominent disease characteristics.From the perspective of botanical research,the shape,texture,and color of diseased leaves can be used as the basis for classification.After a pathogen infects a plant or becomes diseased,the diseased leaves’external characteristics and internal structure undergo subtle changes.The appearance is mainly reflected in fading,rolling,rot,discoloration,etc.The opposite internal factors are reflected in water and pigment.However,the symptoms of different diseases present ambiguity,complexity,and similarity.Farmers’low scientific and cultural quality in Pakistan make it impossible to accurately and timely diagnose plant disease’s period and development process [13].We only spray large doses of chemicals when the human eye finds that the disease severely affects the plant.This negligence causes a significant reduction in crop yields and causes pollution.Therefore;accessible;accurate;prompt plant disease identification and assessing the degree of damage to provide practical information for disease control has become an essential issue in crop production.

    With the continuous development of computer technology and mobile phone applications,smartphones have become essential for people to connect.Taking pictures and videos has become a must-have tool for mobile phones.The concept of deep learning is widely implemented to develop,enhance and expand the utilization of mobile applications in different areas.With the development of network technology,people share data through the network,which not only enriches the material of the data but also obtains data images at a low cost,which provides a large amount of data for the training of convolutional neural networks.With the rapid development of storage technology and the continuous updating of the Internet,mobile phone CPUs’computing power has also been continuously strengthened,laying a foundation for computing power to build a lightweight image recognition model on the mobile phone.In recent years,elements combined with artificial intelligence have begun to appear on mobile terminals.For example,with intelligent voice and recognition development,the mobile phone device is like an intelligent robot.This has laid the equipment foundation for this kind of work on the mobile phone.At present,many cases of image recognition are gradually being applied to mobile phones.

    Using technology,the crop’s disease detection procedure can be automated.Artificial intelligence techniques and computer vision systems are most widely used for automating disease detection in plants[14–21].The use of machine learning has revolutionized computer vision,especially in imagebased detection and classification[22].The convolutional neural networks CNNs is a deep learning approach that is most promising in agriculture for plant species identification,yield management,weed identification,water control,soil maintenance,counting harvest yield,disease identification,pest detection,and field management[23–32].The research proposes a deep learning-based technique to automatically identify plant leaf disease.The proposed mechanism uses the convolutional neural network CNN and Inception v3 to identify plant leaf disease and provide recommendations to overcome the specified condition.To make it convenient for the farmer to implement the automated machines in a real-time agricultural environment,The research focused on developing a mobile application.The mobile application is capable of capturing the image of the plant leaf;identifying the disease and providing recommendations to overcome the identified condition.

    2 Literature Review

    Deep learning techniques are proven to be very successful in all areas [33–35].Plant diseases in agriculture can have devastating consequences and cause economic loss.Researchers are focusing on techniques to improve automatic plant disease detection and have developed different techniques.Convolutional Neural Networks(CNN)showed significant outcomes in image classification,object recognition,and semantic segmentation.The tremendous feature learning and classification capabilities of CNNs have attracted widespread attention.Using PlantVillage datasets with 20,639 pictures,Slava et al.[36]exhibited hyperparameters enhancing the existing ResNet50 for disease classification and achieved good accuracy.Brady et al.[37]proposed a hybrid technique based on the convolutional autoencoder(CAE)and convolutional neural networks for disease detection in leaves of peach.The proposed model uses few parameters and provides 98.38%test accuracy on the PlantVillage dataset.Agarwal et al.[38]suggested a Conv2D model to determine disease severity in cucumber plants and achieved improved results.Similarly,Shen et al.[39]conducted a comparison of six models to identify powdery mildew on strawberry leaves.He concluded that ResNet-50 has the highest classification accuracy of 98.11%,AlexNet is the fastest processing,and SqueezeNet-MOD2 has the smallest memory footprint.

    VGG16 was used by Jiang et al.[40] to detect diseases in rice and wheat plants.Halil Durmu?s[41]developed a plant disease detection system using AlexNet,SqueezeNet,and CNN models.Their dataset contains 18,000 tomato images collected by Plant Village in 10 categories.The overall accuracy of their neural network was 94.3%.

    Another researcher[42]implemented a set of tests using the dataset of 552 apple leaves affected by black rot disease.The photos of disease at four stages were considered,110 photographs of healthy plant leaves,137 images of early disease,180 images of mid-stage disease,and 125 pictures of latestage.They used the VGG-16 model to analyze the data.Transfer learning helped them in improving the model and showed 90.4 percent accuracy.

    The ResNet-50 model was trained on 3750 tomato leaf images using PlantVillage dataset by Bart et al.[43].They correctly classified leaf diseases on tomato plants and achieved 99.7%accuracy.Another researcher[44]has chosen maize leaves for disease identification on a collection of 400 maize leaf images using the CNN model and obtained 92.85%accuracy.Using input images of aspects 200×200,the VGG-A model(Visual Geometry Group-Architecture)along with CNN(8 convolutional layers with 2 fully linked layers) is used to identify healthy radishes affected with fusarium shrink disease.Another research is conducted to classify potato disease using the VGG model containing 8 trainable layers,three fully linked layers,and five convolutional layers.The quantity of the training dataset affects the VGG model’s classification and achieves 83%accuracy[45].

    Amara et al.[46]used 3700 photos of banana leaves from the PlantVillage collection to conduct their studies.They highlighted the effects of lighting,size,background,attitude,and orientation of images on the performance of their model.Yadav et al.[47] used a deep learning technique for automated segmentation and detected the selected diseases in the leaves of peach.They separated the test in the controlled laboratory environment and on actual cultivation and achieved 98.75 percent overall categorization accuracy.Similarly,Sladojevic et al.[48] created a database by downloading 4483 photos from the Internet.These photos are divided into 15 categories,13 classes for damaged plants,one class for healthy leaves,and one class for the background.The overall accuracy of the experimental outcome using AlexNet was 96.3 percent [49,50].Table 1 given below,indicates the benefits and drawbacks of machine learning techniques so far used for plant leaf disease detection.

    Table 1:Benefits and drawbacks of machine learning/deep learning algorithms

    3 Materials and Methods

    The proposed plant leaf disease detection and recommendation consists of dataset preparation,classification,disease identification,and suggestions to cope with the disease.

    3.1 Environmental Settings

    We have implemented the model using Python programming language and TensorFlow and OpenCV libraries.The data preprocessing,prediction,and recommendations performed by the model are implemented using Google Colab with high-speed 16 GB RAM,and eight Tesla P100 GPUs.

    3.2 Dataset

    The dataset used in this experiment selects several plant leaf diseases using PlantVillage and PlantDoc datasets,like scab disease,black rot,rust and grape leaf black rot,black pox,leaf blight in apple leaves,etc.

    3.3 Image Acquisition

    To train our disease detection and recommendation system,we have used PlantVillage and PlantDoc datasets,including vegetables,fruits,and fruits vegetables.The dataset contains 80,848 images of leaves from 21 crops,which include apples,cherries,corn,grapes,peaches,bell peppers,potatoes,strawberries,tomatoes,oranges,and squash.The dataset contains 60 classes.Almost all leaf diseases that can harm crops are included in the dataset.

    3.4 Data Preprocessing

    The preprocessing of the dataset before implementing the deep learning technique can improve performance and accuracy.The data was therefore preprocessed so that it can be analyzed appropriately.

    3.5 Data Augmentation

    The process of data augmentation is to increase the amount of data using existing data to improve accuracy.An improperly trained neural network may be unable to predict explicit output;however,with enough data,it can be perfectly fitted.The disease identification model in this research is built via image augmentation.Image augmentation produces huge diversified images from pictures used for classification,object detection,and segmentation.This study has investigated a few factors to augment the data,such as random horizontal flipping,rescaling 1/255,and zoom.

    3.6 Training and Testing Dataset

    Models must be evaluated to confirm the accuracy of any neural network.After applying data augmentation,we partitioned the selected dataset for testing and training.In training,we let the model learn while in testing the ensuring accuracy.

    Fig.1 given above depicts the flow diagram of the proposed plant disease detection and recommendation system.

    3.7 Model Design

    The proposed system used the CNN model with 5 convolutional layers and 5 max pooling layers.The input width(nw)and height(nh)of the first convolutional layers are 128 and 128,respectively.In the first step the CNN is used to train the selected dataset in the second step image segmentation of leaves is performed.The Inception v3 along with CNN is used to segment the image features extraction.In the third step,the proposed model performs classification or identification of the specie of disease.In the fourth step,the system provides recommendations to overcome the disease.Fig.2 given below describes the structural composition and detailed overview of the proposed plant leaf disease detection and recommendation system.

    3.8 Model Implementation

    Once the data is augmented,the CNN model is used to train selected datasets.The CNN is a multilayer structural model in which each layer generates a reaction and extracts key elements from the dataset.

    A total of 60,448 images were used to train the model,while around 20,461 crop images were used to validate them.The convolutional neural network CNN model,along with Inception v3 is used in the proposed model to detect plant disease and provide recommendations.

    3.9 Deep Features Extraction

    The deep learning model convolutional neural network CNN is very promising for classifying text and images.Fig.3 given below,shows the detail of the layers used in the CNN model.

    Figure 1:Flow diagram of proposed plant disease detection and recommendation system

    3.10 Convolution Operation

    The convolution is an operation applied to two functions with real numbers as arguments.The convolution operation is defined by the following mathematical expression:

    The(x)is the input while the w is the kernel the output of this function is known as a feature map.The discrete convolution is matrices multiplication.Fig.4 is given below the convolutional operation of the CNN model used by the proposed system.

    Figure 2:Diagrammatic representation of the proposed model

    3.11 Pooling

    The pooling layer is a significant component of the CNN model.To lessen the amount of work that has to be done by the network in terms of computing and parameter management,this component must gradually shrink the physical dimensions of the representation.

    Max Pooling is an action that takes the maximum value of all of the parameters and reduces the attribute or value by a factor of 4.

    Figure 3:Layers in convolutional neural network model

    Figure 4:Convolution operation in CNN

    The procedure known as “Average Pooling”chooses the arithmetic mean of the area to utilize,which decreases the amount of data by a factor of 4.Fig.5 below depicts the pooling function of the CNN model using maximum clustering of left and middle right pooling.

    The Convolutional Neural Network model is used with 5 convolutional layers and 5 max pooling layers.The input width (nw) and height (nh) of the first convolutional layers are 128 and 128,respectively.The softmax activation function is used in the output layer of the convolutional model to ensure that all logits add up to one,and satisfies the probability density restrictions.The CNN is responsible for extracting the features.Because we have sixty output categories,the dense unit in our model is sixty.

    3.12 Inception v3

    There are 42 layers in the Inception v3 machine learning model with fewer parameters.Convolutions are factorized to lower the parameters.For example,a 5×5 filter convolution can be achieved by combining two 3×3 filter convolutions.This technique reduces the parameters from 5×5=25 to 3×3+3×3=18.As a result,there is a 28%drop in the number of parameters.Overfitting is less evident in a model with fewer parameters,resulting in higher accuracy.

    Figure 5:Pooling function with maximum clustering of left and middle right pooling

    4 Performance Evaluation Metrics

    The research used performance evaluation metrics for accuracy,precision,recall,and F1-score.Note that the basic confusion matrix can be misleading;therefore,we used the performance above evaluation criteria.

    4.1 Accuracy

    Accuracy (A) represents the proportion of currently classified predictions and is calculated as follows:

    Note that TP,TN,FP,and FN represent true positive,true negative,false positive,and false negative,respectively.

    4.2 Precision

    The term “precision” abbreviated as “P” refers to the percentage of positive outcomes that correspond to answers that are accurate and is computed as follows:

    4.3 Recall

    Recall(R)is a measurement that determines the percentage of real positives that were accurately detected,and its calculation goes as follows:

    4.4 F1-Score

    The F1-score is computed as the harmonic mean of the accuracy and recall scores,and its definition and calculation are as follows:

    4.5 Model Prediction

    A mobile application is developed using an Android studio to make user interaction easier.The user captures the plant leaf image and uploads it on the software as an input image.After processing the image,the proposed system determines if the plant is infected.The system analyzes the plant leaf disease and displays the results.Moreover,the system is capable of suggesting recommendations to overcome the disease.Fig.6 depicts the working of the proposed plant leaf disease detection and recommendation system deployed on an Android environment.

    Figure 6:Working of the proposed model

    5 Results and Discussions

    This section reports results using the proposed model for disease detection in plant leaves and recommendations to overcome the disease.The classification task is carried out using fully connected layers with the ReLu activation function,and softmax is employed at the final layer.Moreover,the Inception v3 is used with the convolutional neural network model to perform the feature extraction and classification.Analysis of results concludes that using Inception v3 architecture along with CNN outperformed with the highest reported accuracy.

    A comparison of the proposed system in terms of precision,recall,and F1-score using the same dataset is given below in Table 2,indicating the proposed model’s improved performance.

    Table 2:Performance evaluation results

    Table 3 indicates the comparative analysis of the proposed model with famous machine-learning techniques used for plant leaf disease detection.

    Table 3:Comparative analysis of the proposed model using the PlantVillage and Plant Doc dataset

    Above Fig.7 depicts the loss for training and validation of the proposed model.

    Figure 7:Loss curve of training and validation

    Fig.8 Indicates the accuracy of validation and training for the proposed model.

    The above Table 4 indicates the performance of the proposed model using different plant leaf datasets.The above results suggest that the proposed model outperformed existing techniques.The Inception v3 uses various kernel sizes to identify features of varying sizes efficiently.Increasing the number of layers model can spread them out more thinly across the screen and pairing Inception v3 with CNN uplifted the performance of the proposed model.

    Figure 8:Accuracy comparison with training and validation

    Table 4:Comparative analysis of the proposed model on multiple datasets

    Although this paper designs a convenient mobile plant disease detection system,there are still some points that can be improved in the future:

    ? The proposed model considers the PlantVillage and PlantDoc Dataset.These images are taken indoors in a controlled environment.The system is designed to work in a real-time environment.It can therefore be said that the system will be affected by external environments such as sunlight.In follow-up research,we will add the collection of plant disease leaves taken under natural conditions to examine the system’s performance.

    6 Conclusion

    With the continuous development of intelligent technology,more intelligent devices began to penetrate various fields to replace human labor and reduce costs.Technological advancements in agriculture have induced efficiency,lowered prices,reduced time,and improved production.The technological advances in plant leaf disease identification are at an early stage;it is done by visiting the field to capture images using cameras.These images are then inspected using technology to identify the disease,which is still time-consuming.This paper proposes a novel approach using a convolutional neural network model and inception v3 to identify plant leaf diseases.The proposed model is capable of working in a real-time environment.This research focused on developing a mobile application using the proposed model to identify plant disease and provide recommendations to overcome the identified disease.The model achieved 99%accuracy.The proposed model is a convenient and beneficial advisory or early warning tool to operate in a real agricultural environment.

    Acknowledgement:Thanks for reviewers and editors for providing suggestions during review process.

    Funding Statement:This study is supported by the Hainan Provincial Natural Science Foundation of China(No.123QN182)and Hainan University Research Fund(Project Nos.KYQD(ZR)-22064,KYQD(ZR)-22063,and KYQD(ZR)-22065).

    Author Contributions:Study conception and design:Uzair Aslam Bhatti,Sibghat Ullah Bazai,Shumaila Hussain,Shariqa Fakhar,Chin Soon Ku,Shah Marjan,Por Lip Yee,Liu Jing;data collection:Sibghat Ullah Bazai,Shumaila Hussain,Shariqa Fakhar,Chin Soon Ku,Shah Marjan,Por Lip Yee,Liu Jing;analysis and interpretation of results: Uzair Aslam Bhatti,Sibghat Ullah Bazai,Shumaila Hussain,Shariqa Fakhar,Chin Soon Ku;draft manuscript preparation:Uzair Aslam Bhatti,Sibghat Ullah Bazai,Shumaila Hussain,Shariqa Fakhar,Chin Soon Ku.All authors reviewed the results and approved the final version of the manuscript.

    Availability of Data and Materials:The data will be available on suitable request from corresponding author.

    Conflicts of Interest:The authors declare they have no conflicts of interest to report regarding the present study.

    网址你懂的国产日韩在线| av女优亚洲男人天堂 | h日本视频在线播放| 12—13女人毛片做爰片一| 成年版毛片免费区| 欧美日本视频| 91九色精品人成在线观看| 亚洲欧美日韩东京热| 国产精品一区二区三区四区免费观看 | 亚洲av五月六月丁香网| 亚洲欧美精品综合一区二区三区| 日本成人三级电影网站| 特级一级黄色大片| 日本熟妇午夜| 国产成+人综合+亚洲专区| 一个人看视频在线观看www免费 | 亚洲专区国产一区二区| 国产极品精品免费视频能看的| 日韩人妻高清精品专区| 亚洲电影在线观看av| 12—13女人毛片做爰片一| 日韩欧美国产一区二区入口| 18禁观看日本| 久久国产乱子伦精品免费另类| 男女做爰动态图高潮gif福利片| 十八禁网站免费在线| 女生性感内裤真人,穿戴方法视频| 欧美性猛交╳xxx乱大交人| 性色av乱码一区二区三区2| 51午夜福利影视在线观看| 欧美性猛交╳xxx乱大交人| 中亚洲国语对白在线视频| 亚洲成av人片免费观看| 亚洲一区高清亚洲精品| 美女大奶头视频| 亚洲专区字幕在线| 欧美日韩精品网址| 99久久久亚洲精品蜜臀av| 久久欧美精品欧美久久欧美| 老熟妇仑乱视频hdxx| 网址你懂的国产日韩在线| 欧美乱码精品一区二区三区| 日韩国内少妇激情av| 亚洲熟女毛片儿| 欧美又色又爽又黄视频| 香蕉av资源在线| 精品久久久久久久毛片微露脸| 一个人观看的视频www高清免费观看 | 国产私拍福利视频在线观看| 久久久久久国产a免费观看| 免费大片18禁| 一二三四社区在线视频社区8| 国产伦精品一区二区三区视频9 | ponron亚洲| 黄色成人免费大全| 黑人操中国人逼视频| 日本三级黄在线观看| 国产一级毛片七仙女欲春2| 婷婷精品国产亚洲av在线| 亚洲美女视频黄频| 国产午夜精品久久久久久| 免费看美女性在线毛片视频| 亚洲国产精品成人综合色| 国产欧美日韩一区二区精品| 精品久久蜜臀av无| 欧美一级a爱片免费观看看| 欧美乱色亚洲激情| 国产99白浆流出| 国产一区在线观看成人免费| www.999成人在线观看| 国产伦人伦偷精品视频| 毛片女人毛片| aaaaa片日本免费| 制服人妻中文乱码| 91在线观看av| 久久国产精品影院| 大型黄色视频在线免费观看| 国产一区在线观看成人免费| 九色成人免费人妻av| 亚洲av熟女| 在线观看免费午夜福利视频| 校园春色视频在线观看| 精品国内亚洲2022精品成人| 成人高潮视频无遮挡免费网站| 1000部很黄的大片| 少妇丰满av| 亚洲熟妇熟女久久| 99久久无色码亚洲精品果冻| 操出白浆在线播放| 日本三级黄在线观看| 久久久久免费精品人妻一区二区| 日本成人三级电影网站| 成人三级黄色视频| 长腿黑丝高跟| 欧美激情在线99| 亚洲欧美日韩高清专用| 国产97色在线日韩免费| 淫妇啪啪啪对白视频| 一个人看视频在线观看www免费 | 久久国产精品影院| 欧美av亚洲av综合av国产av| 国产主播在线观看一区二区| 久久精品影院6| 欧美黑人巨大hd| 很黄的视频免费| 国产精品日韩av在线免费观看| 黄色视频,在线免费观看| 国产亚洲精品久久久com| 狂野欧美激情性xxxx| 亚洲 欧美一区二区三区| 国产乱人伦免费视频| 97碰自拍视频| 日本精品一区二区三区蜜桃| 极品教师在线免费播放| 桃红色精品国产亚洲av| 精品无人区乱码1区二区| 欧美极品一区二区三区四区| 啦啦啦韩国在线观看视频| 又黄又粗又硬又大视频| 国产高潮美女av| 婷婷六月久久综合丁香| 一个人免费在线观看电影 | 亚洲中文字幕一区二区三区有码在线看 | 欧美性猛交╳xxx乱大交人| 最近在线观看免费完整版| 特大巨黑吊av在线直播| 亚洲欧美日韩高清在线视频| 婷婷亚洲欧美| 日本五十路高清| 十八禁网站免费在线| 嫩草影院入口| 狂野欧美激情性xxxx| 成人欧美大片| 国产视频一区二区在线看| 亚洲美女黄片视频| 国产真人三级小视频在线观看| 国产精品自产拍在线观看55亚洲| 亚洲人成电影免费在线| x7x7x7水蜜桃| 看免费av毛片| 全区人妻精品视频| 日韩 欧美 亚洲 中文字幕| 国产精品免费一区二区三区在线| 91久久精品国产一区二区成人 | 桃红色精品国产亚洲av| 午夜成年电影在线免费观看| 久久精品亚洲精品国产色婷小说| 午夜视频精品福利| 精品国产美女av久久久久小说| 一个人免费在线观看电影 | 国产高潮美女av| 美女黄网站色视频| 夜夜爽天天搞| 女人被狂操c到高潮| 亚洲成av人片在线播放无| 精品电影一区二区在线| 精品乱码久久久久久99久播| 免费看十八禁软件| 日韩欧美在线二视频| 特级一级黄色大片| 亚洲美女黄片视频| 俄罗斯特黄特色一大片| 在线观看日韩欧美| 观看免费一级毛片| 在线观看66精品国产| 五月伊人婷婷丁香| 亚洲国产欧美人成| 亚洲色图 男人天堂 中文字幕| 99久久成人亚洲精品观看| 亚洲午夜精品一区,二区,三区| 亚洲成人久久性| 一进一出抽搐gif免费好疼| 色噜噜av男人的天堂激情| 免费观看人在逋| 色视频www国产| 少妇的逼水好多| 不卡一级毛片| 中文字幕人妻丝袜一区二区| 日本 欧美在线| 亚洲国产中文字幕在线视频| 欧美绝顶高潮抽搐喷水| 亚洲真实伦在线观看| 亚洲精品粉嫩美女一区| 给我免费播放毛片高清在线观看| 国产av一区在线观看免费| 国产精品一区二区三区四区免费观看 | 蜜桃久久精品国产亚洲av| 欧美激情久久久久久爽电影| 午夜影院日韩av| 国产成人精品无人区| 99热这里只有精品一区 | 国产成人系列免费观看| 国产精品av久久久久免费| 欧美成人免费av一区二区三区| 黄色成人免费大全| 国产精品av视频在线免费观看| 69av精品久久久久久| 国产精品亚洲av一区麻豆| www日本在线高清视频| 午夜福利欧美成人| 国产一区二区激情短视频| 欧美日韩一级在线毛片| 看黄色毛片网站| 免费看a级黄色片| 国产精品av久久久久免费| 欧美中文日本在线观看视频| 久久精品亚洲精品国产色婷小说| 国产主播在线观看一区二区| a在线观看视频网站| 午夜精品一区二区三区免费看| 亚洲电影在线观看av| 99久久精品热视频| 亚洲成av人片免费观看| 日本一本二区三区精品| 日本黄色视频三级网站网址| 美女黄网站色视频| 中国美女看黄片| 在线播放国产精品三级| 搞女人的毛片| 91av网一区二区| 俄罗斯特黄特色一大片| 性欧美人与动物交配| a级毛片a级免费在线| av天堂中文字幕网| 亚洲色图av天堂| 在线a可以看的网站| 日本黄色视频三级网站网址| 天堂√8在线中文| 男女之事视频高清在线观看| 亚洲国产欧美一区二区综合| 欧洲精品卡2卡3卡4卡5卡区| 狂野欧美激情性xxxx| 在线看三级毛片| 日韩欧美在线二视频| 可以在线观看毛片的网站| 亚洲成av人片在线播放无| 不卡一级毛片| 99久久精品热视频| 午夜精品在线福利| 夜夜躁狠狠躁天天躁| 人妻夜夜爽99麻豆av| 长腿黑丝高跟| 1024手机看黄色片| 亚洲一区二区三区不卡视频| АⅤ资源中文在线天堂| 国内久久婷婷六月综合欲色啪| 国内少妇人妻偷人精品xxx网站 | 国内精品一区二区在线观看| 亚洲精品美女久久av网站| 欧美丝袜亚洲另类 | 最近在线观看免费完整版| 婷婷亚洲欧美| 51午夜福利影视在线观看| 怎么达到女性高潮| 国内揄拍国产精品人妻在线| 中出人妻视频一区二区| 久久欧美精品欧美久久欧美| 精品一区二区三区视频在线 | 午夜视频精品福利| 欧美成狂野欧美在线观看| 日韩国内少妇激情av| 久久香蕉国产精品| www.熟女人妻精品国产| av欧美777| 免费在线观看亚洲国产| 好男人电影高清在线观看| 色精品久久人妻99蜜桃| 成年版毛片免费区| 极品教师在线免费播放| 日韩欧美在线乱码| 一二三四社区在线视频社区8| 日本三级黄在线观看| 村上凉子中文字幕在线| 日本撒尿小便嘘嘘汇集6| 亚洲精品在线美女| 日本一二三区视频观看| 久久国产精品影院| 69av精品久久久久久| 少妇的逼水好多| 国产精品免费一区二区三区在线| www.精华液| 999久久久国产精品视频| 美女cb高潮喷水在线观看 | 嫩草影院入口| 成年版毛片免费区| 欧美成人性av电影在线观看| netflix在线观看网站| 国产高清视频在线观看网站| 伦理电影免费视频| 国内揄拍国产精品人妻在线| 欧美另类亚洲清纯唯美| 12—13女人毛片做爰片一| 18禁裸乳无遮挡免费网站照片| 久久精品亚洲精品国产色婷小说| 欧美性猛交╳xxx乱大交人| 欧美最黄视频在线播放免费| 在线观看一区二区三区| 国产v大片淫在线免费观看| 成年女人看的毛片在线观看| 国产精品 国内视频| 9191精品国产免费久久| 国产一区二区激情短视频| 久久久久国产一级毛片高清牌| 久久精品人妻少妇| 精品一区二区三区视频在线 | 日本精品一区二区三区蜜桃| 亚洲av成人精品一区久久| 男女午夜视频在线观看| 热99re8久久精品国产| 亚洲熟女毛片儿| a级毛片在线看网站| 啦啦啦韩国在线观看视频| 99热只有精品国产| 淫妇啪啪啪对白视频| 久久精品国产清高在天天线| 国产久久久一区二区三区| 久久久国产欧美日韩av| 99久久精品国产亚洲精品| 12—13女人毛片做爰片一| 嫁个100分男人电影在线观看| 岛国在线观看网站| 国产高清有码在线观看视频| 久久精品国产综合久久久| 十八禁网站免费在线| 观看免费一级毛片| 国产私拍福利视频在线观看| 久久国产精品人妻蜜桃| 国产精品久久久人人做人人爽| 狂野欧美激情性xxxx| 欧美日本亚洲视频在线播放| 久久久久性生活片| 久久久久亚洲av毛片大全| 午夜精品在线福利| 国产高潮美女av| tocl精华| 一进一出抽搐gif免费好疼| 免费观看的影片在线观看| 黄色成人免费大全| 99riav亚洲国产免费| 在线观看66精品国产| 男女视频在线观看网站免费| 三级男女做爰猛烈吃奶摸视频| 欧美成人性av电影在线观看| 手机成人av网站| or卡值多少钱| 91九色精品人成在线观看| 精品久久久久久,| 男人和女人高潮做爰伦理| 一级黄色大片毛片| 欧美性猛交╳xxx乱大交人| 看黄色毛片网站| 亚洲18禁久久av| 国产在线精品亚洲第一网站| 亚洲精品色激情综合| 观看免费一级毛片| 一二三四社区在线视频社区8| 亚洲在线观看片| 男女床上黄色一级片免费看| 成人一区二区视频在线观看| 哪里可以看免费的av片| 日日夜夜操网爽| 黄片小视频在线播放| 日本在线视频免费播放| 国产精品,欧美在线| 最近最新中文字幕大全电影3| 国产91精品成人一区二区三区| 精品久久久久久久久久久久久| 一区福利在线观看| 国产激情久久老熟女| 中文字幕人成人乱码亚洲影| 校园春色视频在线观看| 他把我摸到了高潮在线观看| 91老司机精品| 久久久水蜜桃国产精品网| 久久伊人香网站| 无遮挡黄片免费观看| 18美女黄网站色大片免费观看| 国产精品99久久99久久久不卡| 国产伦人伦偷精品视频| 欧美日韩精品网址| 一边摸一边抽搐一进一小说| 国产精品久久久久久久电影 | 精品一区二区三区av网在线观看| 亚洲熟妇熟女久久| 亚洲国产欧美网| 免费看美女性在线毛片视频| 日本熟妇午夜| 国产精品久久视频播放| 国产亚洲精品久久久久久毛片| 国产成人影院久久av| 一区二区三区激情视频| 香蕉国产在线看| 99久久久亚洲精品蜜臀av| 日韩有码中文字幕| 国产三级在线视频| 校园春色视频在线观看| 国产一区在线观看成人免费| 99久久成人亚洲精品观看| 国产精品,欧美在线| 色尼玛亚洲综合影院| 国产伦精品一区二区三区视频9 | 丁香六月欧美| 极品教师在线免费播放| 国产精品国产高清国产av| 欧美zozozo另类| 老熟妇乱子伦视频在线观看| 黄片大片在线免费观看| 动漫黄色视频在线观看| 别揉我奶头~嗯~啊~动态视频| 伊人久久大香线蕉亚洲五| 欧美色欧美亚洲另类二区| 无遮挡黄片免费观看| 成人三级做爰电影| 国产高清激情床上av| 国产aⅴ精品一区二区三区波| 亚洲18禁久久av| 一级毛片精品| 极品教师在线免费播放| 亚洲色图av天堂| av视频在线观看入口| 一个人看的www免费观看视频| 亚洲国产精品成人综合色| 欧美日韩综合久久久久久 | 久久精品亚洲精品国产色婷小说| 亚洲黑人精品在线| 亚洲熟女毛片儿| 午夜精品久久久久久毛片777| ponron亚洲| 99久久精品国产亚洲精品| 亚洲成人精品中文字幕电影| 久久伊人香网站| 88av欧美| 成人精品一区二区免费| 99国产综合亚洲精品| 岛国在线观看网站| 午夜福利视频1000在线观看| 又粗又爽又猛毛片免费看| 免费在线观看亚洲国产| 在线观看一区二区三区| 亚洲欧洲精品一区二区精品久久久| 丰满人妻一区二区三区视频av | 一本综合久久免费| 国产伦人伦偷精品视频| 一个人看的www免费观看视频| 午夜福利视频1000在线观看| 此物有八面人人有两片| av福利片在线观看| 天天添夜夜摸| h日本视频在线播放| 欧美成人一区二区免费高清观看 | 成人av在线播放网站| 97碰自拍视频| 欧美极品一区二区三区四区| 久久精品国产清高在天天线| 麻豆一二三区av精品| 黄频高清免费视频| 成年版毛片免费区| 国产亚洲av嫩草精品影院| 又粗又爽又猛毛片免费看| 免费看日本二区| 日本 av在线| 久久久精品大字幕| 久久中文字幕人妻熟女| 成人精品一区二区免费| 69av精品久久久久久| 99视频精品全部免费 在线 | 国产精品99久久99久久久不卡| 青草久久国产| 免费看a级黄色片| 国产精品久久电影中文字幕| 老汉色av国产亚洲站长工具| 观看免费一级毛片| 嫩草影院精品99| 亚洲中文日韩欧美视频| 国产精品久久久久久人妻精品电影| 日本黄色视频三级网站网址| 热99re8久久精品国产| 成人国产一区最新在线观看| 老司机午夜福利在线观看视频| 香蕉av资源在线| 丁香六月欧美| 亚洲aⅴ乱码一区二区在线播放| 亚洲精品一卡2卡三卡4卡5卡| 99在线视频只有这里精品首页| 久久人人精品亚洲av| 丰满人妻熟妇乱又伦精品不卡| 国产成人福利小说| 免费在线观看视频国产中文字幕亚洲| 久久久久九九精品影院| 午夜免费成人在线视频| 亚洲国产中文字幕在线视频| 19禁男女啪啪无遮挡网站| 9191精品国产免费久久| 久久天躁狠狠躁夜夜2o2o| 亚洲一区二区三区色噜噜| tocl精华| 一级a爱片免费观看的视频| 真实男女啪啪啪动态图| 叶爱在线成人免费视频播放| 国产视频一区二区在线看| 黄色片一级片一级黄色片| 一a级毛片在线观看| 人妻丰满熟妇av一区二区三区| 国产精品女同一区二区软件 | 三级毛片av免费| 91av网一区二区| svipshipincom国产片| 老汉色av国产亚洲站长工具| 天天一区二区日本电影三级| 男人和女人高潮做爰伦理| 精品久久久久久,| 久久国产精品人妻蜜桃| 欧美性猛交╳xxx乱大交人| 在线a可以看的网站| 国产成人一区二区三区免费视频网站| 久久这里只有精品中国| 免费观看精品视频网站| 嫩草影院精品99| 又紧又爽又黄一区二区| 免费在线观看亚洲国产| 亚洲激情在线av| 啦啦啦韩国在线观看视频| 欧美成人性av电影在线观看| 1000部很黄的大片| 成熟少妇高潮喷水视频| 国产午夜精品久久久久久| 日韩精品中文字幕看吧| 久99久视频精品免费| 97人妻精品一区二区三区麻豆| 一本精品99久久精品77| 亚洲狠狠婷婷综合久久图片| 99视频精品全部免费 在线 | 久久精品国产清高在天天线| 91九色精品人成在线观看| 午夜免费激情av| 午夜福利在线在线| 国产又黄又爽又无遮挡在线| 我要搜黄色片| 色哟哟哟哟哟哟| 国产成人一区二区三区免费视频网站| 日韩欧美一区二区三区在线观看| 国产亚洲精品综合一区在线观看| 丝袜人妻中文字幕| 男人舔女人的私密视频| 国产精品永久免费网站| 在线免费观看的www视频| netflix在线观看网站| 十八禁人妻一区二区| 精品人妻1区二区| 观看免费一级毛片| 搡老妇女老女人老熟妇| 999久久久精品免费观看国产| 国产日本99.免费观看| 日韩欧美一区二区三区在线观看| 久久精品国产综合久久久| 欧美激情在线99| 麻豆成人av在线观看| 久久久国产成人免费| 波多野结衣高清无吗| 国产69精品久久久久777片 | 欧美中文综合在线视频| 亚洲精品色激情综合| 亚洲av片天天在线观看| 精品免费久久久久久久清纯| 欧美日韩精品网址| 俺也久久电影网| 国产乱人视频| 国产单亲对白刺激| 国产免费av片在线观看野外av| 最新美女视频免费是黄的| 色综合亚洲欧美另类图片| 免费观看精品视频网站| 国产精品精品国产色婷婷| 国产精品乱码一区二三区的特点| 亚洲aⅴ乱码一区二区在线播放| 成人国产综合亚洲| 在线观看美女被高潮喷水网站 | 国产精华一区二区三区| 午夜福利在线在线| 精品久久久久久久久久免费视频| 性色av乱码一区二区三区2| 国内精品美女久久久久久| 亚洲欧美精品综合久久99| 亚洲精品色激情综合| 一区二区三区国产精品乱码| 欧美日韩中文字幕国产精品一区二区三区| 香蕉久久夜色| 男女做爰动态图高潮gif福利片| 免费看十八禁软件| 麻豆久久精品国产亚洲av| 免费在线观看成人毛片| 欧美av亚洲av综合av国产av| 高清在线国产一区| 亚洲熟妇中文字幕五十中出| 又黄又粗又硬又大视频| 两性夫妻黄色片| 久久国产乱子伦精品免费另类| 熟女少妇亚洲综合色aaa.| 五月玫瑰六月丁香| 国产亚洲精品综合一区在线观看| 亚洲欧美日韩卡通动漫| 午夜福利在线观看免费完整高清在 | 91九色精品人成在线观看| 国产一区二区三区在线臀色熟女| 中文字幕高清在线视频| 国产精品精品国产色婷婷| 老司机在亚洲福利影院| 给我免费播放毛片高清在线观看| 午夜福利在线在线| 欧美成人免费av一区二区三区| 国产免费av片在线观看野外av| 日日夜夜操网爽| 日韩欧美免费精品| 亚洲欧美精品综合一区二区三区| 免费人成视频x8x8入口观看|