Classification of Precipitation Types Using Fall Velocity–Diameter Relationships from 2D-Video Distrometer Measurements

Fall velocity–diameter relationships for four different snowflake types（dendrite,plate,needle,and graupel） were investigated in northeastern South Korea,and a new algorithm for classifying hydrometeors is proposed for distrometric measurements based on the new relationships.Falling ice crystals（approximately 40 000 particles） were measured with a two-dimensional video disdrometer（2DVD） during a winter experiment from 15 January to 9 April 2010.The fall velocity–diameter relationships were derived for the four types of snowflakes based on manual classification by experts using snow photos and 2DVD measurements：the coefficients（exponents） for different snowflake types were 0.82（0.24） for dendrite,0.74（0.35） for plate,1.03（0.71） for needle,and 1.30（0.94） for graupel,respectively.These new relationships established in the present study（PS） were compared with those from two previous studies.Hydrometeor types were classified with the derived fall velocity–diameter relationships,and the classification algorithm was evaluated using 3 × 3 contingency tables for one rain–snow transition event and three snowfall events.The algorithm showed good performance for the transition event：the critical success indices（CSIs） were 0.89,0.61 and 0.71 for snow,wet-snow and rain,respectively.For snow events,the algorithm performance for dendrite and plate（CSIs = 1.0 and 1.0,respectively） was better than for needle and graupel（CSIs = 0.67 and 0.50,respectively）.

Classification of CBERS-2 Imagery with Fuzzy ARTMAP Classifier

A fuzzy ARTMAP classifier is adopted for a classification experiment of CBERS-2 imagery. The fundamental theory and processing about the algorithm are first introduced, followed with a land-use classification experiment in Shihezi County on CBERS-2 high resolution imagery. Three classifiers are compared: maximum likelihood classifier (MLC), error back propagation (BP) classifier, and fuzzy ARTMAP classifier. The comparison shows comparably better results for the fuzzy ARTMAP classifier, with overall classification accuracy of 9.9% and 4.6% higher than that of MLC and BP. The results also prove that the fuzzy ARTMAP classifier has better discernment in identifying bare soil on CBERS-2 imagery.

Accurate Classification of P2P Traffic by Clustering Flows

P2P traffic has always been a dominant portion of Internet traffic since its emergence in the late 1990s. The method used to accurately classify P2P traffic remains a key problem for Internet Service Producers （ISPs） and network managers. This paper proposes a novel approach to the accurate classification of P2P traffic at a fine-grained level, which depends solely on the number of special flows during small time intervals. These special flows, named Clustering Flows （CFs）, are de- fined as the most frequent and steady flows generated by P2P applications. Hence we are able to classify P2P applications by detecting tlle appearance of corresponding CFs. Com- pared to existing approaches, our classifier can realise high classification accuracy by ex- ploiting only several generic properties of flows, instead of extracting sophisticated fea- tures from host behaviours or transport layer data. We validate our framework on a large set of P2P traffic traces using a Support Vector Machine （SVM）. Experimental results show that our approach correctly classifies P2P ap- plications with an average true positive rate of above 98% and a negligible false positive rate of about 0.01%.

P2P Streaming Traffic Classification in High-Speed Networks

The growing P2P streaming traffic brings a variety of problems and challenges to ISP networks and service providers.A P2P streaming traffic classification method based on sampling technology is presented in this paper.By analyzing traffic statistical features and network behavior of P2P streaming,a group of flow characteristics were found,which can make P2P streaming more recognizable among other applications.Attributes from Netflow and those proposed by us are compared in terms of classification accuracy,and so are the results of different sampling rates.It is proved that the unified classification model with the proposed attributes can identify P2P streaming quickly and efficiently in the online system.Even with 1:50 sampling rate,the recognition accuracy can be higher than 94%.Moreover,we have evaluated the CPU resources,storage capacity and time consumption before and after the sampling,it is shown that the classification model after the sampling can significantly reduce the resource requirements with the same recognition accuracy.

EfficientNetV2 Model for Plant Disease Classification and Pest Recognition

Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research.To perform the investigation on Plant and pest classification,Transfer Learning(TL)approach is used on EfficientNet-V2.TL requires limited labelled data and shorter training time.However,the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters.EfficientNet-V2 is a Convolutional Neural Network(CNN)model with significant high speed learning rates across variable sized datasets.The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning capacity.This provides a better interpretability of the model’s understanding on the test domains.With these insights,our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach.This Progressive Learning for TL(PL-TL)is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species,5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms,though it is not a benchmark dataset.To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms,Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes.Also,the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3,and Vision Transformer a non-CNN model.It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%,97.5%,80.1%on Cassava,PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy,precision,f1-score is also studied.

Comparative Study of the Geomagnetic Activity Effect on foF2 Variation as Defined by the Two Classification Methods at Dakar Station over Solar Cycle Phases

This paper aims to establish a comparison between both geomagnetic activity classification methods on foF2 diurnal variation over solar cycle phases. It concerns first a comparison of geomagnetic activity occurrences according to both classification methods;and second the geomagnetic effect on foF2 diurnal variation profiles as defined for the equatorial latitudes. The occurrences of the different disturbed geomagnetic activities (recurrent activity (RA), shock activity (SA) and fluctuant activity (FA)) according to both classifications (ancient classification (AC) and new classification (NC)) have been studied at Dakar ionosonde station (Lat: 14.8°N;Long: 342.6°E). Regarding both classifications, the RA occurs more during the decreasing phase. And it’s observed that the RA occurs the most during the increasing phase for the AC and during the minimum phase for the NC. The maximum gap of occurrence () between both classifications is -11.1% (for the negative value which is observed during the increasing phase) and +16.74% (for the positive one which is observed during the decreasing phase). The occurrence of the SA in relation with both classifications is the lowest during the minimum phase and the maximum occurrence is observed during the maximum and decreasing phases, for the AC, with a value close to 37% and for the NC at the maximum phase with a percentage of 54.47%. The maximum gap of occurrence () between both classifications is -17.85% (for the negative value which is observed at maximum phase) and +13.53% (for the positive one which is observed during the decreasing phase). For both classifications, the FA occurs the least during the minimum phase and the most during the maximum phase for the AC and at maximum and decreasing phases with percentage values of occurrence of roughly 37% for the NC. The maximum gap of occurrence () between both classifications is -10% (for the negative value which is observed during the decreasing phase) and +20.11% (for the positive one which is observed during the maximum phase). foF2 diurnal profiles throughout solar cycle phases concerning the AC and the NC have been compared. The FA diurnal profiles don’t present a difference. The RA and the SA present a difference during minimum and increasing phases and the least at maximum and decreasing phases.

Applications Classification of VPN Encryption Tunnel Based on SAE-2dCNN Model

How to quickly and accurately identify applications in VPN encrypted tunnels is a difficult technique.Traditional technologies such as DPI can no longer identify applications in VPN encrypted tunnel.Various VPN protocols make the feature engineering of machine learning extremely difficult.Deep learning has the advantages that feature extraction does not rely on manual labor and has a good early application in classification.This article uses deep learning technology to classify the applications of VPN encryption tunnel based on the SAE-2dCNN model.SAE can effectively reduce the dimensionality of the data,which not only improves the training efficiency of 2dCNN,but also extracts more precise features and improves accuracy.This paper uses the most common VPN encryption data in the real network to train and test the model.The test results verify the effectiveness of the SAE-2dCNN model.

基于Gaofen-2影像和面向对象的椰子林分类研究

椰子是重要的热带经济作物,海南椰子种植面积占全国的90%以上。快速获取椰子种植面积及其空间分布信息对热带作物产业规划具有十分重要的作用。本研究基于国产Gaofen-2高分辨率卫星影像,以文昌市东郊镇为试验区,开展椰子林遥感分类研究。基于最优分割尺度的面向对象分类方法,选取4个光谱特征、5个植被指数和32个纹理特征为辅助参量,构建了4种不同的面向对象分类组合(光谱特征、光谱特征+纹理特征组合、光谱特征+植被指数组合、光谱特征+纹理特征+植被指数特征组合)进行椰子林分类提取,并与基于像元的椰子林分类结果进行对比分析。结果表明:(1)仅采用基于像元分类方法,椰子林的总体分类精度(overall accuracy,OA)和用户精度(user’s accuracy,UA)分别达到87.05%和85.21%。(2)相比基于像元分类,4种面向对象分类组合的OA值提高了5.51%~8.72%。(3)光谱特征和纹理特征组合提取椰子林分类结果最优,OA值和UA值分别达到95.77%和97.15%;光谱特征和植被指数的组合也得到了较好的分类结果,OA值和UA值分别为94.88%和94.42%;所有的光谱特征、植被指数和纹理特征全部参与分类得到的OA值和UA值分别为94.67%和94.17%,低于仅使用光谱特征或者植被指数的组合。综上,国产高分辨率Gaofen-2影像在椰子林遥感精准识别中具有很大的潜力,结合纹理特征的面向对象分类方法可以更准确地提取椰子林分类信息,研究结果可为多云多雨地区大尺度椰子林遥感识别提供技术参考。

基于哨兵2号数据的撂荒地识别与分析——以甘肃省麦积区为例

以甘肃省麦积区为研究区域,采用哨兵2号遥感卫星数据,并基于面向对象的方法对该区域的撂荒地进行了识别,分类总体精度达到92%,Kappa系数为0.82。空间统计结果显示,麦积区的撂荒地面积为12600.31 hm^(2),占麦积区总面积的3.62%,占耕地总面积的22.02%。坡度分析和交通条件分析发现,地形因素和交通条件是导致撂荒的重要原因。对麦积区的撂荒地进行空间自相关分析发现,撂荒地存在显著的空间集聚特征。

Deep learning based classification of rock structure of tunnel face

The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a result of different regional rock types,as well as in-situ conditions(e.g.,temperature,humidity,and construction procedure),previous automated methods have limited performance in classification of rock structure of tunnel face during construction.This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks(CNN),namely Inception-ResNet-V2(IRV2).A prototype recognition system is implemented to classify 5 types of rock structures including mosaic,granular,layered,block,and fragmentation structures.The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images.Furthermore,different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter.Among all the discussed models,i.e.,ResNet-50,ResNet-101,and Inception-v4,Inception-ResNet-V2 exhibits the best performance in terms of various indicators,such as precision,recall,F-score,and testing time per image.Meanwhile,the model trained by a large database can obtain the object features more comprehensively,leading to higher accuracy.Compared with the original image classification method,the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence.The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face.

An Improved Convolutional Neural Network Model for DNA Classification

Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)classification.In any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature reduction.The downsampling is an improved form of the existing pooling layer to obtain better classification accuracy.The two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for downsampling.They convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature vectors.However,there are parameters which directly affect how a CNN model is trained.In this paper,some issues concerned with the training of CNNs have been handled.The CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of epochs.Training and assessment of the performance of CNNs are carried out on 16S rRNA bacterial sequences.Simulation results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

An Optimized Deep Residual Network with a Depth Concatenated Block for Handwritten Characters Classification

Even though much advancements have been achieved with regards to the recognition of handwritten characters,researchers still face difficulties with the handwritten character recognition problem,especially with the advent of new datasets like the Extended Modified National Institute of Standards and Technology dataset(EMNIST).The EMNIST dataset represents a challenge for both machine-learning and deep-learning techniques due to inter-class similarity and intra-class variability.Inter-class similarity exists because of the similarity between the shapes of certain characters in the dataset.The presence of intra-class variability is mainly due to different shapes written by different writers for the same character.In this research,we have optimized a deep residual network to achieve higher accuracy vs.the published state-of-the-art results.This approach is mainly based on the prebuilt deep residual network model ResNet18,whose architecture has been enhanced by using the optimal number of residual blocks and the optimal size of the receptive field of the first convolutional filter,the replacement of the first max-pooling filter by an average pooling filter,and the addition of a drop-out layer before the fully connected layer.A distinctive modification has been introduced by replacing the final addition layer with a depth concatenation layer,which resulted in a novel deep architecture having higher accuracy vs.the pure residual architecture.Moreover,the dataset images’sizes have been adjusted to optimize their visibility in the network.Finally,by tuning the training hyperparameters and using rotation and shear augmentations,the proposed model outperformed the state-of-the-art models by achieving average accuracies of 95.91%and 90.90%for the Letters and Balanced dataset sections,respectively.Furthermore,the average accuracies were improved to 95.9%and 91.06%for the Letters and Balanced sections,respectively,by using a group of 5 instances of the trained models and averaging the output class probabilities.

Chinese News Text Classification Based on Convolutional Neural Network

With the explosive growth of Internet text information,the task of text classification is more important.As a part of text classification,Chinese news text classification also plays an important role.In public security work,public opinion news classification is an important topic.Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time.This paper introduces a combinedconvolutional neural network text classification model based on word2vec and improved TF-IDF:firstly,the word vector is trained through word2vec model,then the weight of each word is calculated by using the improved TFIDF algorithm based on class frequency variance,and the word vector and weight are combined to construct the text vector representation.Finally,the combined-convolutional neural network is used to train and test the Thucnews data set.The results show that the classification effect of this model is better than the traditional Text-RNN model,the traditional Text-CNN model and word2vec-CNN model.The test accuracy is 97.56%,the accuracy rate is 97%,the recall rate is 97%,and the F1-score is 97%.

甘油三酯葡萄糖乘积指数、纤维蛋白原对2型糖尿病患者并发糖尿病足溃疡的预测价值

目的探讨甘油三酯葡萄糖乘积指数(TyG)、纤维蛋白原(FIB)对2型糖尿病患者并发糖尿病足溃疡(DFU)的预测价值。方法回顾性分析358例2型糖尿病患者的临床资料,将138例未并发DFU的患者作为T2DM组,220例并发DFU的患者作为DFU组,进一步根据Wagner分级将DFU组患者分为轻度DFU组(Wagner 1~2级)106例和重度DFU组(Wagner 3~5级)114例。采用多因素Logistic回归模型分析2型糖尿病患者并发DFU的影响因素,绘制受试者工作特征(ROC)曲线评估TyG、FIB对2型糖尿病患者并发DFU的预测价值,采用Spearman相关法分析TyG、FIB与DFU组患者Wagner分级的相关性。结果多因素Logistic回归分析结果显示,TyG、FIB、2型糖尿病病程是2型糖尿病患者并发DFU的独立危险因素(P<0.05)。ROC曲线分析结果显示,TyG、FIB单独和联合预测2型糖尿病患者并发DFU的曲线下面积(AUC)分别为0.601、0.847、0.873,联合检测的AUC更大。Spearman相关分析结果显示,DFU组患者的TyG、FIB与Wagner分级均呈正相关(P<0.05)。结论TyG、FIB是2型糖尿病患者并发DFU的独立危险因素,二者可用于评估DFU病情严重程度,对2型糖尿病患者并发DFU有一定的预测价值,且联合检测的效能更高。

2型糖尿病住院患者核心护理标准化语言的构建

目的构建2型糖尿病住院患者核心护理标准化语言,为规范临床护理记录提供参考。方法聚焦2型糖尿病住院患者的护理,通过文献检索、回顾医院护理记录及专家咨询确定2型糖尿病住院患者核心护理诊断,通过“国际北美护理诊断协会分类(North American Nursing Diagnosis Association International,NANDA-Ⅰ)、护理措施分类(Nursing Interventions Classification,NIC)、护理结局分类(Nursing Outcomes Classification,NOC)”(简称NNN链接)初步筛选核心护理诊断所匹配的核心护理结局、护理指标及核心护理措施、护理活动,邀请21名专家进行2轮函询进行修订完善,最终形成2型糖尿病住院患者核心护理标准化语言。结果2型糖尿病住院患者核心护理标准化语言包括6项护理诊断、11项护理结局(52项护理指标)、15项护理措施(126项护理活动)。结论本研究构建的2型糖尿病住院患者核心护理标准化语言符合我国国情、文化背景、语言理解及临床实践,具有专业性、科学性,有利于实现护理语言的标准化,提升糖尿病专科护理质量。

基于Sentinel-2时序数据的广东省英德市茶园分类研究

茶叶是一种高附加值的经济作物,是我国南方山区乡村振兴的主要抓手。由于毁林种茶等破坏行为,导致森林资源破坏并引发水土流失等生态环境问题。快速准确获取茶园的空间分布对于政府监管和茶叶产业的规划发展至关重要。由于研究区天气多阴雨,茶园分布较为分散,与森林等植被光谱较为接近等原因,导致基于卫星影像提取茶园挑战性较大。为了摸清英德市的茶园空间分布,系统分析了中高分辨率的多光谱Sentinel-2影像数据,结合多时序多特征信息在茶园提取中的应用潜力。以英德市全境为研究区,选用2019年—2021年的9期Sentinel-2影像数据,详细分析了茶树生长的物候特征,进一步探究了茶园和其他地类在多时序中的特征变化,采用Relief算法对所有特征进行重要性排序。根据特征排序结果,选取特征权重值加权90%的特征因子,即7个植被指数特征和2个纹理特征,通过不同的组合排序构建了9种茶园分类场景,采用RF算法对所有分类场景进行精度评价,选取最佳分类场景,进一步探讨了RF分类算法和SVM分类算法对茶园提取的可行性。结果表明:(1)在进行英德市茶园提取时,2月和10月是采用多时相构造茶园多特征的最佳组合,可能因2月茶树处于萌芽期长出部分嫩绿的新叶易于和森林植被区分且在10月前后由于茶园进行了修剪其特征也较明显,因此两时相特征融合易于区分茶园。(2)RF分类方法与SVM分类方法相比,后者的精度较高,其总体精度达到91.56%,Kappa系数为0.89,生产者精度和用户精度分别为80.22%和84.56%。该研究为快速高效获取英德市茶园空间分布信息提供了一种高效的方法,同时为政府在进行茶叶产业规划、管理提供了数据支持。

血清25羟维生素D与气阴两虚2型糖尿病合并甲状腺结节TI-RADS分级相关性分析

目的分析血清25羟维生素D[25(OH)D]水平与气阴两虚2型糖尿病合并甲状腺结节TI-RADS(甲状腺影像报告与数据系统)分级之间的关系,为精准筛查甲状腺结节患者提供依据。方法收集2021年3月—2023年3月期间在北部战区总医院内分泌科住院的气阴两虚2型糖尿病患者,根据甲状腺超声结果及TI-RADS分级情况,将单纯气阴两虚2型糖尿病不合并甲状腺结节的设为非结节组,将气阴两虚2型糖尿病合并甲状腺结节的设为结节组,其中TI-RADS 2~3类为良性结节组,TI-RADS 4类及以上为可疑恶性结节组,比较3组血清25(OH)D水平,并分析血清25(OH)D与甲状腺结节分级的相关性。结果结节组与非结节组相比,性别、吸烟、高血压、糖尿病病程、血清镁、TgAb比较,差异有统计学意义(P<0.05)。Logistic回归分析结果显示:高血压、糖尿病病程、血肌酐为糖尿病合并甲状腺结节的影响因素。血清25(OH)D与总胆固醇、甘油三酯、甲状旁腺激素(PTH)、甲状腺TI-RADS分级呈负相关(P<0.05),与高密度脂蛋白胆固醇(HDL-C)呈正相关(P<0.05)。结论25(OH)D与气阴两虚2型糖尿病合并甲状腺结节患者TI-RADS分级呈负相关。

Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network

Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people,and its high number of deaths also by 7%.For that purpose,a proposed model of several stages was developed.The first stage is optimizing the images using dynamic adaptive histogram equalization,performing a semantic segmentation using DeepLabv3Plus,then augmenting the data by flipping it horizontally,rotating it,then flipping it vertically.The second stage builds a custom convolutional neural network model using several pre-trained ImageNet.Finally,the model compares the pre-trained data to the new output,while repeatedly trimming the best-performing models to reduce complexity and improve memory efficiency.Several experiments were done using different techniques and parameters.Accordingly,the proposed model achieved an average accuracy of 99.6%and an area under the curve of 0.996 in the Covid-19 detection.This paper will discuss how to train a customized intelligent convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.

An Enhanced Deep Learning Method for Skin Cancer Detection and Classification

The prevalence of melanoma skin cancer has increased in recent decades.The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins.Thus,the early diagnosis of melanoma is a key factor in improving the prognosis of the disease.Deep learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light images.Such systems can provide early and accurate diagnoses of melanoma and other types of skin diseases.This paper proposes a new method which can be used for both skin lesion segmentation and classification problems.This solution makes use of Convolutional neural networks(CNN)with the architecture two-dimensional(Conv2D)using three phases:feature extraction,classification and detection.The proposed method is mainly designed for skin cancer detection and diagnosis.Using the public dataset International Skin Imaging Collaboration(ISIC),the impact of the proposed segmentation method on the performance of the classification accuracy was investigated.The obtained results showed that the proposed skin cancer detection and classification method had a good performance with an accuracy of 94%,sensitivity of 92%and specificity of 96%.Also comparing with the related work using the same dataset,i.e.,ISIC,showed a better performance of the proposed method.

Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality losses.Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural production.This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational complexity.The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy.Using transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant diseases.To evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,respectively.In identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.