基于改进的VGG-16模型的花卉识别小程序设计

由于花卉种类繁多,花卉的识别需要人们掌握深厚的植物学知识和长期观察的经验总结,而利用深度学习可实现花卉种类的智能识别。首先,通过迁移学习在视觉几何群网络(Visual Geometry Group Network,VGG-16)算法的基础上进行改进,实现花卉的识别;其次,将训练好的模型进行封装,上传至云服务器;最后,在云服务器上进行识别,通过超文本传输协议(Hyper Text Transfer Protocol,HTTP)与微信小程序进行通信,实现了拍照上传即可识别花卉种类和了解花卉特性的小程序设计。

Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision loss.Traditional methods of diagnosing and treating the disease are time-consuming and expensive.However,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic retinopathy.These advanced techniques offer higher accuracy and performance.ComputerAided Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease diagnoses.Overall,these technological advancements hold great potential for improving the management of diabetic retinopathy.The study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these classes.The robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its robustness.The outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance understanding.This study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of DR.The manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.

基于深度学习及改进模糊KMeans的寻常型银屑病智能诊断方法

为了解决寻常型银屑病在样本分布不平衡的数据中可能会导致的深度学习模型诊断效果下降等问题,通过结合改进模糊KMeans聚类算法对高聚类复杂度数据的处理能力以及Visual Geometry Group 13(VGG13)深度卷积神经网络模型的预测能力,提出一种基于改进模糊KMeans聚类算法的VGG13深度卷积神经网络(VGG13-KMeans)模型,并将其应用于寻常型银屑病的诊断任务中。实验结果表明,相较于VGG13以及ResNet18两种方法,本文方法更适用于对银屑病特征的识别。

Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life

The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.

基于深度学习的农田害虫识别研究

农田害虫降低了农作物的产量和质量,如何有效区分和治理农田害虫成为首要解决的问题。文章紧抓农田环境需求和农民对农作物的产量需求不匹配的痛点,基于卷积神经网络技术识别农田害虫,为农业提供有效的识别方式。采用MobileNetV1、残差神经网络(Residual Network,ResNet)50、视觉几何群网络(Visual Geometry Group Network,VGG)16以及微调预训练模型VGG16共4种网络模型二分类农田害虫图片集。由于样本数据量较少,为防止出现过拟合,使用了数据增强技术,即通过现有训练图片生成更多的训练图片,从而提高泛化能力。实验表明,4种网络模型的准确率分别为88.63%、91.73%、86.49%和90.13%,在农田害虫识别中均具有较好的实际应用效果。