为了能准确地识别植物的生长阶段从而实现智慧植物补光,设计一套能识别植物种类和生长阶段的智慧植物补光系统,其中识别植物生长阶段以ResNet18模型进行改进,用深度可分离卷积代替传统卷积,并引入SE模块(squeeze and excitation module...为了能准确地识别植物的生长阶段从而实现智慧植物补光,设计一套能识别植物种类和生长阶段的智慧植物补光系统,其中识别植物生长阶段以ResNet18模型进行改进,用深度可分离卷积代替传统卷积,并引入SE模块(squeeze and excitation module)来提高模型任务处理的效率和准确性,结合早停法和学习率衰减机制来训练,避免过拟合。以番茄为研究对象进行验证,识别其幼苗期、开花坐果期和果实成熟期。结果表明,改进ResNet18模型的识别准确率达到了96.57%,比原模型提高了4.93个百分点,单张识别时间为0.27 s,比原模型快了0.30 s,模型体积为原模型的14%,同时,改进后的模型在测试集准确率、参数量和Macro F1得分等方面都优于ResNet18、ResNet34、AlexNet和VGG16四种卷积神经网络。最后,将改进ResNet18模型应用于植物补光系统,实际识别番茄生长阶段的准确率达到了96.49%,并能输出预期的光谱。该系统能精准地识别植物种类及其生长阶段,从而调用匹配植物及其生长阶段的光配方,达到智慧补光的目的。展开更多
The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)an...The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.展开更多
文摘The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.
