matlab卷积识别花卉,应用卷积神经网络识别花卉及其病症
Abstract：With the development of flower market and industry，the number of people who cultivate and admire flowers continues to increase.How to identify flower categories and their diseases quickly and automatically has received widespread attention，since it is extremely convenient for the flower management in home gardening or large plantations.At present，there has been a lot of research work on computer vision and image recognition.The convolutional neural network research has achieved a major breakthrough，whose application in flower classification has also made great progress.However，research on flower disease identification is still relatively insufficient，so is the data set.Aiming at 10 species of common ornamental flowers，we provide an image data set including 16 leaf diseases of four species of flowers.What’s more，we design and implement a classification model based on the convolutional neural network combining with methods such as image pre-processing，network multi-input，data augmentation，batch normalization and so on，and integrate it into a tool for flower category and disease classification.The result of the experiments shows that the designed classification model has a high illness recognition accuracy of 88.2%，and achieved a better one of 94.4% combining with transfer learning，which is higher at least 27.0% than the classification model based on the support vector machine.
相关知识
应用卷积神经网络识别花卉及其病症
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