Using Tensor Processing Units to enhance the training of Convolutional Neural Networks in Multiclass Classification
Keywords:
Ayurvedic Plant Leaf Images, Custom Leaf Image Dataset,, Computer Vision, Multiclass Classification, Convolution Neural Networks, Pre-trained image recognition models, Tensor Processing UnitAbstract
Ayurveda is the ancient medical science of India. Many of the Ayurvedic medicines are plant-based. There is growing interest in Ayurveda nowadays and hence there is a need to make it more relevant to the current era by using modern technologies in Ayurveda. Identification of required Ayurvedic plants among a host of other plants is a problem that can be solved using computer vision-based technology. In this research work, classification of plants is done using Convolution Neural Networks (CNN) on the images and labels of respective leaves. A leaf image dataset for seven different Ayurvedic plants has been created as a part of this research work. Keras deep learning framework with TensorFlow as the backend is used for building the CNN model. The platform used for training this model was Google Colab which is available free of cost. As a part of this research work, Method and program are developed to load data from normal storage like Google drive, to TPU. After loading the image data to TPU, the TPU hardware acceleration is used for training the CNN model. In addition to a custom CNN model developed from scratch, pretrained image recognition models like DenseNet201, EfficientNetB7, InceptionV3, ResNet50V2, VGG19 and Xception are leveraged as a part of this research work. Image recognition accuracies ranging from 95% to 100% have been achieved using the mentioned CNN training methods for plant leaf image classification.
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