Abstract
Abstract: Modern farming practises have the potential to feed the world's 7.6 billion inhabitants. Despite the availability of sufficient food, individuals continue to suffer from malnutrition. Plant diseases reduce both the amount and the quality of total production. Building an image processing model for prediction or classification applications presents several obstacles. We present a deep learning model for illness detection that makes use of CNN and Capsule Network (CapsNet). The backbone of image processing is a convolutional neural network (CNN). The new architecture called as CapsNet is suggested to reduce the limitations and to achieve greater performance than standard neural networks. In this project, we analyze CNN and CapsNet for tomato plant disease datasets. The performance of both the models are measured and analysed. As a result, this approach may be used to a variety of plants and on a huge scale. Keywords: Leaf disease detection; Convolutional Neural Network (CNN); Capsule Network (CapsNet); Performance Evaluation
Published Version
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