Authors: Kushal M U, Mrs Nikitha S, Shashank L M, Partha Sarathi S, Maruthi M N
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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.
In recent years, the use of computer vision techniques for image processing in precision agriculture has grown dramatically. Tomatoes are one of India's most extensively grown crops. Tomatoes are a crucial vegetable crop in terms of both income and nutrition. Tomatoes are often grown in the summer, although they may be grown all year. Most farmers are depended on tomato crop. Tomato crops, however, are plagued by diseases. Pests and diseases damage more than half of all vegetation, according to surveys. Climate change, a shortage of organic fertilisers, and other factors all contribute to plant disease. Small-scale farmers undertake the majority of farming, and they are often uninformed of several plant diseases.
Computer vision and image recognition tasks have come a long way in the last few years. As a result, a convolutional neural network is the ideal option for a task like plant disease diagnosis. Convolutional neural networks (CNN) are one of the most popular models used today. But, the spatial link between an object's lower and upper level properties is ignored by CNN architecture functions. That is, they have a tendency to lose information in the data intake, regarding feature positions, spatial relationships between features, and feature orientations. As a result, a deep learning model capable of performing classification tasks with greater performance on small datasets is required. CNN architectures also fail to describe an object's equivariance, which is why proposed a new deep learning model called Capsule Network (CapsNet), which is a neuronal network that designs the hierarchical connections of characteristics or attributes. The pose (position, size) and other features of this item are among them.
II. LITERATURE SURVEY
III. RELATED WORKS
We cover relevant efforts in classification challenges using deep learning architectures in this part. In general, deep learning approaches have been intensively investigated for object identification and picture classification applications. Convolutional Neural Networks (CNNs) are a deep learning technique that has attained state-of-the-art performance in image classification when applied to recognition and classification problems. The tomato disease dataset was used to assess the first CNN architecture termed MobileNet for object identification. The implementation of pre-trained CNN architectures namely VGG16, MobileNet, and ResNet50, to diagnose the tomato leaf disease severity from Tomato leaf images, and the performance is increased by extracting the features of ResNet50 to the classical CNN model. CNN architectures are inefficient for geometric transformations and do not take into account the spatial relationships between the image's components. The max-pooling layer in CNN has a propensity to lose information while routing features from one layer to another. They are unable to model an object's rotation invariance. The section introduces a Capsule Network with Dynamic Routing algorithm to address the drawbacks of CNN architecture. The studies have employed capsule networks on medical imaging for illness categorization, and they have obtained higher accuracy than traditional CNN.
Even though multiple fertilizers and chemicals are present, due to lack of knowledge and instructions to use, it fails to overcome diseases. Farmers pay a lot of money to hire plant pathologists who manually inspect the crops\' leaves for diseases and propose management measures. This approach is prone to ignorance and partiality, necessitating the use of Artificial Intelligence algorithms to diagnose these disorders automatically. The input layer, convolution layer, principal capsule layer, and digitcaps layer are used to justify the Capsule Network model. We\'re constructing CNN architectural variations (CNN learned from scratch, MobileNet, VGG16, and ResNet50) to see how they compare to the Capsule Network model. This research is confined to 10 different types of tomato leaf disease, and future work will entail developing a robust capsule network model that can handle diseases from a variety of plant species.
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Copyright © 2022 Kushal M U, Mrs Nikitha S, Shashank L M, Partha Sarathi S, Maruthi M N. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.