Deep Learning Techniques in Tomato Plant – A Review
S. Mohana Saranya,R. R. Rajalaxmi,R Prabavathi,T Suganya,S. Mohanapriya,T Tamilselvi +5 more
- Vol. 1767, Iss: 1, pp 012010
TLDR
The finding showed that DL techniques outperformed all other image processing techniques but DL performs mainly depends on the dataset used.Abstract:
Deep learning establishes an ongoing, modern technique for image processing with large potential and promising results. After proving its efficiency in various applications DL has also entered into the domain of agriculture. Here, we surveyed 38 research works that applied deep learning techniques to various research problems in tomato plant. We examine the areas of tomato plant research where deep learning is applied, data preprocessing techniques applied, transfer learning and augmentation techniques used. Studied dataset information like data sources used, number of images, classes and train test validation ratio applied. In addition, we study comparisons done on various deep learning architectures and discussed the outcome. The finding showed that DL techniques outperformed all other image processing techniques but DL performs mainly depends on the dataset used.read more
Citations
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What is an Expert System
TL;DR: This paper discusses expert system components including the knowledge base, the inference engine, user interface, and knowledge acuisition, advantages and disadvantages of expert systems.
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Style-Consistent Image Translation: A Novel Data Augmentation Paradigm to Improve Plant Disease Recognition
TL;DR: This study proposes a novel data augmentation paradigm that can adapt variations from one class to another, and leverages a prior mask as input to tell the area the authors are interested in and reuse the original annotations.
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Vegetable Size Measurement Based on Stereo Camera and Keypoints Detection
TL;DR: An intelligent method for vegetable recognition and size estimation based on object detection and stereo cameras is proposed that can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length.
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Identification of Navel Orange Diseases and Pests Based on the Fusion of DenseNet and Self-Attention Mechanism.
Yin’e Zhang,Yong Ping Liu +1 more
TL;DR: In this paper, the authors proposed a power mechanism fusion (DCPSNET) identification method of navel orange diseases and pests, which improves the traditional deep dense network DenseNet model to realize accurate and efficient identification of Navel Orange disease and pests.
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Synthetic Data Augmentation of Tomato Plant Leaf using Meta Intelligent Generative Adversarial Network: Milgan
TL;DR: The proposed research aims to develop more data using a Meta approach, which uses random sampling techniques, passes a few processed images to the generator component of GAN, and the system uses a discriminator component to classify the synthetic data created by the Meta-Learning Approach.
References
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Deep learning in agriculture: A survey
TL;DR: A survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges indicates that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.
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Journal ArticleDOI
Deep Learning for Tomato Diseases: Classification and Symptoms Visualization
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Tomato crop disease classification using pre-trained deep learning algorithm
TL;DR: The role of number of images and significance of hyperparameters namely minibatch size, weight and bias learning rate in the classification accuracy and execution time have been analyzed.
Journal ArticleDOI
Attention embedded residual CNN for disease detection in tomato leaves
TL;DR: Two different deep architectures for detecting the type of infection in tomato leaves are presented and the first architecture applies residual learning to learn significant features for classification and the second architecture applies attention mechanism on top of the residual deep network.