Journal Article10.1016/J.ASOC.2019.105933
Attention embedded residual CNN for disease detection in tomato leaves
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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.
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About: This article is published in Applied Soft Computing. The article was published on 01 Jan 2020. The article focuses on the topics: Plant disease & Convolutional neural network.
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References
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
TL;DR: A new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks, which is able to recognize 13 different types of plant diseases out of healthy leaves.
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition
TL;DR: A deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions, and combines each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network.
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Plant disease identification from individual lesions and spots using deep learning
TL;DR: The use of individual lesions and spots for the task, rather than considering the entire leaf, allows the identification of multiple diseases affecting the same leaf and indicates that, as long as enough data is available, deep learning techniques are effective for plant disease detection and recognition.
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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.
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Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm
Melike Sardogan,Adem Tuncer,Yunus Ozen +2 more
- 01 Sep 2018
TL;DR: A Convolutional Neural Network model and Learning Vector Quantization algorithm based method for tomato leaf disease detection and classification and results validate that the proposed method effectively recognizes four different types of tomato leaf diseases.
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