Journal ArticleDOI
Three-channel convolutional neural networks for vegetable leaf disease recognition
TLDR
The proposed three-channel convolutional neural networks model can automatically learn the representative features from the complex diseased leaf images, and effectively recognize vegetable diseases.About:
This article is published in Cognitive Systems Research.The article was published on 2019-01-01. It has received 114 citations till now. The article focuses on the topics: Convolutional neural network & Plant disease.read more
Citations
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Proceedings ArticleDOI
Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm
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.
Journal ArticleDOI
Plant diseases and pests detection based on deep learning: a review.
Jun Liu,Xuewei Wang +1 more
TL;DR: In this article, the authors provide a definition of plant diseases and pests detection problem, and put forward a comparison with traditional plant disease and pest detection methods, and discuss possible challenges and research ideas for the challenges, and several suggestions are given.
Journal ArticleDOI
Convolutional Neural Networks for the Automatic Identification of Plant Diseases.
TL;DR: This work surveys 19 studies that relied on CNNs to automatically identify crop diseases, describing their profiles, their main implementation aspects and their performance, and provides guidelines to improve the use of CNNs in operational contexts.
Journal ArticleDOI
Performance Analysis of Deep Learning CNN Models for Disease Detection in Plants using Image Segmentation
TL;DR: This research work investigates a potential solution to food security for the 7 billion people on earth by using segmented image data to train the convolutional neural network (CNN) models, and shows that the confidence of self-classification for S-CNN model improves significantly over F-CNN.
Journal ArticleDOI
Recent advances in image processing techniques for automated leaf pest and disease recognition – A review
TL;DR: A comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques and reports that recent efforts have focused on the use of deep learning instead of training shallow classifiers using hand-crafted features.
References
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Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Proceedings ArticleDOI
Multi-column deep neural networks for image classification
TL;DR: In this paper, a biologically plausible, wide and deep artificial neural network architectures was proposed to match human performance on tasks such as the recognition of handwritten digits or traffic signs, achieving near-human performance.
Journal ArticleDOI
Using Deep Learning for Image-Based Plant Disease Detection
TL;DR: In this article, a deep convolutional neural network was used to identify 14 crop species and 26 diseases (or absence thereof) using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions.
Journal ArticleDOI
Convolutional neural networks for speech recognition
TL;DR: It is shown that further error rate reduction can be obtained by using convolutional neural networks (CNNs), and a limited-weight-sharing scheme is proposed that can better model speech features.