Journal ArticleDOI
Lightweight convolutional neural network model for field wheat ear disease identification
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TLDR
In this article, a lightweight convolutional neural network (CNN) model called SimpleNet was designed for the automatic identification of wheat ear diseases, such as glume blotch and scab, in natural scene images taken in the field.About:
This article is published in Computers and Electronics in Agriculture.The article was published on 2021-10-01. It has received 45 citations till now. The article focuses on the topics: Feature (computer vision) & Convolutional neural network.read more
Citations
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Journal ArticleDOI
Identification method of vegetable diseases based on transfer learning and attention mechanism
TL;DR: Zhang et al. as discussed by the authors developed a vegetable disease identification model, DTL-SE-ResNet50, optimized by SENet and pre-trained by ImageNet to form a new model, which was trained with the AI Challenger 2018 public database to obtain a new weight.
Journal ArticleDOI
Swin-MLP: a strawberry appearance quality identification method by Swin Transformer and multi-layer perceptron
Hao Zheng,Guohui Wang,Xuchen Li +2 more
TL;DR: The proposed Swin-MLP method, based on Swin Transformer and multi-layer perceptron (MLP) to identify the strawberry appearance quality has a good effect and provides a new solution for strawberry quality identification.
Journal ArticleDOI
GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases
Jianwu Lin,Xiaoyulong Chen,Renyong Pan,Tengbao Cao,Jitong Cai,Yang Chen,Xishun Peng,Tomislav Cernava,Xin Zhang +8 more
TL;DR: In this article , the authors proposed a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases, which consists of residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules.
Journal ArticleDOI
Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review
Ya-Hong Wang,Wen-Hao Su +1 more
TL;DR: A comprehensive review of CNNs in computer vision for grain crop phenotyping is provided in this article , where the main results of recent studies on crop phenotype detection are discussed and summarized.
Journal ArticleDOI
A multi-scale cucumber disease detection method in natural scenes based on YOLOv5
TL;DR: Zhang et al. as mentioned in this paper proposed MTC-YOLOv5n, which integrates the Coordinate Attention (CA) and Transformer in order to reduce invalid information interference in the background, and combines a multi-scale training strategy (MS) and feature fusion network to improve the small object detection accuracy.
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
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ImageNet classification with deep convolutional neural networks
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Densely Connected Convolutional Networks
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Rethinking the Inception Architecture for Computer Vision
TL;DR: In this article, the authors explore ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
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