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Pdcnet: A Lightweight Model for Plant Disease Detection

https://doi.org/10.2139/ssrn.4749751
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inferredcites →https://doi.org/10.1007/978-3-030-01264-9_8openalex-extracted
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
inferredcites →https://doi.org/10.1016/j.asoc.2023.110534openalex-extracted
Leaf disease detection using machine learning and deep learning: Review and challenges
inferredcites →https://doi.org/10.1016/j.biosystemseng.2018.05.013openalex-extracted
Factors influencing the use of deep learning for plant disease recognition
inferredcites →https://doi.org/10.1016/j.compag.2017.09.012openalex-extracted
An in-field automatic wheat disease diagnosis system
inferredcites →https://doi.org/10.1016/j.compag.2019.01.034openalex-extracted
PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network
inferredcites →https://doi.org/10.1016/j.compag.2019.104948openalex-extracted
Depthwise separable convolution architectures for plant disease classification
inferredcites →https://doi.org/10.1016/j.compag.2020.105341openalex-extracted
Crop leaf disease recognition based on Self-Attention convolutional neural network
inferredcites →https://doi.org/10.1016/j.compag.2020.105527openalex-extracted
Deep feature based rice leaf disease identification using support vector machine
inferredcites →https://doi.org/10.1016/j.compag.2020.105803openalex-extracted
Do we really need deep CNN for plant diseases identification?
inferredcites →https://doi.org/10.1016/j.compag.2022.107175openalex-extracted
MS-DNet: A mobile neural network for plant disease identification
inferredcites →https://doi.org/10.1016/j.compag.2022.107407openalex-extracted
CST: Convolutional Swin Transformer for detecting the degree and types of plant diseases
inferredcites →https://doi.org/10.1016/j.compag.2023.108070openalex-extracted
Automatic cotton spider mites' damage grading algorithm for depth residual network based on transfer learning
inferredcites →https://doi.org/10.1016/j.compag.2023.108072openalex-extracted
Plant image recognition with deep learning: A review
inferredcites →https://doi.org/10.1016/j.compag.2023.108121openalex-extracted
Spatial convolutional self-attention-based transformer module for strawberry disease identification under complex background
inferredcites →https://doi.org/10.1016/j.eswa.2021.114770openalex-extracted
Rice diseases detection and classification using attention based neural network and bayesian optimization
inferredcites →https://doi.org/10.1016/j.matpr.2023.02.370openalex-extracted
Agricultural plant diseases identification: From traditional approach to deep learning
inferredcites →https://doi.org/10.1109/access.2022.3141371openalex-extracted
Plant Disease Identification Using a Novel Convolutional Neural Network
inferredcites →https://doi.org/10.1109/csitss47250.2019openalex-extracted
2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)
inferredcites →https://doi.org/10.1109/cvpr52729.2023.01157openalex-extracted
Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks
inferredcites →https://doi.org/10.1109/cvpr52729.2023.01548openalex-extracted
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
inferredcites →https://doi.org/10.1109/hnicem.2018.8666365openalex-extracted
Mobile Platform Implementation of Lightweight Neural Network Model for Plant Disease Detection and Recognition
inferredcites →https://doi.org/10.1109/iccubea.2015.153openalex-extracted
Plant Disease Detection Using Image Processing
inferredcites →https://doi.org/10.1109/iccv48922.2021.00986openalex-extracted
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
inferredcites →https://doi.org/10.11591/ijece.v12i4.pp3642-3654openalex-extracted
A classification model based on depthwise separable convolutional neural network to identify rice plant diseases
inferredcites →https://doi.org/10.13164/mendel.2022.1.055openalex-extracted
Color-Aware Two-Branch DCNN for Efficient Plant Disease Classification
inferredcites →https://doi.org/10.3389/fpls.2017.01852openalex-extracted
Deep Learning for Image-Based Cassava Disease Detection
inferredcites →https://doi.org/10.3389/fpls.2021.723294openalex-extracted
Identification of Apple Leaf Diseases by Improved Deep Convolutional Neural Networks With an Attention Mechanism
inferredcites →https://doi.org/10.3390/agriculture12020228openalex-extracted
A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification
inferredcites →https://doi.org/10.3390/bios5030537openalex-extracted
Current and Prospective Methods for Plant Disease Detection
inferredcites →https://doi.org/10.3390/plants12142642openalex-extracted
Plant-CNN-ViT: Plant Classification with Ensemble of Convolutional Neural Networks and Vision Transformer
inferredcites →https://doi.org/10.48550/arxiv.1511.08060openalex-extracted
An open access repository of images on plant health to enable the development of mobile disease diagnostics
inferredcites →https://doi.org/10.48550/arxiv.1602.07360openalex-extracted
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
inferredcites →https://doi.org/10.48550/arxiv.1608.03983openalex-extracted
SGDR: Stochastic Gradient Descent with Warm Restarts
inferredcites →https://doi.org/10.48550/arxiv.1704.04861openalex-extracted
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.48550/arxiv.1711.05101openalex-extracted
Decoupled Weight Decay Regularization
inferredcites →https://doi.org/10.48550/arxiv.1906.02629openalex-extracted
When Does Label Smoothing Help?
inferredcites →https://doi.org/10.48550/arxiv.2010.11929openalex-extracted
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
inferredcites →https://doi.org/10.52202/068431-0940openalex-extracted
EfficientFormer: Vision Transformers at MobileNet Speed
inferredcites →https://doi.org/10.52202/075280-2140openalex-extracted
Symbolic Discovery of Optimization Algorithms
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