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Enhancing Tea Leaf Disease Classification Using Vision Transformers for Precision Agriculture

https://doi.org/10.2139/ssrn.5346178
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TDDet: A novel lightweight and efficient tea disease detector
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A Novel Approach For the Detection of Tea Leaf Disease Using Deep Neural Network
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Identification of tea leaf diseases by using an improved deep convolutional neural network
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The global burden of pathogens and pests on major food crops
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Detection and identification of tea leaf diseases based on AX-RetinaNet
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Tea leaf disease detection and identification based on YOLOv7 (YOLO-T)
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Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar
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Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review
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Model Selection of Hybrid Feature Fusion for Coffee Leaf Disease Classification
inferredcites →https://doi.org/10.1109/access.2023.3326721openalex-snapshot
A Review of Leaf Diseases Detection and Classification by Deep Learning
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Rethinking the Inception Architecture for Computer Vision
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Deep Residual Learning for Image Recognition
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Xception: Deep Learning with Depthwise Separable Convolutions
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
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Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
inferredcites →https://doi.org/10.1109/iccv48922.2021.00986openalex-snapshot
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
inferredcites →https://doi.org/10.1109/icrest51555.2021.9331214openalex-snapshot
Transfer Learning Approach for Plant Leaf Disease Detection Using CNN with Pre-Trained Feature Extraction Method Mobilnetv2
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Image-based disease diagnosing and predicting of the crops through the deep learning mechanism
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Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks
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Plant disease and insect pest identification based on vision transformer
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Olive Disease Classification Based on Vision Transformer and CNN Models
inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
inferredcites →https://doi.org/10.3389/fpls.2016.01419openalex-snapshot
Using Deep Learning for Image-Based Plant Disease Detection
inferredcites →https://doi.org/10.3390/agronomy13051184openalex-snapshot
Transform and Deep Learning Algorithms for the Early Detection and Recognition of Tomato Leaf Disease
inferredcites →https://doi.org/10.3390/agronomy14112719openalex-snapshot
Crops Disease Detection, from Leaves to Field: What We Can Expect from Artificial Intelligence
inferredcites →https://doi.org/10.3390/app13105982openalex-snapshot
A Review of Plant Disease Detection Systems for Farming Applications
inferredcites →https://doi.org/10.3390/plants9101302openalex-snapshot
Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion
inferredcites →https://doi.org/10.3390/s23073751openalex-snapshot
Aggregating Different Scales of Attention on Feature Variants for Tomato Leaf Disease Diagnosis from Image Data: A Transformer Driven Study
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Multispectral Plant Disease Detection with Vision Transformer–Convolutional Neural Network Hybrid Approaches
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AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2010.11929openalex-snapshot
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
inferredcites →https://doi.org/10.48550/arxiv.2104.05704openalex-snapshot
Escaping the Big Data Paradigm with Compact Transformers
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Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review
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