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A Non-Destructive Method for Identification of Tea Plant Cultivars Based on Deep Learning

https://doi.org/10.3390/f14040728
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/mnfr.200500109openalex-snapshot
Tea, obesity, and diabetes
inferredcites →https://doi.org/10.1007/978-3-030-01264-9_8openalex-snapshot
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
inferredcites →https://doi.org/10.1007/s12161-011-9319-9openalex-snapshot
Discrimination of Green Teas from Different Geographical Origins by Using HS-SPME/GC–MS and Pattern Recognition Methods
inferredcites →https://doi.org/10.1016/j.arabjc.2014.11.048openalex-snapshot
Comparative study of the anti-oxidant activity of the total polyphenols extracted from Hibiscus Sabdariffa L., Glycine max L. Merr., yellow tea and red wine through reaction with DPPH free radicals
inferredcites →https://doi.org/10.1016/j.compag.2021.106547openalex-snapshot
Detection and classification of tea buds based on deep learning
inferredcites →https://doi.org/10.1016/j.compeleceng.2021.107023openalex-snapshot
Detection and severity analysis of tea leaf blight based on deep learning
inferredcites →https://doi.org/10.1016/j.foodchem.2010.05.007openalex-snapshot
Characterisation of tea leaves according to their total mineral content by means of probabilistic neural networks
inferredcites →https://doi.org/10.1016/j.foodchem.2015.08.054openalex-snapshot
Simultaneous determination of free amino acids in Pu-erh tea and their changes during fermentation
inferredcites →https://doi.org/10.1016/j.foodchem.2021.129372openalex-snapshot
Intelligent evaluation of taste constituents and polyphenols-to-amino acids ratio in matcha tea powder using near infrared spectroscopy
inferredcites →https://doi.org/10.1016/j.saa.2012.10.052openalex-snapshot
Differentiation of tea varieties using UV–Vis spectra and pattern recognition techniques
inferredcites →https://doi.org/10.1016/j.suscom.2019.100353openalex-snapshot
Identification of tea leaf diseases by using an improved deep convolutional neural network
inferredcites →https://doi.org/10.1021/jf0106143openalex-snapshot
Differentiation of Tea ( Camellia sinensis ) Varieties and Their Geographical Origin According to their Metal Content
inferredcites →https://doi.org/10.1039/a909219fopenalex-snapshot
HPLC determination of catechins and caffeine in tea. Differentiation of green, black and instant teas
inferredcites →https://doi.org/10.1039/d0ja00317dopenalex-snapshot
A modified genetic algorithm optimized SVM for rapid classification of tea leaves using laser-induced breakdown spectroscopy
inferredcites →https://doi.org/10.1080/10408398.2010.508568openalex-snapshot
A Minireview of Analytical Methods for the Geographical Origin Analysis of Teas ( Camellia sinensis )
inferredcites →https://doi.org/10.1080/10408690390826464openalex-snapshot
Tea Catechins and Polyphenols: Health Effects, Metabolism, and Antioxidant Functions
inferredcites →https://doi.org/10.1088/1755-1315/985/1/012008openalex-snapshot
The Diversity and Classification of Tea Germplasm Based on Morpho-Agronomic Characteristics
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/icesc48915.2020.9155850openalex-snapshot
Image Analysis and Detection of Tea Leaf Disease using Deep Learning
inferredcites →https://doi.org/10.1109/snpdwinter52325.2021.00021openalex-snapshot
Research and Implementation Of Image Recognition of Tea Based on Deep Learning
inferredcites →https://doi.org/10.1109/spac46244.2018.8965555openalex-snapshot
Image Recognition of Tea Leaf Diseases Based on Convolutional Neural Network
inferredcites →https://doi.org/10.1111/j.1467-3010.2007.00665.xopenalex-snapshot
The impact of caffeine on mood, cognitive function, performance and hydration: a review of benefits and risks
inferredcites →https://doi.org/10.1111/jfpe.13289openalex-snapshot
Classification of oolong tea varieties based on hyperspectral imaging technology and BOSS‐LightGBM model
inferredcites →https://doi.org/10.1111/jfpe.13298openalex-snapshot
Identification of tea varieties by mid‐infrared diffuse reflectance spectroscopy coupled with a possibilistic fuzzy c‐means clustering with a fuzzy covariance matrix
inferredcites →https://doi.org/10.13031/2013.24363openalex-snapshot
Identification of Tea Varieties Using Computer Vision
inferredcites →https://doi.org/10.25165/ijabe.v11i3.3482openalex-snapshot
Automatic determination of optimal spectral peaks for classification of Chinese tea varieties using laser-induced breakdown spectroscopy
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/f13010001openalex-snapshot
Identification of Oil Tea (Camellia oleifera C.Abel) Cultivars Using EfficientNet-B4 CNN Model with Attention Mechanism
inferredcites →https://doi.org/10.3390/horticulturae8080685openalex-snapshot
Biochemical and Proteome Analysis Reveal Different Nutritional Compound Compositions and Chloroplast Development Situations between Purple-Red and White-Yellow Tea Plant Cultivars
inferredcites →https://doi.org/10.3390/s22010173openalex-snapshot
Apple Leaf Disease Identification with a Small and Imbalanced Dataset Based on Lightweight Convolutional Networks
inferredcites →https://doi.org/10.3390/s22207764openalex-snapshot
Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1905.11946openalex-snapshot
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4674638openalex-extracted
A Non-Destructive Method for Identification of Plants in Gentiana Based on Deep Learning
inferred← citeshttps://doi.org/10.1016/j.compag.2025.111076openalex-snapshot
Deep learning and UAV-Based image recognition for identification of medicinal plants in Gentiana Sect. Cruciata
inferred← citeshttps://doi.org/10.1016/j.compag.2025.111140openalex-snapshot
A hybrid and high-performance model for tea cultivars discrimination using canopy images with convolutional neural network and machine learning algorithm
inferred← citeshttps://doi.org/10.1109/amlds63918.2025.11159386openalex-snapshot
Optimizing Aedes Mosquito Detection Using YOLOv5-S ROI Localization and Confidence-Weighted Voting via Reinforcement-Based Pruning
inferred← citeshttps://doi.org/10.21323/2618-9771-2024-7-2-282-287openalex-snapshot
Methodological approaches to gene identification of tea raw materials and raw material composition of tea-based soft drinks
inferred← citeshttps://doi.org/10.3389/fpls.2024.1495222openalex-snapshot
YOLO-SDL: a lightweight wheat grain detection technology based on an improved YOLOv8n model
inferred← citeshttps://doi.org/10.3389/fpls.2026.1863075openalex-snapshot
A lightweight deep learning framework for tea cultivar identification based on leaf images
inferred← citeshttps://doi.org/10.3389/fvets.2025.1660163openalex-snapshot
Enhanced cattle identification using Siamese network and MobileViT with EMA attention
inferred← citeshttps://doi.org/10.3390/agriculture14122324openalex-snapshot
Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree
inferred← citeshttps://doi.org/10.3390/agriculture15192027openalex-snapshot
Training Strategy Optimization of a Tea Canopy Dataset for Variety Identification During the Harvest Period
inferred← citeshttps://doi.org/10.3390/ani14131923openalex-snapshot
A Sheep Identification Method Based on Three-Dimensional Sheep Face Reconstruction and Feature Point Matching
inferred← citeshttps://doi.org/10.3390/biology15151283openalex-snapshot
To Explore the Utility of Leaf Morphological, Color, and Chlorophyll Traits in Assessing Inter-Cultivar Variations Among Six Tea Plant Cultivars
inferred← citeshttps://doi.org/10.3390/horticulturae10070665openalex-snapshot
Counterfeit Detection of Iranian Black Tea Using Image Processing and Deep Learning Based on Patched and Unpatched Images
inferred← citeshttps://doi.org/10.3390/s25185724openalex-snapshot
A Lightweight Hybrid Detection System Based on the OpenMV Vision Module for an Embedded Transportation Vehicle
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