Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques
Authors
Keywords
-
Journal
EUROPEAN FOOD RESEARCH AND TECHNOLOGY
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-08-05
DOI
10.1007/s00217-022-04080-1
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Discrimination of onion subjected to drought and normal watering mode based on fluorescence spectroscopic data
- (2022) Ewa Ropelewska et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization
- (2022) Muhammet Fatih Aslan et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Authentication of tomato (Solanum lycopersicum L.) cultivars using discriminative models based on texture parameters of flesh and skin images
- (2022) Ewa Ropelewska et al. EUROPEAN FOOD RESEARCH AND TECHNOLOGY
- Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models
- (2022) Dilbag Singh et al. Electronics
- Images, features, or feature distributions? A comparison of inputs for training convolutional neural networks to classify lentil and field pea milling fractions
- (2021) Linda S. McDonald et al. BIOSYSTEMS ENGINEERING
- Real-time recognition system of soybean seed full-surface defects based on deep learning
- (2021) Guoyang Zhao et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Classification of rice varieties with deep learning methods
- (2021) Murat Koklu et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- An improved image processing scheme for automatic detection of harvested soybean seeds
- (2021) Sachin Sonawane et al. Journal of Food Measurement and Characterization
- Automated In Situ Seed Variety Identification via Deep Learning: A Case Study in Chickpea
- (2021) Amin Taheri-Garavand et al. Plants-Basel
- CNN-based bi-directional and directional long-short term memory network for determination of face mask
- (2021) Murat Koklu et al. Biomedical Signal Processing and Control
- Classification by a stacking model using CNN features for COVID-19 infection diagnosis
- (2021) Yavuz Selim Taspinar et al. Journal of X-Ray Science and Technology
- Classification of Date Fruits into Genetic Varieties Using Image Analysis
- (2021) Murat Koklu et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- A CNN-SVM study based on selected deep features for grapevine leaves classification
- (2021) Murat Koklu et al. MEASUREMENT
- Deep learning-based appearance features extraction for automated carp species identification
- (2020) Ashkan Banan et al. AQUACULTURAL ENGINEERING
- Inception v3 based cervical cell classification combined with artificially extracted features
- (2020) N. Dong et al. APPLIED SOFT COMPUTING
- Multiclass classification of dry beans using computer vision and machine learning techniques
- (2020) Murat Koklu et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Interactive machine learning for soybean seed and seedling quality classification
- (2020) André Dantas de Medeiros et al. Scientific Reports
- Corn seed variety classification based on hyperspectral reflectance imaging and deep convolutional neural network
- (2020) Jun Zhang et al. Journal of Food Measurement and Characterization
- Evaluation of Cultivar Identification Performance Using Feature Expressions and Classification Algorithms on Optical Images of Sweet Corn Seeds
- (2020) Yu Tang et al. Agronomy-Basel
- Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms
- (2020) Yanlin Wei et al. SENSORS
- Corn Classification System based on Computer Vision
- (2019) Xiaoming Li et al. Symmetry-Basel
- Logistic Regression for Machine Learning in Process Tomography
- (2019) Tomasz Rymarczyk et al. SENSORS
- Automatic Classification of Chickpea Varieties Using Computer Vision Techniques
- (2019) Pourdarbani et al. Agronomy-Basel
- Global-connected network with generalized ReLU activation
- (2019) Zhi Chen et al. PATTERN RECOGNITION
- Multivariate comparison of classification performance measures
- (2018) Davide Ballabio et al. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
- Classification of corn kernels grades using image analysis and support vector machine
- (2018) Ang Wu et al. Advances in Mechanical Engineering
- Accelerating Very Deep Convolutional Networks for Classification and Detection
- (2016) Xiangyu Zhang et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Bean seeds: leading nutraceutical source for human health
- (2015) Silvia Esperanza Suárez-Martínez et al. CyTA-Journal of Food
- Classification of Black Beans Using Visible and Near Infrared Hyperspectral Imaging
- (2015) Jun Sun et al. INTERNATIONAL JOURNAL OF FOOD PROPERTIES
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Bean seeds: leading nutraceutical source for human health
- (2015) Silvia Esperanza Suárez-Martínez et al. CyTA-Journal of Food
- Representation Learning: A Review and New Perspectives
- (2013) Y. Bengio et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Image processing technique to estimate geometric parameters and volume of selected dry beans
- (2013) Mahesh Kumar et al. Journal of Food Measurement and Characterization
- Classification of chickpea seeds using supervised and unsupervised artificial neural networks
- (2012) Salah Ghamari African Journal of Agricultural Research
- A Survey on Transfer Learning
- (2009) Sinno Jialin Pan et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started