A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties

Estimation of the physicochemical variation of chickpea seeds (Cicer arietinum L.)

(2019) Wojciech Rybiński   International Agrophysics

Corn Classification System based on Computer Vision

(2019) Xiaoming Li et al.   Symmetry-Basel

Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions

(2019) Razieh Pourdarbani et al.   Remote Sensing

Multi-classifier majority voting analyses in provenance studies on iron artefacts

(2019) Grzegorz Żabiński et al.   JOURNAL OF ARCHAEOLOGICAL SCIENCE

A Classification Method for Seed Viability Assessment with Infrared Thermography

(2017) Sen Men et al.   SENSORS

A novel hybrid Cultural Algorithms framework with trajectory-based search for global numerical optimization

(2016) Mostafa Z. Ali et al.   INFORMATION SCIENCES

A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm

(2016) Tanuj Sen et al.   INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Discrete wavelets transform for improving greenness image segmentation in agricultural images

(2015) M. Guijarro et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Discriminating rapeseed varieties using computer vision and machine learning

(2015) F. Kurtulmuş et al.   EXPERT SYSTEMS WITH APPLICATIONS

Discriminating and elimination of damaged soybean seeds based on image characteristics

(2015) Dejun Liu et al.   JOURNAL OF STORED PRODUCTS RESEARCH

Selective Search for Object Recognition

(2013) J. R. R. Uijlings et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION