Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Multiclass classification of dry beans using computer vision and machine learning techniques
Computers and Electronics in Agriculture · Temmuz 2020
YÖKSİS Kayıtları
Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques
Computers and Electronics in Agriculture · 2020 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques
Computers and Electronics in Agriculture · 2020 SCI-Expanded
Doç. Dr. İLKER ALİ ÖZKAN →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis
2011 ISSN: 01681699 SCI-Expanded
Prof. Dr. ŞAKİR TAŞDEMİR →
Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features
2019 ISSN: 0168-1699 SCI-Expanded
Prof. Dr. İSMAİL SARITAŞ →
Generating of land suitability index for wheat with hybrid system aproach using AHP and GIS
2019 ISSN: 0168-1699 SCI-Expanded
Prof. Dr. MERT DEDEOĞLU →
Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques
2020 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Classification of Rice Varieties with Deep Learning Methods
2021 ISSN: 0168-1699 SCI-Expanded Q1
Dr. Öğr. Üyesi İLKAY ÇINAR →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Dr. Öğr. Üyesi İLKAY ÇINAR →
Dry Bean Cultivars Classification Using Deep CNN Features and Salp Swarm Algorithm Based Extreme Learning Machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Classification of Rice Varieties with Deep Learning Methods
2021 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. MURAT KÖKLÜ →
Towards a real-time sorting system: Identification of vitreous durum wheat kernels using ANN based on their morphological, colour, wavelet and gaborlet features
2019 ISSN: 0168-1699 SCI-Expanded Q1
Dr. Öğr. Üyesi ESRA KAYA ERDOĞAN →
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine
2023 ISSN: 0168-1699 SCI-Expanded Q1
Doç. Dr. İLKER ALİ ÖZKAN →
Makale Bilgileri
ISSN01681699
Yayın TarihiTemmuz 2020
Cilt / Sayfa174
Scopus ID2-s2.0-85085548613
Özet
There is a wide range of genetic diversity of dry bean which is the most produced one among the edible legume crops in the world. Seed quality is definitely influential in crop production. Therefore, seed classification is essential for both marketing and production to provide the principles of sustainable agricultural systems. The primary objective of this study is to provide a method for obtaining uniform seed varieties from crop production, which is in the form of population, so the seeds are not certified as a sole variety. Thus, a computer vision system was developed to distinguish seven different registered varieties of dry beans with similar features in order to obtain uniform seed classification. For the classification model, images of 13,611 grains of 7 different registered dry beans were taken with a high-resolution camera. A user-friendly interface was designed using the MATLAB graphical user interface (GUI). Bean images obtained by computer vision system (CVS) were subjected to segmentation and feature extraction stages, and a total of 16 features; 12 dimension and 4 shape forms, were obtained from the grains. Multilayer perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Decision Tree (DT) classification models were created with 10-fold cross validation and performance metrics were compared. Overall correct classification rates have been determined as 91.73%, 93.13%, 87.92% and 92.52% for MLP, SVM, kNN and DT, respectively. The SVM classification model, which has the highest accuracy results, has classified the Barbunya, Bombay, Cali, Dermason, Horoz, Seker and Sira bean varieties with 92.36%, 100.00%, 95.03%, 94.36%, 94.92%, 94.67% and 86.84%, respectively. With these results, the demands of the producers and the customers are largely met about obtaining uniform bean varieties.
Yazarlar (2)
1
Murat Koklu
ORCID: 0000-0002-2737-2360
2
Ilker Ali Ozkan
Anahtar Kelimeler
Classification of dry beans
Computer vision system
Image processing
Machine learning techniques
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
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Scimago Dergi (ISSN Eşleşmesi)
Computers and Electronics in Agriculture
Q1
SJR Skoru2,165
H-Index209
YayıncıElsevier B.V.
ÜlkeNetherlands
Agronomy and Crop Science (Q1)
Animal Science and Zoology (Q1)
Computer Science Applications (Q1)
Forestry (Q1)
Horticulture (Q1)
Metrikler
327
Atıf
2
Yazar
4
Anahtar Kelime