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Multiclass classification of dry beans using computer vision and machine learning techniques
Scopus
Toplam 346 atıf DOI
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.
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Identification of Dry Bean Seeds Using PSO Feature Selection Technique
Scopus
Havuzumuzda 2 atıf almış
Automatic classification of seed varieties is critical for seed producers to maintain the purity of a variety and crop yield. Seed varieties of premium quality are more costly since their ability to increase productivity and profit margins. Dry bean seeds are one of the most widely used seed varieties in Turkey. Therefore, the classification of dried beans is vital both for the purity of the product and for production and marketing. Learning-based classification and optimization approaches are increasingly being employed in all fields of research as a result of advancements in computer and machine learning techniques. The aim of this study is to classify the types of dry beans, that are, Barbunya, Dermason, Cali, Bombay, Sira, Seker and Horoz. The dataset used in the study contains data consisting of sixteen attributes belonging to seven dry bean varieties. Firstly, particle swarm optimisation (PSO) method from metaheuristic algorithms for the characteristics of the data set feature selection process was carried out by using the feature selection algorithm. Secondly, the selected features were identified using machine learning techniques such as decision tree (DT), Naive Bayes (NB), support vector machine (SVM). The highest identification accuracy for the selected features was SVM with 93.03%.
Atıf Yapan Makale Bilgileri
Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey