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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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Analysis of selected deep features with CNN-SVM-based for bread wheat seed classification
Scopus
Havuzumuzda Open Access 16 atıf almış
The main ingredient of flour is processed wheat. Wheat is an agricultural product that is harvested once a year. It may be necessary to choose the variety of wheat for growing wheat and efficient harvesting. The variety of wheat is important for its economic value, taste, and crop yield. Although there are many varieties of wheat, they are very similar in colour, size, and shape, and it requires expertise to distinguish them by eye. This is very time consuming and can lead to human error. Using computer vision and artificial intelligence, such problems can be solved more quickly and objectively. In this study, an attempt was made to classify five bread wheat varieties belonging to different cultivars using Convolutional Neural Network (CNN) models. Three approaches have been proposed for classification. First, pre-trained CNN models (ResNet18, ResNet50, and ResNet101) were trained for bread wheat cultivars. Second, the features extracted from the fc1000 layer of the pre-trained CNN models ResNet18, ResNet50, and ResNet101 were classified using a support vector machine (SVM) classifier with different kernel features from machine learning techniques for classification with different variants. Finally, SVM methods were used in the second stage to classify the features obtained from the fc1000 layer of the pre-trained CNN models with an optimal set of features that can represent all features using the minimum redundancy maximum relevance (mRMR) feature selection algorithm.The accuracies obtained in the first, second, and last phases are as follows. In the first phase, the most successful method in classifying wheat grains was the ResNet18 model with 97.57%. In the second phase, the ResNet18 + ResNet50 + ResNet101 + Quadratic SVM model was the most successful model in classification using the features obtained from the ResNet CNN models with 94.08%.The accuracy for classification with the 1000 most effective features selected by the feature selection algorithm was 94.51%. Although the classification with features is slightly lower than deep learning, the classification time is much shorter and is 93%. This result confirms the great effectiveness of CNN models for wheat grain classification.
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Kurumlar (1)
Selçuk Üniversitesi
Selçuklu, Turkey