Discriminating rapeseed varieties using computer vision and machine learning

Kurtulmuş, Ferhat; Ünal, Halil

doi:10.1016/j.eswa.2014.10.003

Cited by 54 publications

(22 citation statements)

References 39 publications

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“…The used of ANN in the classification analysis of agrobased product as well as in other application has been widely used such as in the classification of Chinese rice seed varieties [11], apple grading [12], discrimination of rapeseed varieties [13], olive fruits recognition [14], detection of segmentation points of Arabic Words [15] and face recognition [16].…”

Section: Introductionmentioning

confidence: 99%

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

Aznan

Ruslan

Rukunudin

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Abstract-Determination of rice seed varieties is very important to ensure varietal purity in the production of high-quality seed. To date, manual seed inspection is carried out to separate foreign rice seed varieties in rice seed sample in the laboratory as there is lack of an automatic seed classification system. This paper describes a simple approach of using image processing technique and artificial neural network (ANN) to determine rice seed varieties based on extracted colour features of individual seed images. The experiment was conducted using 200 individual seed images of two Malaysian rice seed varieties namely MR 219 and MR 269. The acquired seed images were processed using a set of image processing procedure to enhance the image quality. Colour feature extraction was carried out to extract the red (R), green (G), blue (B), hue (H), saturation (S), value (V) and intensity (I) levels of the individual seed images. The classification using ANN was carried out by dividing the data sets into training (70% of data), validation (15%) and testing (15%) dataset respectively. The best ANN model to determine the rice seed varieties was developed, and the accuracy levels of the classification results were 67.5% and 76.7% for testing and training data sets using 40 hidden neurons.

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Section: Introductionmentioning

confidence: 99%

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

Aznan

Ruslan

Rukunudin

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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“…In this study 16 different GLCM-based texture features namely; entropy, energy, inertia, correlation, homogeneity, dissimilarity, sum of squares, sum of averages, sum of variances, sum of entropies, difference variance, difference entropy, cluster shade, cluster prominence, inverse difference moment, and maximum probability were calculated from GLCMs and used for plant classi cation. These features have been previously described in detail and used by several researchers [41,[46][47][48][49][50].…”

Section: Feature Extractionmentioning

confidence: 99%

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

Bakhshipour

Zareiforoush

2020

Preprint

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A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies were 95.56% for J48-CFS, 92.78% for REP-CFS, and 93.33% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

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“…The CVS is widely applied as a quality assurance technique for food products nowadays. More specifically, the CVS has been extensively studied over the decades in rapidly examining a series of interior and exterior quality metrics such as the varieties, defects and maturities of fruits in grapes (Xia, Wu, Nie, & He, ), bananas (Mendoza & Aguilera, ), watermelons (Koc, ), and rapeseeds (Kurtulmus & Unal, ). It is foreseeable that the CVS can be applied in larger fields of industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Rapid identification of chrysanthemum teas by computer vision and deep learning

Gao

et al. 2020

Food Science & Nutrition

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Seven commercial Chinese chrysanthemum tea products were classified by computer vision combined with machine learning algorithms. Without the need of building any specific hardware, the image acquisition was achieved in two computer vision approaches. In the first approach, a series of multivariate classification models were built after morphological feature extraction of the image. The best prediction accuracies when classifying flowering stages and tea types were respectively 90% and 63%. In comparison, the deep neural network was applied directly on the raw image, yielded 96% and 89% correct identifications when classifying flowering stage and tea type, respectively. The model can be applied for rapid and automatic quality determination of teas and other related foods. The result indicated that computer vision, especially when combined with deep learning or other machine learning techniques can be a convenient and versatile method in the evaluation of food quality. K E Y W O R D S chrysanthemum tea, computer vision classification, deep neural network, morphological feature

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Discriminating rapeseed varieties using computer vision and machine learning

Cited by 54 publications

References 39 publications

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

Rice Seed Varieties Identification based on Extracted Colour Features using Image Processing and Artificial Neural Network (ANN)

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

Rapid identification of chrysanthemum teas by computer vision and deep learning

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