Classification of canola seed varieties based on multi-feature analysis using computer vision approach

Qadri, Salman; Qadri, Syed Furqan; Razzaq, Abdul; Rehman, Muzammil Ul; Aḥmad, Naẕīr; Nawaz, Syed Ali; Saher, Najia; Akhtar, Nadeem; Khan, Dost Muhammad

doi:10.1080/10942912.2021.1900235

Cited by 14 publications

(5 citation statements)

References 29 publications

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“…Combining the handcrafted and deep feature vectors increases the feature discrimination ability of the algorithm. The study used CNN for deep feature extraction and a HOG for handcrafted feature extraction [30]. The details of how the two feature vectors were concatenated are illustrated in Figure 2.…”

Section: Feature Vector Concatenationmentioning

confidence: 99%

Maize seed variety identification model using image processing and deep learning

Gebeyehu,

Sintayehu Shibeshi

2024

IJEECS

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Maize is Ethiopia’s dominant cereal crop regarding area coverage and production level. There are different varieties of maize in Ethiopia. Maize varieties are classified based on morphological features such as shape and size. Due to the nature of maize seed and its rotation variant, studies are still needed to identify Ethiopian maize seed varieties. With expert eyes, identification of maize seed varieties is difficult due to their similar morphological features and visual similarities. We proposed a hybrid feature-based maize variety identification model to solve this problem. For training and testing the model, images of each maize variety were collected from the adet agriculture and research center (AARC), Ethiopia. A multi-class support vector machine (MCSVM) classifier was employed on a hybrid of handcrafted (i.e., gabor and histogram of oriented gradients) and convolutional neural network (CNN)-based feature selection techniques and achieved an overall classification accuracy of 99%.

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Section: Feature Vector Concatenationmentioning

confidence: 99%

Maize seed variety identification model using image processing and deep learning

Gebeyehu,

Sintayehu Shibeshi

2024

IJEECS

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“…Scholars have done a lot of research on single image homogenization. The brightness unevenness phenomenon of the submarine visual image caused by point light source irradiation is effectively corrected by gray-scale stretching element by element [8]. The traditional Perona-Malik model combines gradient calculations to improve image contrast, increase image details and reduce noise.…”

Section: Related Workmentioning

confidence: 99%

Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation

Wang

Yuan

2022

Advances in Mathematical Physics

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This paper studies the processing of digital media images using a diffusion equation to increase the contrast of the image by stretching or extending the distribution of luminance data of the image to obtain clearer information of digital media images. In this paper, the image enhancement algorithm of nonlinear diffusion filtering is used to add a velocity term to the diffusion function using a coupled denoising model, which makes the diffusion of the original model smooth, and the interferogram is solved numerically with the help of numerical simulation to verify the denoising processing effect before and after the model correction. To meet the real-time applications in the field of video surveillance, this paper focuses on the optimization of the algorithm program, including software pipeline optimization, operation unit balancing, single instruction multiple data optimization, arithmetic operation optimization, and onchip storage optimization. These optimizations enable the nonlinear diffusion filter-based image enhancement algorithm to achieve high processing efficiency on the C674xDSP, with a processing speed of 25 posts per second for 640 × 480 size video images. Finally, the significance means a value of super pixel blocks is calculated in superpixel units, and the image is segmented into objects and backgrounds by combining with the Otsu threshold segmentation algorithm to mention the image. In this paper, the proposed algorithm experiments with several sets of Kor Kor resolution remote sensing images, respectively, and the Markov random field model and fully convolutional network (FCN) algorithm are used as the comparison algorithm. By comparing the experimental results qualitatively and quantitatively, it is shown that the algorithm in this paper has an obvious practical effect on contrast enhancement of digital media images and has certain practicality and superiority.

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“…The highest classification accuracy was achieved using SVM (88.3-92.4%) and CNN (88.0-92.6%). Further examples of other applications of colour sensors in the food domain include recognizing canola cultivars based on histogram and texture features coupled with ANN [26], assessing olive lot ripeness degree using k-Nearest Neighbour (kNN) unsupervised learning, and more generally the evaluation of the quality of rice grains [27], and maize [28]. To the best of the authors' knowledge, there are no previous studies investigating the classification of apricot stones using RGB colour sensors.…”

Section: Introductionmentioning

confidence: 99%

Apricot Stone Classification Using Image Analysis and Machine Learning

2023

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Apricot stones have high commercial value and can be used for manufacturing functional foods, cosmetic products, active carbon, and biodiesel. The optimal processing of the stones is dependent on the cultivar and there is a need for methods to sort among different cultivars (which are often mixed in processing facilities). This study investigates the effectiveness of two low-cost colour imaging systems coupled with supervised learning to develop classification models to determine the cultivar of different stones. Apricot stones of the cultivars ‘Bella’, ‘Early Orange’, ‘Harcot’, ‘Skierniewicka Słodka’, and ‘Taja’ were used. The RGB images were acquired using a flatbed scanner or a digital camera; and 2172 image texture features were extracted within the R, G, B; L, a, b; X, Y, Z; U, and V colour coordinates. The most influential features were determined and resulted in 103 and 89 selected features for the digital camera and the flatbed scanner, respectively. Linear and nonlinear classifiers were applied including Linear Discriminant Analysis (LDA), Decision Trees (DT), k-Nearest Neighbour (kNN), Support Vector Machines (SVM), and Naive Bayes (NB). The models resulting from the flatbed scanner and using selected features achieved an accuracy of 100% via either quadratic diagonal LDA or kNN classifiers. The models developed using images from the digital camera and all or selected features had an accuracy of up to 96.77% using the SVM classifier. This study presents novel and simple-to-implement at-line (flatbed scanner) and online (digital camera) methodologies for apricot stone sorting. The developed procedure combining colour imaging and machine learning may be used for the authentication of apricot stone cultivars and quality evaluation of apricot from sustainable production.

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Classification of canola seed varieties based on multi-feature analysis using computer vision approach

Cited by 14 publications

References 29 publications

Maize seed variety identification model using image processing and deep learning

Maize seed variety identification model using image processing and deep learning

Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation

Apricot Stone Classification Using Image Analysis and Machine Learning

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