Colour based Object Classification using KNN Algorithm for Industrial Applications

Kumar, Nitin; Maheswari, S.; M, Vigneshwari.; Pramila, P. V.; Khilar, Rashmita; Kumar, Ashok

doi:10.1109/icacrs55517.2022.10029315

Cited by 1 publication

(1 citation statement)

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“…Phiri et al (2020) used the results of 25 studies in classifying Sentinel-2 images and reported that the SVM and Bayes method has better accuracy for classi cation purposes. Kumar et al (2022) emphasize using color in image classi cation using the object-oriented KNN method. Yan et al (2022) stated that the objectoriented classi cation method performs better than the pixel-based classi cation method in the same training conditions.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the ability to measure morphological structures of plants obtained from tissue culture applying image processing techniques

Aliabad,

aliabad,

Habbab

et al. 2023

Preprint

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Biotechnological approaches, for instance, plant tissue culture, can be used to improve and accelerate the reproduction of plants. A single portion of a plant can produce many plants throughout the year in a relatively short period of laboratory conditions. Monitoring and recording plant morphological characteristics such as root length and shoot length in different conditions and stages are necessary for tissue culture. These features were measured using graph paper in a laboratory environment and sterile conditions. This research investigated the ability to use image processing techniques in determining the morphological features of plants obtained from tissue culture. In this context RGB images were prepared from the plants inside the glass, and different pixel-based and object-based classification methods were applied to an image as a control. The accuracy of these methods was evaluated using the kappa coefficient, and overall accuracy was obtained from Boolean logic. The results showed that among pixel-based classification methods, the maximum likelihood method with a kappa coefficient of 87% and overall accuracy of 89.4 was the most accurate, and the Spectral angle mapper method (SAM) method with a kappa coefficient of 58% and overall accuracy of 54.6 was the least accurate. Also, among object-based classification methods, Support Vector Machine (SVM), Naïve Bayes, and K-nearest neighbors algorithm (KNN) techniques, with a Kappa coefficient of 88% and overall accuracy of 90, can effectively distinguish the cultivation environment, plant, and root. Comparing the values of root length and shoot length estimated in the laboratory culture environment with the values obtained from image processing showed that the use of the SVM image classification method, which is capable of estimating root length and shoot length with RMSE 2.4, MAD 3.01 and R2 0.97, matches the results of manual measurements with even higher accuracy.

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Section: Introductionmentioning

confidence: 99%