Recognition of Green Colour Vegetables' Images Using an Artificial Neural Network

Madgi, Manohar; Danti, Ajit; Anami, Basavaraj S.

doi:10.1109/icondsc.2019.8816943

Cited by 3 publications

(1 citation statement)

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“…Aiming at diverse sample images, the underlying features of the images are extracted combining with diverse classifiers to solve the image classification problems. The major machine learning algorithms such as k ‐nearest neighbours [19], support vector machine (SVM) [20], Fisher linear discriminant [21], artificial neural networks (ANNs) [22], and random forests (RF) [23] have gained popularity in the field of image recognition. Particularly, various ANNs‐based methods are commonly used in the classification problem, because of their learning capability, parallel distributed process, non‐linear recognition, and simulation modelling ability [24–26].…”

Section: Introductionmentioning

confidence: 99%

Intelligent monitoring method of water quality based on image processing and RVFL‐GMDH model

et al. 2020

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The water quality, contaminant migration characteristics, and emissions quantity of pollutants in the basin would have a great impact on aquatic creatures, agricultural irrigation, human life, and so on. In the aquaculture industry, because water colour can reflect the species and number of phytoplankton in the water, the water quality type can be obtained by analysing the colour of the aquaculture water using image processing techniques. Therefore, this study proposes an intelligent monitoring approach for water quality. The critical features of water colour images are extracted, and then using the machine learning methods, an intelligent system for water quality monitoring is established based on the fused random vector functional link network (RVFL) and group method of data handling (GMDH) model. The proposed approach presents a superior performance relative to other state‐of‐the‐art methods, and it achieves an average predicting accuracy of 96.19% on the feature dataset. Experimental findings demonstrate the validity of the proposed approach, and it is accomplished efficiently for the monitoring of water quality.

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