Characterization of a Multispectral Imaging System Based on Narrow Bandwidth Power LEDs

Monsalve, Manuel Alejandro Tamayo; Osorio, Gustavo; Montes, Nubia Liliana; López, S. Camacho; Cubero, Sergio; Blasco, José

doi:10.1109/tim.2020.3010109

Cited by 11 publications

(2 citation statements)

References 52 publications

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“…The proposed multispectral system is a proprietary design, which is calibrated and validated within a spectral range of 400 to 1000 nm and can be seen in detail in [51]. This system has three main elements: The broad electromagnetic spectrum camera, a controlled illumination environment to improve image quality, and a narrow bandwidth LED illumination corona to generate the necessary illumination at different wavelengths.…”

Section: B Acquisition Systemmentioning

confidence: 99%

Coffee Maturity Classification Using Convolutional Neural Networks and Transfer Learning

et al. 2022

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Section: B Acquisition Systemmentioning

confidence: 99%

Coffee Maturity Classification Using Convolutional Neural Networks and Transfer Learning

et al. 2022

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“…Multispectral images of coffee fruit are obtained using a special camera modified with specifications in the form of a wide electromagnetic spectrum, controlled illumination space, and narrow LED bandwidth [4], [5]. The camera produces 15 color channels, including violet, royal blue, blue, azure, cyan, green, lime, yellow, amber, red-orange, red, deep red, far red, and 2 Near Infrared (NIR).…”

Section: Introductionmentioning

confidence: 99%

Classification of Coffee Fruit Maturity Level based on Multispectral Image Using Naïve Bayes Method

‘Ulhaq,

Hidayat,

Dharmawan

2024

Jurnal Ilmu Komputer dan Informasi

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The current research about the classification of coffee fruit ripeness based on multispectral images has been developed using the Convolutional Neural Network (CNN) method to extract patterns from highdimensional multispectral images. The high complexity of CNN allows the model to capture complex features but requires more time and computational resources for model training and testing. Therefore, in this study, classification is performed using a more straightforward method such as Naïve Bayes because its complexity only depends on the number of features and samples. The method only considers each feature independently, so it has high speed and does not require a lot of computational resources. Naïve Bayes is applied to color and texture features extracted from multispectral images of coffee fruit. There are 300 features consisting of 60 color features and 240 texture features. Experiments were conducted based on the comparison of training and testing data and the use of each feature. The combination of color and texture features showed better performance than color or texture features alone, with the highest accuracy reaching 91.01%. In conclusion, using Naïve Bayes is still reasonably good in classifying the ripeness of coffee fruit based on multispectral images.

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A Framework for Multispectral Instance Classification Using Examples

Peláez-García¹,

Patiño-Martínez²,

Gómez-Mendoza³

et al. 2021

Communications in Computer and Information Science

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Characterization of a Multispectral Imaging System Based on Narrow Bandwidth Power LEDs

Cited by 11 publications

References 52 publications

Coffee Maturity Classification Using Convolutional Neural Networks and Transfer Learning

Coffee Maturity Classification Using Convolutional Neural Networks and Transfer Learning

Classification of Coffee Fruit Maturity Level based on Multispectral Image Using Naïve Bayes Method

A Framework for Multispectral Instance Classification Using Examples

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