Computer vision for purity, phenol, and pH detection of Luwak Coffee Green Bean

Hendrawan, Yusuf; Widyaningtyas, Shinta; Sucipto, Sucipto

doi:10.12928/telkomnika.v17i6.12689

Cited by 27 publications

(17 citation statements)

References 34 publications

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“…After obtaining the weight and ranking for each feature, the features selected in Table 1 were modelled using ANN using the trial and error method to define the best combination of features-subset which later for chlorophyll prediction; the lowest validation MSE was also considered as a robust model. Trial and error on ANN modelling on preliminary research generated the best ANN topology, consisting of 30 nodes on the first hidden layer, 40 nodes on the second hidden layer, with the learning function of trainlm, tansig activation function in each hidden layer, and purelin on the output layer; learning rate 0.1; and momentum 0.5 (Hendrawan et al, 2019c). Table 2 shows that the entire 120 texture features configuration created higher validation MSE compare with feature selection.…”

Section: Resultsmentioning

confidence: 99%

“…2020), a good learning process decreased the MSE but iterations, so the learning graph will show a decrease in linear lines. Determination of epoch and goals based on previous research (Hendrawan et al, 2019c) stated that validated MSE with the goal of 0.01 was considerably accurate for predicting objective functions. Nevertheless, extremely low MSE can cause overfitting.…”

Section: Resultsmentioning

confidence: 99%

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The Prediction of Chlorophyll Content in African Leaves (Vernonia amygdalina Del.) Using Flatbed Scanner and Optimised Artificial Neural Network

Damayanti¹,

Rachma²,

Riza³

et al. 2021

JST

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African leaves (Vernonia amygdalina Del.) is a nutrient-rich plant that has been widely used as a herbal plant. African leaves contain chlorophyll which identify compounds produced by a plant, such as flavonoids and phenols. Chlorophyll testing can be carried out non-destructively by using the SPAD 502 chlorophyll meter. However, it is quite expensive, so that another non-destructive method is developed, namely digital image analysis. Relationships between chlorophyll content and leaf image colour indices in the RGB, HSV, HSL, and Lab* space are examined. The objectives of this study are 1) to analyse the relationship between texture parameters of red, green, blue, grey, hue, saturation(HSL), lightness (HSL), saturation( HSV), value(HSV), L*, a*, and b* against the chlorophyll content in African leaves using a flatbed scanner (HP DeskJet 2130 Series); and 2) built a model to predict chlorophyll content in African leaves using optimised ANN through a feature selection process by using several filter methods. The best ANN topologies are 10-30-40-1 (10 input nodes, 40 nodes in hidden layer 1, 30 nodes in hidden layer 2, and 1 output node) with a trainlm on the learning function, tansig on the hidden layer, and purelin on the output layer. The selected topology produces MSE training of 0.0007 with R training 0.9981 and the lowest validation MSE of 0.012 with R validation of 0.967. With these results, it can be concluded that the ANN model can be potentially used as a model for predicting chlorophyll content in African leaves.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The Prediction of Chlorophyll Content in African Leaves (Vernonia amygdalina Del.) Using Flatbed Scanner and Optimised Artificial Neural Network

Damayanti¹,

Rachma²,

Riza³

et al. 2021

JST

Self Cite

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“…Hyperspectral machine vision was investigated and proven effective to detect water stress in tomato plants [22]. A simpler and cheaper machine vision method using a low-cost commercial visible-light camera was also researched and proven effective to detect water stress in plants in a rapid and non-destructive way [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

Classification of water stress in cultured Sunagoke moss using deep learning

et al. 2021

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Water stress greatly determines plant yield as it affects plant metabolism, photosynthesis rate, chlorophyll content index, number of leaves, physiological, biochemical compound, and vegetative growth. The research aimed to detect and classify water stress of cultured Sunagoke moss into several categories i.e. dry, semi-dry, wet, and soak by using a low-cost commercial visible light camera combined with a deep learning model. Cultured Sunagoke moss is a commercial product which has the potential use as rooftop-greening and wall-greening material. This research compared the performance of four convolutional neural network models, such as SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The best convolutional neural network model according to the training and validation result was ResNet50 with RMSProp optimizer, 30 epoch, and 128 mini-batch size; this also gained an accuracy rate at 87.50%. However, the best result of the convolutional neural network model on data testing using confusion matrices on different data sample was ResNet50 with Adam optimizer, 30 epoch, 128 mini-batch size, and average testing accuracy of 94.15%. It can be concluded that based on the overall results, convolutional neural network model seems promising as a smart irrigation system that real-time, non-destructive, rapid, and precise method when controlling water stress of plants.

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“…Before green coffee beans can be classified and graded, their features must first be extracted [6]. Many kinds of research have been done in green coffee beans feature extraction which includes the use of chemicals [7,8], different types of spectroscopy such as fluorescence [9], near infrared (NIRS) [10,11], Fourier transform [12], and Raman [13,14], electronic tongue [15], electronic nose (16), and image processing [17,18].…”

Section: Introductionmentioning

confidence: 99%

Green coffee beans feature extractor using image processing

2020

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This study offers a novel solution to deal with the low signal-to-noise ratio and slow execution rate of the first derivative edge detection algorithms namely, Roberts, Prewitt and Sobel algorithms. Since the two problems are brought about by the complex mathematical operations being used by the algorithms, these were replaced by a discriminant. The developed discriminant, equivalent to the product of total difference and intensity divided by the normalization values, is based on the "pixel pair formation" that produces optimal peak signal to noise ratio. Results of the study applying the discriminant for the edge detection of green coffee beans shows improvement in terms of peak signal to noise ratio (PSNR), mean square error (MSE), and execution time. It was determined that accuracy level varied according to the total difference of pixel values, intensity, and normalization values. Using the developed edge detection technique led to improvements in the PSNR of 2.091%, 1.16 %, and 2.47% over Sobel, Prewitt, and Roberts respectively. Meanwhile, improvement in the MSE was measured to be 13.06%, 7.48 %, and 15.31% over the three algorithms. Likewise, improvement in execution time was also achieved at values of 69.02%, 67.40 %, and 65.46% over Sobel, Prewitt, and Roberts respectively.

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Computer vision for purity, phenol, and pH detection of Luwak Coffee Green Bean

Cited by 27 publications

References 34 publications

The Prediction of Chlorophyll Content in African Leaves (Vernonia amygdalina Del.) Using Flatbed Scanner and Optimised Artificial Neural Network

The Prediction of Chlorophyll Content in African Leaves (Vernonia amygdalina Del.) Using Flatbed Scanner and Optimised Artificial Neural Network

Classification of water stress in cultured Sunagoke moss using deep learning

Green coffee beans feature extractor using image processing

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