Sugarcane Stalk Content Prediction in the Presence of a Solid Impurity Using an Artificial Intelligence Method Focused on Sugar Manufacturing

Guedes, Wesley Nascimento; Santos, Lucas Janoni dos; Filletti, Érica Regina; Pereira, Fernando Mendes

doi:10.1007/s12161-019-01551-2

Cited by 14 publications

(11 citation statements)

References 11 publications

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“…The solid impurity in raw sugarcane was successfully estimated using the ANN model for color image data since the data showed no-linear nature. The parameters computed for the ANN model were very promising, the relative errors were 3%, and the data were highly correlated, with the reference values achieving 0.98 for the training set (Guedes et al, 2020).…”

Section: Introductionmentioning

confidence: 92%

“…Our research group has developed analytical methods to evaluate raw sugarcane to help the mills or biorefineries manufacturing process of this material routinely monitored as a consignment for payment purposes. The quality of raw sugarcane influences the manufacturing process, directly compromising two essential commoditiessugar and ethanol (Andrade et al, 2018;Guedes and Pereira, 2018;2019;Guedes et al, 2020;Romera et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Artificial neural network methods include accurate results, ease of implementation, low computational cost, speed in obtaining results, and the ability to learn through a set of examples and provide consistent responses to new data (Braga et al, 2000;Guedes et al, 2020;Santos et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence method developed for classifying raw sugarcane in the presence of the solid impurity

2021

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An investigation dedicated to evaluating a big issue in biorefineries, solid impurity in raw sugarcane, is presented. This relevant industrial sector requests a high-frequency, low-cost, and noninvasive method. Then, the developed method uses the averaged color values of ten color-scale descriptors: R (red), G (green), B (blue), their relative colors (r, g, and b), H (hue), S (saturation), V (value) and L (luminosity) from digital images acquired from 146 solid mixtures among sugarcane stalks and solid impurity � vegetal parts (green and dry leaves) and soil. The solid mixture of samples was prepared considering desirable and undesirable scenarios for the solid impurity amounts. The outstanding result was revealed by an artificial neural network (ANN), achieving 100% of accurate classifications for two ranges of raw sugarcane in the samples: from 90 to 100 wt% and from 41 to 87 wt%. Low-computational cost and a simple setup for image acquisition method could screen solid impurity in sugarcane shipments as a promising application.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence method developed for classifying raw sugarcane in the presence of the solid impurity

2021

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“…The ANN model used had successfully tuned the cooking parameters to regain the nutritional composition of the fried fish. Guedes et al (2020) measured the stalk content prediction of sugarcane using ANN model coupled with the colour information from the images. The study achieved relative errors of 3% by developing the ANN architecture using the average colour values (input layer) and the sugarcane stalk content (output layer) as shown in Figure 4.…”

Section: Applications Of Ai In Quality Determination Of Food and Agricultural Productsmentioning

confidence: 99%

Quality Inspection of Food and Agricultural Products using Artificial Intelligence

Ali

Hashim²,

Aziz³

et al. 2021

Adv Agric Food Res J

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A rising awareness for quality inspection of food and agricultural products has generated a growing effort to develop rapid and non-destructive techniques. Quality detection of food and agricultural products has prime importance in various stages of processing due to the laborious processes and the inability of the system to measure the whole of the food production. The detection of food quality has previously depended on various destructive techniques that require sample destruction and a large amount of postharvest losses. Artificial Intelligence (AI) has emerged with big data technologies and high-performance computation to create new opportunities in the multidisciplinary agri-food domain. This review presents the key concepts of AI comprising an expert system, artificial neural network (ANN), and fuzzy logic. A special focus is laid on the strength of AI applications in determining food quality for producing high and optimum yields. It was demonstrated that ANN provides the best result for modelling and effective in real-time monitoring techniques. The future use of AI for assessing quality inspection is promising which could lead to a real-time as well as rapid evaluation of various food and agricultural products.

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“…Use of Bioinformatics predictive methods based on ANN at different steps of the olive oil production process, which includes olive tree and fruit care, fruit harvest, mechanical and chemical processing, and oil packaging have been examined in-depth with a view to their optimization and so have the authenticity, sensory properties and other quality-related properties of olive oil. Artificial intelligence smelling using Sensomics-Based Expert System are used to determine Food Odour Codes [6].…”

Section: Agriculture Food Healthmentioning

confidence: 99%

Review of Artificial Intelligence Driven Chemistry

Sharma¹

2019

IJRASET

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From discovering new drugs to autonomous laboratories in Chemistry, impact of AI will be huge in the coming years. To understand the philosophy and trend of how AI is transforming the discipline and introducing new dimensions, we start from reviewing the history of Artificial Intelligent systems in Chemistry, how it has evolved over the time and penetrated into different subdisciplines of Chemistry and is being adopted across multiple sectors of industry. We also discuss changing the job roles of the professionals and identify that new jobs will be created especially related to data management and production.

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Sugarcane Stalk Content Prediction in the Presence of a Solid Impurity Using an Artificial Intelligence Method Focused on Sugar Manufacturing

Cited by 14 publications

References 11 publications

Artificial intelligence method developed for classifying raw sugarcane in the presence of the solid impurity

Artificial intelligence method developed for classifying raw sugarcane in the presence of the solid impurity

Quality Inspection of Food and Agricultural Products using Artificial Intelligence

Review of Artificial Intelligence Driven Chemistry

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