AI Medical Compendium Journal:
Journal of the science of food and agriculture

Showing 41 to 48 of 48 articles

Detection of sunn pest-damaged wheat grains using artificial bee colony optimization-based artificial intelligence techniques.

Journal of the science of food and agriculture Nov 21, 2019
BACKGROUND: In this study, artificial intelligence models that identify sunn pest-damaged wheat grains (SDG) and healthy wheat grains (HWG) are presented. Svevo durum wheat cultivated in Konya province, Turkey is used for the process, with 150 HWG an...
Artificial Intelligence Machine Learning Neural Networks, Computer Seeds Triticum Turkey Algorithms Image Processing, Computer-Assisted
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Using artificial neural network in determining postharvest LIFE of kiwifruit.

Journal of the science of food and agriculture Jul 18, 2019
BACKGROUND: Artificial intelligence systems have been employed for the development of predictive models that estimate many agricultural processes.
Actinidia Magnesium Nitrogen Calcium Fruit Potassium Neural Networks, Computer
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Recognition pest by image-based transfer learning.

Journal of the science of food and agriculture Apr 22, 2019
BACKGROUND: Plant pests mainly refers to insects and mites that harm crops and products. There are a wide variety of plant pests, with wide distribution, fast reproduction and large quantity, which directly causes serious losses to crops. Therefore, ...
Neural Networks, Computer Machine Learning Humans Plant Weeds Insecta Crops, Agricultural Animals Image Processing, Computer-Assisted Pest Control Plant Diseases Weed Control
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Development of super-atmospheric oxidation chamber for orthodox tea processing and its validation through neural network approach.

Journal of the science of food and agriculture Mar 12, 2019
BACKGROUND: Orthodox tea is known for its distinct aroma and superior quality. However, the oxidation step that is most crucial for developing these attributes needs precise control and is also time consuming. In the present study, a super-atmospheri...
Biflavonoids Camellia sinensis Tea Color Catechin Oxidation-Reduction Polyphenols Plant Leaves Neural Networks, Computer
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The potential of computer vision, optical backscattering parameters and artificial neural network modelling in monitoring the shrinkage of sweet potato (Ipomoea batatas L.) during drying.

Journal of the science of food and agriculture Sep 11, 2017
BACKGROUND: Drying is a method used to preserve agricultural crops. During the drying of products with high moisture content, structural changes in shape, volume, area, density and porosity occur. These changes could affect the final quality of dried...
Plant Tubers Lasers Ipomoea batatas Desiccation Temperature Optical Devices Neural Networks, Computer Light Scattering, Radiation Computers
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Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms.

Journal of the science of food and agriculture Aug 8, 2017
BACKGROUND: Beer quality is mainly defined by its colour, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical...
Algorithms Image Processing, Computer-Assisted Spectrophotometry, Infrared Beer Food Analysis Machine Learning
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Grain classifier with computer vision using adaptive neuro-fuzzy inference system.

Journal of the science of food and agriculture Mar 6, 2017
BACKGROUND: A computer vision-based classifier using an adaptive neuro-fuzzy inference system (ANFIS) is designed for classifying wheat grains into bread or durum. To train and test the classifier, images of 200 wheat grains (100 for bread and 100 fo...
Photography Fuzzy Logic Triticum Neural Networks, Computer
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An assessment of the barriers to the consumers' uptake of genetically modified foods: a neural network analysis.

Journal of the science of food and agriculture Jun 9, 2015
BACKGROUND: This paper studies which of the attitudinal, cognitive and socio-economic factors determine the willingness to purchase genetically modified (GM) food, enabling the forecasting of consumers' behaviour in Andalusia, southern Spain. This cl...
Socioeconomic Factors Adult Consumer Behavior Female Male Adolescent Aged Middle Aged Health Knowledge, Attitudes, Practice Humans Risk Assessment Neural Networks, Computer Perception Food Safety Food, Genetically Modified Spain
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