Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non–destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of frui...

Full description

Saved in:

Bibliographic Details
Published in:	PloS one Vol. 19; no. 8; p. e0308826
Main Authors:	Elmetwalli, Adel H., Derbala, Asaad, Alsudays, Ibtisam Mohammed, Al-Shahari, Eman A., Elhosary, Mahmoud, Elsayed, Salah, Al-Shuraym, Laila A., Moghanm, Farahat S., Elsherbiny, Osama
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 26.08.2024 Public Library of Science (PLoS)
Subjects:	Acidity Algorithms Artificial intelligence Biology and Life Sciences Carotenoids Carotenoids - analysis Carotenoids - metabolism Chlorophyll Chlorophyll - analysis Chlorophyll A - analysis Citrus - growth & development Citrus fruits Computer and Information Sciences Crop diseases Crops Datasets Decision trees Diseases and pests Engineering and Technology Feature selection Food quality Fruit - chemistry Fruit - growth & development Fruits Genetic aspects Growth Hyperspectral Imaging - methods Image processing Image quality Learning algorithms Lycopene Lycopene - analysis Machine Learning Mandarins Methods Nondestructive testing Parameter estimation Performance evaluation Physical Sciences Reflectance Research and Analysis Methods Ripening Solanum lycopersicum - growth & development Spectral reflectance Spectrum analysis Tangerine Tomatoes Vegetables Vegetation index Egypt
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!