Environmental assessment of soluble solids contents and pH of orange using hyperspectral method and machine learning
Authors:
- Mansour RasekhMRMansour Rasekh
- Sina ArdabiliSASina Ardabili
- Amir MosaviAmir Mosavi
Open Access
- 27 August 2024
- journal article
- research article
- Published by Elsevier in Smart Agricultural Technology
- Vol. 9
Abstract
No abstract availableKeywords
This publication has 14 references indexed in Scilit:
- Non-destructive visible and short-wave near-infrared spectroscopic data estimation of various physicochemical properties of Fuji apple (Malus pumila) fruits at different maturation stagesChemometrics and Intelligent Laboratory Systems, 2020
- Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral imagingFood Chemistry, 2020
- Environmental advantages of visible and near infrared spectroscopy for the prediction of intact olive ripenessBiosystems Engineering, 2020
- Multiyear life energy and life cycle assessment of orange production in IranEnvironmental Science and Pollution Research, 2019
- Rapid Detection and Visualization of Mechanical Bruises on “Nanfeng” Mandarin Using the Hyperspectral Imaging Combined with ICA_LSQ MethodFood Analytical Methods, 2019
- Relief-based feature selection: Introduction and reviewJournal of Biomedical Informatics, 2018
- Non-destructive sensing methods for quality assessment of on-tree fruits: a reviewJournal of Food Measurement and Characterization, 2017
- A novel cytoprotective antioxidant: 4-Hydroxyisophthalic acidFood Chemistry, 2012
- The New International Standards for Life Cycle Assessment: ISO 14040 and ISO 14044The International Journal of Life Cycle Assessment, 2006
- Random ForestsMachine Learning, 2001