Enhancing corn quality prediction: Variable selection and explainable AI in spectroscopic analysis-Reference-Cited by-同舟云学术

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Enhancing corn quality prediction: Variable selection and explainable AI in spectroscopic analysis

Published:2024-08 Issue: Volume:8 Page:100458
ISSN:2772-3755
Container-title:Smart Agricultural Technology
language:en
Short-container-title:Smart Agricultural Technology

Author:

Ahmed Md. Toukir,Kamruzzaman Mohammed^ORCID

Publisher

Elsevier BV

Reference61 articles.

1. Near infrared spectroscopy: a mature analytical technique with new perspectives–A review;Pasquini;Anal. Chim. Acta,2018

2. In situ’ olive ripening monitoritation by low-cost handheld NIR;Jiménez Márquez;Smart Agric. Technol.,2023

3. Vibrational spectroscopy in analysis of pharmaceuticals: critical review of innovative portable and handheld NIR and Raman spectrophotometers;Deidda;TrAC Trends Anal. Chem.,2019

4. Comparison of short-wavelength infrared (SWIR) hyperspectral imaging system with an FT-NIR spectrophotometer for predicting alpha-amylase activities in individual Canadian Western Red Spring (CWRS) wheat kernels;Xing;Biosyst. Eng.,2011

5. Structural characterization of organo-montmorillonites prepared from a series of primary alkylamines salts: mid-IR and near-IR study;Slaný;Appl. Clay Sci.,2019

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning-based hyperspectral image reconstruction for quality assessment of agro-product;Journal of Food Engineering;2024-12

2. Comparative analysis of hyperspectral Image reconstruction using deep learning for agricultural and biological applications;Results in Engineering;2024-09

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