ReviewFoods (Basel, Switzerland)2021
Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality.
Review in Foods (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Biotic Stress Resistance in Sweet Potato: Mechanisms, Perspectives, and Sustainable Production Strategies.Plants (Basel, Switzerland) · 2026Review
- Leveraging unmanned aerial vehicle derived multispectral data for improved genomic prediction in potato (Solanum tuberosum).The plant genome · 2025Article
- Automation and Optimization of Food Process Using CNN and Six-Axis Robotic Arm.Foods (Basel, Switzerland) · 2024Article
- Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review.Plants (Basel, Switzerland) · 2024Review
- ScabyNet, a user-friendly application for detecting common scab in potato tubers using deep learning and morphological traits.Scientific reports · 2024Article
- Using machine learning for image-based analysis of sweetpotato root sensory attributes.Smart agricultural technology · 2023Article
- Effect of potassium fertilization on storage root number, yield, and appearance quality of sweet potato (Frontiers in plant science · 2023Article
- Assessing and Quantifying the Surface Texture of Milk Powder Using Image Processing.Foods (Basel, Switzerland) · 2022Article
- Live-cell fluorescence spectral imaging as a data science challenge.Biophysical reviews · 2022Review
- A Rapid Non-Destructive Hyperspectral Imaging Data Model for the Prediction of Pungent Constituents in Dried Ginger.Foods (Basel, Switzerland) · 2022Article
- Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review.Biosensors · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.