ArticleSensors (Basel, Switzerland)2023
TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Beyond predictive accuracy: A case for mechanism-informed, uncertainty-aware machine learning in food microbiology.iScience · 2026Review
- Non-destructive yield estimation of onion and garlic using UAV-based hyperspectral imaging and hybrid machine learning models.BMC plant biology · 2026Article
- Image-Based Machine Learning for Predicting Acceptability Limits in Frozen Pizza Shelf Life.Foods (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Edible Mushroom Cultivation, Breeding, and Classification: A Comprehensive Review.Journal of fungi (Basel, Switzerland) · 2025Review
- Efficient human activity recognition on edge devices using DeepConv LSTM architectures.Scientific reports · 2025Article
- Evaluation of Low-Cost Multi-Spectral Sensors for Measuring Chlorophyll Levels Across Diverse Leaf Types.Sensors (Basel, Switzerland) · 2025Article
- Article
- Edge intelligence for poultry welfare: Utilizing tiny machine learning neural network processors for vocalization analysis.PloS one · 2025Article
- Non-Destructive Monitoring of External Quality of Date Palm Fruit (Sensors (Basel, Switzerland) · 2024Article
- Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables.Foods (Basel, Switzerland) · 2024Review
- Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose.Sensors (Basel, Switzerland) · 2024Article
- Impact of Modified Atmosphere Packaging Conditions on Quality of Dates: Experimental Study and Predictive Analysis Using Artificial Neural Networks.Foods (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fresh dates have a limited shelf life and are susceptible to spoilage, which can lead to economic losses for producers and suppliers. The problem of accurate shelf life estimation for fresh dates is essential for various stakeholders involved in the production, supply, and consumption of dates. Modified atmosphere packaging (MAP) is one of the essential methods that improves the quality and increases the shelf life of fresh dates by reducing the rate of ripening. Therefore, this study aims to apply fast and cost-effective non-destructive techniques based on machine learning (ML) to predict and estimate the shelf life of stored fresh date fruits under different conditions. Predicting and estimating the shelf life of stored date fruits is essential for scheduling them for consumption at the right time in the supply chain to benefit from the nutritional advantages of fresh dates. The study observed the physicochemical attributes of fresh date fruits, including moisture content, total soluble solids, sugar content, tannin content, pH, and firmness, during storage in a vacuum and MAP at 5 and 24 ∘C every 7 days to determine the shelf life using a non-destructive approach. TinyML-compatible regression models were employed to predict the stages of fruit development during the storage period. The decrease in the shelf life of the fruits begins when they transition from the Khalal stage to the Rutab stage, and the shelf life ends when they start to spoil or ripen to the Tamr stage. Low-cost Visible-Near-Infrared (VisNIR) spectral sensors (AS7265x-multi-spectral) were used to capture the internal physicochemical attributes of the fresh fruit. Regression models were employed for shelf life estimation. The findings indicated that vacuum and modified atmosphere packaging with 20% CO
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