Cited by Non-destructive estimation of mandarin orange fruit quality during the ripening stage using machine-learning-based spectroscopic techniques

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems DOI

Aristeidis Karras, Αναστάσιος Γιάνναρος, Christos Karras

et al.

Future Internet, Journal Year: 2024, Volume and Issue: 16(2), P. 42 - 42

Published: Jan. 25, 2024

In the context of Internet Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing extensive data produced numerous connected devices. Our study introduces a set TinyML algorithms designed developed to improve Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, TinyHybridSenseQ, operate together enhance processing, storage, quality control networks, utilizing capabilities AI. particular, TinyCleanEDF applies federated learning Edge-based cleaning anomaly detection. EdgeClusterML combines reinforcement with self-organizing maps effective clustering. CompressEdgeML uses neural networks adaptive compression. CacheEdgeML employs predictive analytics smart caching, TinyHybridSenseQ concentrates on evaluation hybrid storage strategies. experimental proposed techniques includes executing all various numbers Raspberry Pi devices ranging from one ten. The results promising as we outperform similar methods across metrics. Ultimately, anticipate that offer comprehensive efficient approach complexities IoT,

Language: Английский

Citations

Combined Influences of Edible Coating and Storage Conditions on the Quality of Fresh Dates: An Investigation and Predictive Analysis Using Artificial Neural Networks DOI

Nashi K. Alqahtani,

Bayan Alkhamis,

Tareq M. Alnemr

et al.

Heliyon, Journal Year: 2025, Volume and Issue: unknown, P. e42373 - e42373

Published: Jan. 1, 2025

Language: Английский

Citations

What can artificial intelligence approaches bring to an improved and efficient harvesting and postharvest handling of date fruit (Phoenix dactylifera L.)? A review DOI

Younés Noutfia, Ewa Ropelewska

Postharvest Biology and Technology, Journal Year: 2024, Volume and Issue: 213, P. 112926 - 112926

Published: April 6, 2024

Language: Английский

Citations

Integrating machine learning, optical sensors, and robotics for advanced food quality assessment and food processing DOI

Inhwan Lee, Luyao Ma

Food Innovation and Advances, Journal Year: 2025, Volume and Issue: 4(1), P. 65 - 72

Published: Jan. 1, 2025

Language: Английский

Citations

Spectroscopy food functionality and safety DOI

G. Jeevarathinam, J. Deepa,

P. Bhava Nishevidha

et al.

Advances in food and nutrition research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

Shelf-life assessment techniques for fruit and vegetables: Recent trends and future prospects DOI

Ahmed Islam ElManawy,

Ali Maratab,

Abdallah Ghazal

et al.

Postharvest Biology and Technology, Journal Year: 2025, Volume and Issue: 226, P. 113521 - 113521

Published: March 29, 2025

Language: Английский

Citations

Development, RSM-based modeling, and process optimization of an ultrasonic coating system for extending the storage life of fresh fruits DOI

Maged Mohammed, Nashi K. Alqahtani, Salim Ali

et al.

Frontiers in Sustainable Food Systems, Journal Year: 2024, Volume and Issue: 8

Published: June 12, 2024

Effective and innovative freshly produced preservation methods are paramount for ensuring safe sustainable food. Edible coatings fresh dates can provide an additional protective layer to enhance their shelf life extend marketability. However, the optimum coating requires a high uniformity of on fruit. The ultrasonic achieve more uniform consistent fruit surface. Therefore, this study aimed design, evaluate, optimize process parameters system (UCS) quality date palm utilizing gum Arabic as edible coating. response surface methodology (RSM) was conducted using Design-Expert software Version 13. central composite design employed determine influence eight independent variables, namely, time, air flow rate, liquid height above transducers, temperature, concentration, drying time coated fruit, storage temperature responses which were life, ripe percentage, color changes, weight loss. optimization optimal solutions enhancing quality. outcome with desirability 0.90 demonstrated that fruits concentration 9.58% at rate 1.95 m/s, transducer 0.62 cm, 40°C, 7.4 min, 30°C, 5°C resulted in extending stored 65 days 3.47 ripening 7.39 change, 4.22% validation experiment same variable levels indicated extended 60.2 ± 0.5 days, accompanied by percentage 3.4 0.4%, change 10.3 0.9, loss 5.4 0.9%. validated through rigorous experiments Khalal stage. findings showed positive slight decrease ripe,

Language: Английский

Citations

Impact of Modified Atmosphere Packaging Conditions on Quality of Dates: Experimental Study and Predictive Analysis Using Artificial Neural Networks DOI

Ahmed Abdelrahman, Salah M. Aleid, Maged Mohammed

et al.

Foods, Journal Year: 2023, Volume and Issue: 12(20), P. 3811 - 3811

Published: Oct. 17, 2023

Dates are highly perishable fruits, and maintaining their quality during storage is crucial. The current study aims to investigate the impact of conditions on dates (Khalas Sukary cultivars) at Tamer stage predict attributes using artificial neural networks (ANN). studied were modified atmosphere packing (MAP) gases (CO2, O2, N), packaging materials, temperature, time, evaluated moisture content, firmness, color parameters (L*, a*, b*, ∆E), pH, water activity, total soluble solids, microbial contamination. findings demonstrated that significantly impacted (p < 0.05) two stored date cultivars. use MAP with 20% CO2 + 80% N had a high potential decrease rate transformation growth 4 °C for both developed ANN models efficiently predicted changes closely aligned observed values under different conditions, as evidenced by low Root Mean Square Error (RMSE) Absolute Percentage (MAPE) values. In addition, reliability was further affirmed linear regression between measured values, which follow 1:1 line, R2 ranging from 0.766 0.980, demonstrate accurate estimating fruit attributes. study’s contribute food supply chain management through identification optimal predicting thereby minimizing waste enhancing safety.

Language: Английский

Citations

Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables DOI

Dawei Li,

Lin Bai,

Rong Wang

et al.

Foods, Journal Year: 2024, Volume and Issue: 13(19), P. 3025 - 3025

Published: Sept. 24, 2024

Fruits and vegetables are valued for their flavor high nutritional content, but perishability seasonality present challenges storage marketing. To address these, it is essential to accurately monitor quality predict shelf life. Unlike traditional methods, machine learning efficiently handles large datasets, identifies complex patterns, builds predictive models estimate food These can be continuously refined with new data, improving accuracy robustness over time. This article discusses key methods predicting life control of fruits vegetables, a focus on conditions, physicochemical properties, non-destructive testing. It emphasizes advances such as dataset expansion, model optimization, multi-model fusion, integration deep developments aim reduce resource waste, provide theoretical basis technical guidance the formation modern intelligent agricultural supply chains, promote sustainable green development industry, foster interdisciplinary in field artificial intelligence.

Language: Английский

Citations

Evaluating the shelf life of strawberries using a portable Vis-NIR spectrophotometer and a Reflectance Quality Index (RQI) DOI

Laura Rabasco-Vílchez, Francisco Jiménez-Jiménez, Arícia Possas

et al.

Postharvest Biology and Technology, Journal Year: 2024, Volume and Issue: 218, P. 113189 - 113189

Published: Sept. 7, 2024

Language: Английский

Citations