ArticleFrontiers in public health2021
An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis.
Article in Frontiers in public health, 2021. 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.
- Fungal Growth Risk Prediction and Optimal Regulation Method for Food Storage Based on the Forward Reachable Set.Foods (Basel, Switzerland) · 2026Article
- Utilization of nanosensors and nanomaterials to revolutionize the food industry: boosting product safety, quality, and sustainability.RSC advances · 2026Review
- FreshTrack: an innovative IoT-sensor-driven food freshness estimation framework integrating blockchain.Scientific reports · 2026Article
- Artificial intelligence in bread making: Applications in quality control, formulation and sensory prediction.Food chemistry: X · 2026Review
- An SFN-YOLO-based detection method for the fermentation stage in the anaerobic digestion process.iScience · 2026Article
- Probiotics and Postbiotics for Green Control of Foodborne Pathogens: Intelligent Detection and Biopreservation Strategies for Safer Foods.Foods (Basel, Switzerland) · 2025Review
- Revolutionizing the food industry: The transformative power of artificial intelligence-a review.Food chemistry: X · 2024Review
- Observational
- Development of a Novel Colorimetric pH Biosensor Based on A-Motif Structures for Rapid Food Freshness Monitoring and Spoilage Detection.Biosensors · 2024Article
- Robust deep learning method for fruit decay detection and plant identification: enhancing food security and quality control.Frontiers in plant science · 2024Article
- Integrated Smart Gas Tracking Device with Artificially Tailored Selectivity for Real-Time Monitoring Food Freshness.Sensors (Basel, Switzerland) · 2023Article
- Nano and Technological Frontiers as a Sustainable Platform for Postharvest Preservation of Berry Fruits.Foods (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a result, efficient food spoilage tracking schemes are required to sustain food quality levels. We have designed a prototype to track food quality and to manage storage systems at home. Initially, we have employed a Convolutional Neural Network (CNN) model to detect the type of fruit and veggies. Then the proposed system monitors the gas emission level, humidity level, and temperature of fruits and veggies by using sensors and actuators to check the food spoilage level. This would additionally control the environment and avoid food spoilage wherever possible. Additionally, the food spoilage level is informed to the customer by an alert message sent to their registered mobile numbers based on the freshness and condition of the food. The model employed proved to have an accuracy rate of 95%. Finally, the experiment is successful in increasing the shelf life of some categories of food by 2 days.
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Registered trials
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