Food Monitoring Using Adaptive Naïve Bayes Prediction in IoT

Ganjewar, Pramod D.; Barani, S.; Wagh, Sanjeev J.; Sonavane, S. S.

doi:10.1007/978-3-030-16657-1_39

Cited by 8 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Eko Ariawan and Stanley A. Makalew address the problem to create a system of sustainable algae spirulina growth monitoring; they constructed a blueprint for a smart micro-farm in 2018 [ 149 ]. At the same time, another researcher used the technology of IoT to track food quality and safety in the food supply chain and a mobile app has been successfully developed to detect the freshness of food by using a mobile phone camera [ 150 ]. Two years later, Ganjewar also used the IoT to build a food monitoring framework to prevent food spoilage due to changes in environmental conditions during the storage period.…”

Section: The Prospect Of Food Safety Research On Algaementioning

confidence: 99%

Current Status and Future Trends in Removal, Control, and Mitigation of Algae Food Safety Risks for Human Consumption

Zhuang

Chew

et al. 2022

Molecules

View full text Add to dashboard Cite

With the rapid development of the economy and productivity, an increasing number of citizens are not only concerned about the nutritional value of algae as a potential new food resource but are also, in particular, paying more attention to the safety of its consumption. Many studies and reports pointed out that analyzing and solving seaweed food safety issues requires holistic and systematic consideration. The three main factors that have been found to affect the food safety of algal are physical, chemical, and microbiological hazards. At the same time, although food safety awareness among food producers and consumers has increased, foodborne diseases caused by algal food safety incidents occur frequently. It threatens the health and lives of consumers and may cause irreversible harm if treatment is not done promptly. A series of studies have also proved the idea that microbial contamination of algae is the main cause of this problem. Therefore, the rapid and efficient detection of toxic and pathogenic microbial contamination in algal products is an urgent issue that needs to be addressed. At the same time, two other factors, such as physical and chemical hazards, cannot be ignored. Nowadays, the detection techniques are mainly focused on three major hazards in traditional methods. However, especially for food microorganisms, the use of traditional microbiological control techniques is time-consuming and has limitations in terms of accuracy. In recent years, these two evaluations of microbial foodborne pathogens monitoring in the farm-to-table chain have shown more importance, especially during the COVID-19 pandemic. Meanwhile, there are also many new developments in the monitoring of heavy metals, algal toxins, and other pollutants. In the future, algal food safety risk assessment will not only focus on convenient, rapid, low-cost and high-accuracy detection but also be connected with some novel technologies, such as the Internet of Things (artificial intelligence, machine learning), biosensor, and molecular biology, to reach the purpose of simultaneous detection.

show abstract

Section: The Prospect Of Food Safety Research On Algaementioning

confidence: 99%

Current Status and Future Trends in Removal, Control, and Mitigation of Algae Food Safety Risks for Human Consumption

Zhuang

Chew

et al. 2022

Molecules

View full text Add to dashboard Cite

show abstract

“…Work in the field of food storage deals with various aspects of cold storage and supply chain management. Instead of predicting spoilage based on observed environmental parameters, Ganjewar et al 3 proposed an automated controlling technique for managing environmental parameters using Adaptive Naïve Bayes prediction and IoT. For wide-scale deployment of quality-driven distribution, monitoring the temperature of perishable food along the supply chain using a minimal number of temperature sensors per shipment is necessary.…”

Section: Related Workmentioning

confidence: 99%

IntelliStore: IoT And AI‐based intelligent storage monitoring for perishable food

Singha

Saha

Brahma

et al. 2022

Internet Technology Letters

View full text Add to dashboard Cite

Among all the food produced annually, the loss rate for fruits and vegetables is the highest. This is due to the inability to detect critical ambient environmental parameters in cold storage. The self‐life of food plays an important characteristic in minimizing loss. We present IntelliStore in this work, an intelligent Machine Learning (ML) and Internet of Things (IoT) powered storage monitoring system that enables real‐time monitoring of temperature, humidity & CO2$$ {CO}_2 $$ concentration, and pest detection using a Passive Infrared (PIR) sensor and microphone. We collected a dataset of 10 different fruit and vegetables from the Food Quality and Analysis lab. We have experimented with SVM, Decision Tree, AdaBoost, and Gradient Boosting machine learning algorithms and have achieved the highest accuracy of 88% with SVM. Moreover, the proposed system has additional functionality to update the dataset with actual observations in the future and retraining models that would allow improvement of ML performance over time.

show abstract

“…The authors claimed to handle massive data efficiently on increase of sensors and clients [32]. An IoT-based system presented for food monitoring and adaptive naïve base model was used to make prediction about food by measuring temperature and humidity [33]. A neural network based approach was presented to predict the temperature of perishable food in the supply chain.…”

Section: Table I a Comparative Analysis Of The Existing Cold Supply Chain Monitoring Solutionsmentioning

confidence: 99%

An IoT-Based Real-Time Intelligent Monitoring and Notification System of Cold Storage

Afreen

Bajwa

2021

IEEE Access

View full text Add to dashboard Cite

The intake of the perishable fruits and vegetables (FVs) in the human diet can contribute to reduce the risk of some chronic diseases. But unfortunately, FVs loss rate is high among all the food produced annually and occurs at storage stage of post-harvest life cycle. One of the key factors contributing to this high loss rate is inability to gauge vital ambient environmental parameters in cold storage. The existing monitoring solutions about cold storage are limited to only gauge temperature, relative humidity and ignore other vital ambient environmental parameters such as luminosity and concentration of gases. This is a critical issue that needs to be addressed to overcome the loss rate of FVs. This paper presents a real-time intelligent monitoring and notification system (RT-IMNS) banked on an Internet of Things (IoT)enabled approach for real-time monitoring of temperature, relative humidity, luminosity and concentration of gas in cold storage and notifies the personnel on exceeding of dangerous limits of these parameters. Moreover, decision support is implemented in the RT-IMNS using Artificial Neural Network (ANN) with forward propagation to classify the status of commodity into one of three classes i.e. good, unsatisfactory or alarming. The proposed prediction model outperforms Compress Sending (CS), Adaptive Naïve Bayes (ANB), Extreme Gradient Boosting (XGBoost) and Data Mining (DM) with respect to forecasting accuracy. We achieved 99% accuracy using forward propagation neural network model while existing models such as CS, ANB, XGBoost, DM achieved 95.60%, 87.50%, 93.59%, 90% accuracy respectively. Moreover, proposed approach achieved 100% precision, 100% recall, 100% F1-score for good class is achieved, for unsatisfactory class precision is 98%, recall is 99%, F1-score is 98% and for alarming class precision is 100%, recall is 98% and F1-score is 99%.

show abstract

Food Monitoring Using Adaptive Naïve Bayes Prediction in IoT

Cited by 8 publications

References 4 publications

Current Status and Future Trends in Removal, Control, and Mitigation of Algae Food Safety Risks for Human Consumption

Current Status and Future Trends in Removal, Control, and Mitigation of Algae Food Safety Risks for Human Consumption

IntelliStore: IoT And AI‐based intelligent storage monitoring for perishable food

An IoT-Based Real-Time Intelligent Monitoring and Notification System of Cold Storage

Contact Info

Product

Resources

About