AI-based soft-sensor for shelf life prediction of ‘Kesar’ mango

Dutta, Jayita; Deshpande, Parijat; Rai, Beena

doi:10.1007/s42452-021-04657-7

Cited by 15 publications

(9 citation statements)

References 17 publications

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“…A dynamic pricing model based on real-time IoT sensor data can enable retailers to choose prices at various moments throughout the sales season (Kayikci et al. , 2022; Dutta et al ., 2021).…”

Section: Results and Analysismentioning

confidence: 99%

“…In selling season, hyperspectral imaging sensors can be used for monitoring the freshness of the FV and prices can be decided depending on the freshness scores. A dynamic pricing model based on realtime IoT sensor data can enable retailers to choose prices at various moments throughout the sales season (Kayikci et al, 2022;Dutta et al, 2021).…”

Section: Network/thematic Analysismentioning

confidence: 99%

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Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Yuvaraj

Basu

Abdulrahman

et al. 2023

IMDS

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PurposeInformation and communication technology (ICT) implementation has demonstrated usefulness in supply chain coordination and efficiency optimization in various industries and sectors. This study investigates the extent of ICT deployment in fruits and vegetable supply chains (FVSC) from “farm-to-fork” to ensure food security.Design/methodology/approachThis paper employs a systematic literature review (SLR) methodology and identified a total of 99 journal articles ranging from 2001 to April 2023 for analysis. The reviewed articles have been classified based on the framework proposed from the perspective of food security. Bibliometric and content analysis is carried out with the final list of articles to extract useful insights.FindingsThe findings reveal that ICT implementation in FVSC is a relatively new research area; researchers have started investigating several aspects of ICT in FVSC through varied research methodologies. Experimental research aimed at addressing food safety and condition monitoring of fruits and vegetables (FV) has started to gain traction while theory building is yet to gain traction in the literature reviewed. Findings indicate further research is required on technologies like blockchain (BCT), artificial intelligence (AI) and machine learning (ML), especially on key objectives such as food security, and the triple-bottom-line approach of sustainability. It also indicates that implementing relevant ICTs in FVSC can help delay, if not avert, the food crisis predicted by Malthusian theory.Research limitations/implicationsThis study used only well-established databases to ensure quality of the studies examined. There is a possibility of missing out on articles from other sources not considered. As a result, future SLR studies may employ additional databases, such as Springer Link, Taylor and Francis, Emerald Insight and Google Scholar. Other methodologies such as expert interviews and extra empirical methodologies may also be employed to give a more balanced picture and insights into ICTs implementation in FVSC.Practical implicationsThis study offers a summative detail of the status of ICT implementation in FVSC and can serve as a reference guide for stakeholders in developing strategies for efficient FVSC management. This research work highlights the impact of ICT implementation in FVSC on the four pillars of food security which include improved availability, accessibility, utilization and stability.Originality/valueThis study focuses on ICT implementation for food security in FVSC. The SLR highlights the gaps and proffers potential solutions that enhance global efforts on food security through ICT-enabled reduction in food waste and food loss in FVSC.

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Section: Results and Analysismentioning

confidence: 99%

Section: Network/thematic Analysismentioning

confidence: 99%

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Yuvaraj

Basu

Abdulrahman

et al. 2023

IMDS

View full text Add to dashboard Cite

show abstract

“…These applications can be adapted to the issue of (food) shelf-life prediction in future studies. In the context of the determination of food shelf-life, efforts to treat data with AI-based soft-sensors have been made [107]. However, the combination with an omics-based technology or to be more precise a MS-based data is not yet performed.…”

Section: Prediction Of Shelf-lifementioning

confidence: 99%

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Class

Kuhnen

Rohn

et al. 2021

Foods

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Deep learning is a trending field in bioinformatics; so far, mostly known for image processing and speech recognition, but it also shows promising possibilities for data processing in food analysis, especially, foodomics. Thus, more and more deep learning approaches are used. This review presents an introduction into deep learning in the context of metabolomics and proteomics, focusing on the prediction of shelf-life, food authenticity, and food quality. Apart from the direct food-related applications, this review summarizes deep learning for peptide sequencing and its context to food analysis. The review’s focus further lays on MS (mass spectrometry)-based approaches. As a result of the constant development and improvement of analytical devices, as well as more complex holistic research questions, especially with the diverse and complex matrix food, there is a need for more effective methods for data processing. Deep learning might offer meeting this need and gives prospect to deal with the vast amount and complexity of data.

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“…It provides deeper insight into autonomous fruit supply chain management and decision-making. Using the “Temperature and Humidity” information, the maturity datasets are categorized into different relative humidity and temperature ranges (Dutta et al, 2021 ). When real-time temperature and humidity are within the range of the datasets, there is a strong correlation between actual and predicted ripeness.…”

Section: Introductionmentioning

confidence: 99%

Decentralized governance and artificial intelligence policy with blockchain-based voting in federated learning

Lee

Chow²,

Chan³

et al. 2023

Front. Res. Metr. Anal.

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IntroductionFruit losses in the supply chain owing to improper handling and a lack of proper control are common in the industry. As losses are caused by the inefficiency of the export method, selecting the appropriate export method is a possible solution. Several organizations employ only a single strategy, which is mainly based on a first-in-first-out approach. Such a policy is easy to manage but inefficient. Given that the batch of fruits may become overripe during transportation, frontline operators do not have the authority or immediate support to change the fruit dispatching strategy. Thus, this study aims to develop a dynamic strategy simulator to determine the sequence of delivery based on forecasting information projected from probabilistic data to reduce the amount of fruit loss.MethodsThe proposed method to accomplish asynchronous federated learning (FL) is based on blockchain technology and a serially interacting smart contract. In this method, each party in the chain updates its model parameters and uses a voting system to reach a consensus. This study employs blockchain technology with smart contracts to serially enable asynchronous FL, with each party in the chain updating its parameter model. A smart contract combines a global model with a voting system to reach a common consensus. Its artificial intelligence (AI) and Internet of Things engine further strengthen the support for implementing the Long Short-Term Memory (LSTM) forecasting model. Based on AI technology, a system was constructed using FL in a decentralized governance AI policy on a blockchain network platform.ResultsWith mangoes being selected as the category of fruit in the study, the system improves the cost-effectiveness of the fruit (mango) supply chain. In the proposed approach, the simulation outcomes show fewer mangoes lost (0.035%) and operational costs reduced.DiscussionThe proposed method shows improved cost-effectiveness in the fruit supply chain through the use of AI technology and blockchain. To evaluate the effectiveness of the proposed method, an Indonesian mango supply chain business case study has been selected. The results of the Indonesian mango supply chain case study indicate the effectiveness of the proposed approach in reducing fruit loss and operational costs.

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AI-based soft-sensor for shelf life prediction of ‘Kesar’ mango

Cited by 15 publications

References 17 publications

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Implementation of information and communication technologies in fruit and vegetable supply chain: a systematic literature review

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Decentralized governance and artificial intelligence policy with blockchain-based voting in federated learning

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