A Transfer Learning-based Approach to Predict the Shelf life of Fruit

Bhole, Varsha; Kumar, Arun

doi:10.4114/intartif.vol24iss67pp102-120

Cited by 8 publications

(6 citation statements)

References 32 publications

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“…The prime activities in each agriculture task and AI models used are listed below in Table 2. [12,16,22,23,25,27,36,39,[47][48][49] Post Harvesting…”

Section: Need For ML Models In Enhancing Food Sustainabilitymentioning

confidence: 99%

“…The researchers [ 17 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] have put forth a collection of deep learning (DL) and machine learning (ML) techniques to classify and grade the quality of date fruits. However, these approaches predominantly rely on cloud services for training and inference, making them unsuitable for edge computing.…”

Section: Introductionmentioning

confidence: 99%

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TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits

Srinivasagan

Mohammed

Alzahrani

2023

Sensors

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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% CO2 and N balance efficiently increased the shelf life of the stored fresh fruit to 53 days and 44 days, respectively, when maintained at 5 ∘C. However, the shelf life decreased to 44 and 23 days when the vacuum and modified atmosphere packaging with 20% CO2 and N balance were maintained at room temperature (24 ∘C). Edge Impulse supports the training and deployment of models on low-cost microcontrollers, which can be used to predict real-time estimations of the shelf life of fresh dates using TinyML sensors.

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“…The prime activities in each agriculture task and AI models used are listed below in Table 2. [12,16,22,23,25,27,36,39,[47][48][49] Post Harvesting…”

Section: Need For ML Models In Enhancing Food Sustainabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits

Srinivasagan

Mohammed

Alzahrani

2023

Sensors

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“…Internet of things (IoT) and ML applications are used to evaluate the quality of the fruit. In addition, the use of deep learning techniques to grade, classify, and predict the quality parameters of fruits has been proposed by several researchers [5,27,[37][38][39][40][41][42][43]. The decision regarding model development and deployment is determined through two methodologies: cloud computing and edge computing.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres

Mohammed,

Srinivasagan,

Alzahrani

et al. 2023

Sustainability

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The safety and quality of fresh fruits deserve the greatest attention, and are a priority for producers and consumers alike. Modern technologies are crucial to accurately estimating and predicting fresh fruits’ quality and shelf life, to optimize supply chain management. Modified atmosphere packaging (MAP) is an essential method that maintains quality parameters and increases the shelf life of fresh fruits by reducing their ripening rates. This study aimed to develop a cost-effective, non-destructive technique using tiny machine learning (TinyML) and a multispectral sensor to predict/estimate the quality parameters and shelf life of packaged fresh dates under the natural atmosphere (Control), vacuum-sealed bags (VSBs), and MAP with different gas combinations: 20% CO2 + N balance (MAP1), and 20% CO2 + 10% O2 + N balance (MAP2). The shelf life and quality parameters of the packaged fresh dates (pH, total soluble solids (TSSs), sugar content (SC), moisture content (MC), and tannin content (TC)) were evaluated under different storage temperatures and times. A multispectral sensor (AS7265x) was utilized to correlate the fruit quality parameters with spectrum analysis under the same storage conditions, to prepare the dataset to train the prediction models. The prediction models were trained in the Edge Impulse Platform, and deployed to an Arduino Nano 33 BLE sense microcontroller unit (MCU) for inference. The findings indicated that the vacuum and MAP1 efficiently increased the shelf life and maintained the quality parameters of the packaged fresh fruit to 43 ± 2.39 and 39 ± 3.34 days, respectively, at 5 °C. The optimal neural network consisted of the input layer with 20 nodes (the packaging type, storage temperature, and 18 channels of the spectral sensor data at 410 to 940 nm wavelengths), two hidden layers with 20 and 12 nodes, and an output layer with one node for the target quality parameter or shelf life. These optimal prediction models efficiently predicted the shelf life with R2 = 0.951, pH with R2 = 0.854, TSSs with R2 = 0.893, SC with R2 = 0.881, MC with R2 = 0.941, and TC with R2 = 0.909. The evaluation of the developed prediction models under each packaging condition indicated that these models serve as powerful tools for accurately predicting fruit quality parameters, and estimating the shelf life of fresh dates.

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“…But, this would need sophisticated feature identification process to evaluate fruit shelf-life using similar features that have a high color resemblance. Therefore, customized machine learning models are required to evaluate fruit's shelf-life and address the intricate features (Bhole et al, 2021) of images for particular applications.…”

Section: Introductionmentioning

confidence: 99%

Flutter-Based Real-Time Mobile App for Fruit Shelf-Life Prediction (FSP) Using Multi-Modality Imaging

Bhole

Kumar

2022

International Journal of Information Retrieval Research

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The present work uses flutter and transfer learning-based techniques for predicting the shelf-life of fruits in real-time by reducing the demand for huge number of samples and longer training durations. The work uses mango fruit as a case study, by capturing two types of images axiomatically through smartphone rear camera and a Seek thermal camera. A set of models based on transfer learning were created, then trained, and applied post-training quantization for optimizing the model size and low latency. The models were subsequently converted into TFLite format with TF-Select Ops and deployed in the Flutter app code, and thereby generating the APK files for real-time execution on mobile devices. The performance metrics of the models were evaluated on accuracy, model size, and latency. With a minor level of degradation in accuracy, the model with quantization outperforms and achieves better latency and 12x times reduction in size. The overall accuracy achieved for both the cameras (normal and Thermal) on mobile device was 90% and 93.33% respectively, for predicting the fruit shelf life.

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A Transfer Learning-based Approach to Predict the Shelf life of Fruit

Cited by 8 publications

References 32 publications

TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits

TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits

Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres

Flutter-Based Real-Time Mobile App for Fruit Shelf-Life Prediction (FSP) Using Multi-Modality Imaging

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