TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits

Abstract

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.

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

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“…This approach meets all the requirements of cutting-edge AI computing and enables the efficient processing and classification of data quality [ 57 , 58 ], independent of resource-consuming cloud services or a computer for classifying new VOC-collected data [ 59 ]. In human breath analysis, the integration of TinyML and sensors as a noninvasive technique has been effective in predicting respiratory diseases such as chronic obstructive pulmonary disease (COPD) [ 60 ].…”

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

“…It estimated the fruit's quality and ripening stages, i.e., Khalal, Rutab, or spoiled. The decline in the shelf life of fresh date fruits initiates as they convert from the Khalal stage to the Rutab stage and concludes upon their beginning of rotting or ripening into the Tamr stage (Srinivasagan et al, 2023). The percentage of Rutab and the decay fruits was estimated every 3 days.…”

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

“…These capabilities, along with the ANN ability to achieve higher predictive performance as evidenced by the higher R 2 and lower RMSE values in this study, demonstrate that ANNs are a more powerful and suitable technique for establishing prediction mod-els involving complex systems with unknown mathematical equations relating multiple variables, such as the relationships between the storage conditions and quality parameters of date fruits. Srinivasagan et al [79] and Mohammed et al [80] utilized ANN regression models to accurately predict and estimate the shelf life of stored date fruits. This application of NN models offers several advantages.…”