Fruit Shelf-Life Estimation and Freshness  Evaluation: A Review

Author(s)	Sanika Gonjari, Dr. Gyankamal Chhajed
Country	India
Abstract	Abstract—The freshness of fruits and vegetables is a growingchallenge in daily life, as traditional inspection methods often failto provide consistent and objective results which turn into foodand health concerns. While physical inspection issubjective and inaccurate, the absence of automated freshnessdetection and shelf-life prediction results in monetary losses andinappropriate consumption. Traditional methods, such aschemical testing and human inspection, are costly, time-consuming, and often unreliable. Convolutional NeuralNetworks (CNNs), which automatically recognize visual traitslike color, texture, and form in fruit pictures, provide a powerfulalternative. In this work, a CNN-based model that candifferentiate between fresh and rotting fruits and vegetables isdeveloped using convolution, pooling, and fully connected layers.The method ensures non-invasive, scalable, and precisedetection, which lowers dependency on humans and boostsproductivity. This approach can be advantageous for automatedsorting systems and real-time quality monitoring.
Keywords	Computer vision, Image classification, Fruit detection, Convolutional neural networks (CNNs), Deep Learning.
Published In	Volume 7, Issue 3, May-June 2026
Published On	2026-05-28
DOI	https://doi.org/10.63363/aijfr.2026.v07i03.5773

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