Sensor, IoT-based post-harvest shelf life determination of tomato (Lycopersicon esculentum) through machine learning predictive analysis for intelligent transport

Aim: The current research explores the potential of machine learning predictive models in optimizing the storage conditions of tomatoes. This is achieved through Internet of Things (IoT) technology, sensors, cameras, and microprocessors integrated into refrigerators along the supply chain. Methodolo...

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Bibliographic Details
Published in:	Journal of environmental biology Vol. 45; no. 4; pp. 455 - 464
Main Authors:	Shankaraswamy, J., Radhika, T.S.L.
Format:	Journal Article
Language:	English
Published:	Lucknow Triveni Enterprises 01.07.2024
Subjects:	Artificial intelligence Carbon Commodities Data collection Effectiveness Food Food quality Fruits Harvest Horticulture Humidity Internet of Things Learning algorithms Lycopersicon esculentum Machine learning Microcontrollers Microprocessors Optimization Oxygen Physiology Polynomials Prediction models Sensors Shelf life Software Software development tools Storage conditions Supply chains Temperature Tomatoes Vegetables India
ISSN:	0254-8704, 2394-0379
Online Access:	Get full text
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