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Combined Experimental and Predictive Modeling of Jasmine Flower Shelf Life Using Machine Learning

Year: 2026

Type: article

Source: New Zealand Journal of Crop and Horticultural Science

DOI: 10.1002/nzc2.70000

Authors: Syed Mazar Ali, Udaykumar Nidoni, Palanimuthu, Sharangouda Hiregoudar, K. T. Ramappa, G. Ramesh, Nagraj Naik, Ambrish Ganachari, D. T. Santosh

Institutions: University of Agricultural Sciences, Bangalore, University of Agricultural Sciences Raichur, National Institute of Food Technology, Entrepreneurship and Management - Thanjavur, Centurion University of Technology and Management

Language: English

Topic: Postharvest Quality and Shelf Life Management

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: closed

OpenAlex ID: W7154403681

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Cites: 19

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