cogsys-tuebingen/deephs_fruit
Measuring the ripeness of fruit with Hyperspectral Imaging and Deep Learning
This project helps food quality control specialists and agricultural researchers assess fruit ripeness non-destructively. It takes hyperspectral camera images of avocados, kiwis, persimmons, papayas, or mangoes and outputs predictions for fruit flesh firmness, sugar content, and overall ripeness. Growers, distributors, and retailers can use this to make informed decisions about harvesting, storage, and sale.
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Use this if you need to train a machine learning model to automatically classify the ripeness of fruit using hyperspectral imaging data.
Not ideal if you are looking for a ready-to-use application or a model that is already trained for fruits not included in the dataset.
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Language
Python
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Last pushed
Jan 09, 2024
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