Melon ripeness detection by an improved object detection algorithm for resource constrained environments-Reference-Cited by-同舟云学术

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Melon ripeness detection by an improved object detection algorithm for resource constrained environments

Published:2024-08-16 Issue:1 Volume:20 Page:
ISSN:1746-4811
Container-title:Plant Methods
language:en
Short-container-title:Plant Methods

Author:

Jing Xuebin,Wang Yuanhao,Li Dongxi,Pan Weihua

Funder

Basic Research Programs of Shanxi Province

National Natural Science Foundation of China

Shenzhen Science and Technology Program

Innovation Program of Chinese Academy of Agricultural Sciences

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1186/s13007-024-01259-3.pdf

Reference41 articles.

1. Sun M, Zhang D, Liu L, Wang Z. How to predict the sugariness and hardness of melons: a near-infrared hyperspectral imaging method. Food Chem. 2017;218:413–21. https://doi.org/10.1016/j.foodchem.2016.09.023.

2. Laur LM, Tian L. Provitamin A and vitamin C contents in selected California-grown cantaloupe and honeydew melons and imported melons. J Food Comp Anal. 2011;24(2):194–201. https://doi.org/10.1016/j.jfca.2010.07.009.

3. Kader AA. Fruit maturity, ripening, and quality relationships (1999). https://api.semanticscholar.org/CorpusID:89021673.

4. Prasad K, Jacob S, Siddiqui MW. Chapter 2 - Fruit Maturity, Harvesting, and Quality Standards. In: Siddiqui MW, editor. Preharvest Modulation of Postharvest Fruit and Vegetable Quality. Academic Press; 2018. p. 41–69. https://www.sciencedirect.com/science/article/pii/B9780128098073000020

5. Verma L, Joshi V. Post-harvest technology of fruits and vegetables. Post Harvest Technol Fruits Veg. 2000;1:1–76.

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