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Updated: Jun 26, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

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Published on: August 5, 2020

机器学习在园艺中的应用和预测新鲜产品损失和浪费的前景:一篇评论

Ikechukwu Kingsley Opara^1,2, Umezuruike Linus Opara^1,3, Jude A Okolie⁴

¹SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa.

Plants (Basel, Switzerland)

|May 11, 2024

概括

机器学习 (ML) 正在通过改善作物管理和预测新鲜产品浪费来彻底改变园艺. 对ML模型的进一步研究可以显著减少收获后的损失,提高粮食安全.

科学领域:

农业科学农业科学
计算机科学计算机科学
数据科学数据科学数据科学

背景情况:

新鲜产品对于营养和粮食安全至关重要.
园艺生产需要高效准确的操作.
收获后损失和浪费是全球面临的重大挑战.

研究的目的:

审查机器学习 (ML) 在园艺中的应用.
评估ML在预测和减少新鲜产品损失和浪费方面的潜力.
确定在收获后管理中对ML的未来研究方向.

主要方法:

关于ML在收获前和收获后园艺中的应用的文献综述.
分析ML算法用于分类和预测任务.
评估ML在量化收获后损失和浪费方面的作用.

主要成果:

机器学习算法在园艺任务的分类和预测方面表现令人满意.
在各种收获前和收获后的应用中,ML已经证明了其潜力.
现有的ML模型为预测和减轻产品浪费提供了基础.

结论:

需要对先进的ML模型或组合进行进一步的研究,以提高预测准确度.
在减少收获后的损失和浪费方面,ML具有显著的前景.
未来的研究应该专注于优化ML,以便在园艺供应链中实际应用.

关键词:

预测预测预测预测果子的果子的果子的果子的果子园艺园艺园艺园艺损失和浪费,以及废弃物.机器学习是机器学习.模型模型模型模型在收获后的收获.预测预测预测预测量化量化量化的量化.蔬菜蔬菜蔬菜蔬菜

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