A Study of Deep Learning Method Opportunity on Palm Oil FFB (Fresh Fruit Bunch) Grading Methods

Wahyu Aji, K. Hawari
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引用次数: 1

Abstract

The deep learning method is a state of the art in technological developments in various fields, including in agriculture. Deep learning applications in agriculture include many things including the application of fruit grading, including the fruit of palm or palm oil FFB (Fresh Fruit Bunch). Deep learning implementation opportunity in palm oil FFB grading is open because one aspect of fresh fruit grading is based on the number of sockets (fruitless) contained in FFB. Deep learning has the ability to recognize objects, so the determination of FFB grading can be developed based on calculating sockets (fruitless) by utilizing deep learning. So far no researcher has used the number of sockets for grading FFB using deep learning. Keywords—deep learning, palm oil, ffb, grading
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棕榈油鲜果串分级方法的深度学习机会研究
深度学习方法是包括农业在内的各个领域技术发展的最新技术。深度学习在农业中的应用包括很多东西,包括水果分级的应用,包括棕榈或棕榈油的水果FFB(新鲜水果串)。棕榈油FFB分级的深度学习实现机会是开放的,因为新鲜水果分级的一个方面是基于FFB中包含的插座(无果)的数量。深度学习具有识别物体的能力,因此可以利用深度学习开发基于计算套接字(无果)的FFB分级确定。到目前为止,还没有研究人员使用深度学习的socket数量来对FFB进行评分。关键词:深度学习,棕榈油,ffb,分级
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