Deep Learning
Deep Learning
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DOI：
10.4018/978-1-5225-9096-5.ch007
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
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文献类型：
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作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
深度学习方法已被发现适用于农业领域，并成功应用于通过植物疾病感染蔬菜。在本章中，作者讨论了一些广泛使用的深度学习架构及其实际应用。如今，在机器视觉的许多典型应用中，有一种趋势是用深度学习算法取代经典技术。好处是有价值的;一方面，它避免了对专门的手工特征提取器的需要，另一方面，结果不会被破坏。而且，它们通常会得到改善。
Deep learning approaches have been found to be suitable for the agricultural field with successful applications to vegetable infection through plant disease. In this chapter, the authors discuss some widely used deep learning architecture and their practical applications. Nowadays, in many typical applications of machine vision, there is a tendency to replace classical techniques with deep learning algorithms. The benefits are valuable; on one hand, it avoids the need of specialized handcrafted features extractors, and on the other hand, results are not damaged. Moreover, they typically get improved.