Improving weeds identification with a repository of agricultural pre-trained deep neural networks
Improving weeds identification with a repository of agricultural pre-trained deep neural networks
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DOI：
10.1016/j.compag.2020.105593
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发表时间：
2020-08-01
影响因子：
8.3
通讯作者：
Fountas, Spyros
中科院分区：
文献类型：
--
作者：
Espejo-Garcia, Borja;Mylonas, Nikolaos;Fountas, Spyros
如今，农业深度学习领域的一些研究通过微调神经网络在杂草识别方面获得了高性能，这些神经网络之前是在包含与农业无关的图像的通用数据集上进行训练的。这项工作探讨了是否可以通过微调在农业数据集而不是 ImageNet 上预训练的神经网络来进一步提高这些成就。实验结果表明，采用建议的方法可以提高整体性能。 Xception 和 Inception-Resnet 等架构分别提高了 0.51% 和 1.89%，同时减少了 13.67% 的 epoch 数量。然后有人认为，应该开发一个农业存储库来参与研究，使其预先训练的神经网络公开可用，以促进研究进展和效率。
Nowadays, several studies in the field of deep learning in agriculture obtain high performances in weeds identification by fine-tuning neural networks, previously trained on general-purpose datasets containing images unrelated to agriculture. This work examines whether these achievements could be further improved by fine-tuning neural networks pre-trained on agricultural datasets instead of ImageNet. The experimental results showed that with the suggested method the overall performance can increase. Some architectures such as Xception and Inception-Resnet presented an improvement of 0.51% and 1.89% respectively, while reducing the number of epochs by 13.67%. It is then argued that an agricultural repository should be developed to engage research into making their pre-trained neural networks publicly available, for the benefit of research progress and efficiency.