In recent years, automatic weed control has emerged as a promising alternative for reducing the amount of herbicide applied to the field, instead of conventional spraying. The use of artificial intelligence through the implementation of deep learning for early weeds identification has been one of the engines to boost this progress. However, these techniques usually need very large datasets coping with real-world conditions, which are scarce in the agricultural domain. To address the lack of such datasets, this paper proposes a methodology that combines the use of agricultural transfer learning and the creation of artificial images by generative adversarial networks (GANs). Several architectures and configurations have been evaluated on a dataset containing images of tomato and black nightshade. The best configuration was a combination of GANs creating plausible synthetic images and the Xception network, with a performance of 99.07% on the test set and 93.23% on a noisy version of the same set. Other architectures, such as Inception or DenseNet have also been evaluated, and they obtained promising results by using GANs. According to the results, the combination of advanced transfer learning and data augmentation techniques through GANs should be deeply studied in the future with more complex datasets.

近年来，自动杂草控制已成为一种有希望的替代方法，可以代替传统的喷洒方法，减少田间除草剂的用量。通过深度学习实施人工智能以实现早期杂草识别一直是推动这一进展的引擎之一。但是，这些技术通常需要非常庞大的数据集来应对现实条件，而这些条件在农业领域是稀缺的。为了解决此类数据集的不足，本文提出了一种方法，该方法结合了农业转移学习的使用和生成对抗网络（GAN）的人工图像的创建。在包含番茄和黑色茄科植物图像的数据集上已经评估了几种架构和配置。最好的配置是GAN组合在一起创建合理的合成图像和Xception网络的组合，在测试仪上的性能为99.07％，在相同版本的嘈杂版本上的性能为93.23％。还评估了其他架构，例如Inception或DenseNet，并通过使用GAN获得了可喜的结果。根据结果​​，将来应该使用更复杂的数据集深入研究通过GAN进行的高级迁移学习和数据增强技术的结合。