Boulent Justine, Foucher Samuel, Théau Jérôme, St-Charles Pierre-Luc
Department of Applied Geomatics, Université de Sherbrooke, Sherbrooke, QC, Canada.
Vision and Imagery Team, Computer Research Institute of Montréal, Montréal, QC, Canada.
Front Plant Sci. 2019 Jul 23;10:941. doi: 10.3389/fpls.2019.00941. eCollection 2019.
Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research.
深度学习技术，尤其是卷积神经网络（CNN），在图像处理领域取得了显著进展。自2016年以来，已经开发了许多用于自动识别作物病害的应用程序。这些应用程序可以作为专业知识辅助或自动筛选工具开发的基础。此类工具有助于实现更可持续的农业实践和更高的粮食生产安全性。为了评估这些网络在此类应用中的潜力，我们调查了19项依靠CNN自动识别作物病害的研究。我们描述了它们的概况、主要实施方面及其性能。我们的调查使我们能够确定该研究领域工作中的主要问题和不足。我们还提供了在实际操作环境中改进CNN使用的指导方针以及未来研究的一些方向。
