Zhang Yan, Wa Shiyun, Zhang Longxiang, Lv Chunli
College of Information and Electrical Engineering, China Agricultural University, Beijing, China.
College of Science, China Agricultural University, Beijing, China.
Front Plant Sci. 2022 May 26;13:875693. doi: 10.3389/fpls.2022.875693. eCollection 2022.
The detection of plant disease is of vital importance in practical agricultural production. It scrutinizes the plant's growth and health condition and guarantees the regular operation and harvest of the agricultural planting to proceed successfully. In recent decades, the maturation of computer vision technology has provided more possibilities for implementing plant disease detection. Nonetheless, detecting plant diseases is typically hindered by factors such as variations in the illuminance and weather when capturing images and the number of leaves or organs containing diseases in one image. Meanwhile, traditional deep learning-based algorithms attain multiple deficiencies in the area of this research: (1) Training models necessitate a significant investment in hardware and a large amount of data. (2) Due to their slow inference speed, models are tough to acclimate to practical production. (3) Models are unable to generalize well enough. Provided these impediments, this study suggested a Tranvolution detection network with GAN modules for plant disease detection. Foremost, a generative model was added ahead of the backbone, and GAN models were added to the attention extraction module to construct GAN modules. Afterward, the Transformer was modified and incorporated with the CNN, and then we suggested the Tranvolution architecture. Eventually, we validated the performance of different generative models' combinations. Experimental outcomes demonstrated that the proposed method satisfyingly achieved 51.7% (), 48.1% (), and 50.3% (), respectively. Furthermore, the SAGAN model was the best in the attention extraction module, while WGAN performed best in image augmentation. Additionally, we deployed the proposed model on Hbird E203 and devised an intelligent agricultural robot to put the model into practical agricultural use.
植物病害检测在实际农业生产中至关重要。它能仔细检查植物的生长和健康状况，并确保农业种植的正常运作和收获顺利进行。近几十年来，计算机视觉技术的成熟为实现植物病害检测提供了更多可能性。然而，植物病害检测通常受到诸如图像采集时光照和天气变化以及一张图像中含有病害的叶片或器官数量等因素的阻碍。同时，传统的基于深度学习的算法在该研究领域存在多个缺陷：（1）训练模型需要大量硬件投资和大量数据。（2）由于推理速度慢，模型难以适应实际生产。（3）模型的泛化能力不够强。鉴于这些障碍，本研究提出了一种带有GAN模块的Tranvolution检测网络用于植物病害检测。首先，在主干网络之前添加一个生成模型，并在注意力提取模块中添加GAN模型以构建GAN模块。然后，对Transformer进行修改并与CNN结合，进而提出Tranvolution架构。最后，我们验证了不同生成模型组合的性能。实验结果表明，所提方法分别令人满意地达到了51.7%（）、48.1%（）和50.3%（）。此外，SAGAN模型在注意力提取模块中表现最佳，而WGAN在图像增强方面表现最佳。此外，我们将所提模型部署在Hbird E203上，并设计了一个智能农业机器人将该模型应用于实际农业生产。
