Automatic Plant Disease Detection Based on Tranvolution Detection Network With GAN Modules Using Leaf Images - Zhang, Yan; Wa, Shiyun; Zhang, Longxiang; Lv, Chunli (Frontiers in Plant Science, 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, GAN modules were constructed by adding a generative model in front of the backbone and GAN models to the attention extraction module. Afterward, the Transformer was modified and incorporated with the CNN, and then we suggested the Tranvolution architecture. Eventually, we validated different combinations' performance of generative models. From the experimental results, the proposed method satisfyingly achieves 51.7% (Precision), 48.1% (Recall) and 50.3% (mAP), respectively. Furthermore, the SAGAN model performs best in the attention extraction module, while WGAN performs best in image augmentation. Additionally, we deployed the proposed model on Hbird E203 and devised an agricultural intelligent robot to put the model into practical agricultural use.