A Deep Learning Image Augmentation Method for Field Agriculture

Abstract

Vision-based smart agriculture is an important way to improve the efficiency of agricultural production. Labeling images for deep learning in complex field photos is a difficult task. In this paper, an image augmentation method that can help reduce the workload of image labeling based on synthetic images is proposed. The synthetic images consisted of three parts: crop, weed, and soil. The crop and weeds were obtained automatically by Excess Green (ExG) and minimum error threshold segmentation. The data augmentation method was tested on image classification, object detection, and semantic segmentation tasks by Resnet, YOLOV5, and DeeplabV3. The accuracy of the classification model reached 0.99. The IoU of object detection and semantic segmentation were 0.98 and 0.96, respectively. The results showed that the method in this paper was acceptable despite slight overfitting. This method was proposed based on the characteristics of field images, and it was meant for reducing the workload of labeling images.

Keywords

• Data augmentation
• deep learning
• field
• synthetic samples