Semantic Segmentation of Crop and Weed using an Encoder-Decoder Network and Image Enhancement Method under Uncontrolled Outdoor Illumination

Abstract

Weeds are among the major factors that could harm crop yield. Site-specific weed management has become an effective tool to control weed and machine vision combined with image processing is an effective approach for weed detection. In this work, an encoder-decoder deep learning network was investigated for pixel-wise semantic segmentation of crop and weed. Different input representations including different color space transformations and color indices were compared to optimize the input of the network. Three image enhancement methods were investigated to improve model robustness against different lighting conditions. The results show that for images without enhancement, color space transformation and vegetation indices without NIR (Near Infrared) information did not improve the segmentation results, while inclusion of NIR information significantly improved the segmentation accuracy, indicating the effectiveness of NIR information for precise segmentation under weak lighting condition. Image enhancement improved the image quality and consequently the robustness of segmentation models against different lighting conditions. The best MIoU value for pixel-wise segmentation was 88.91% and the best mean accuracy of object-wise segmentation was 96.12%. The deep network and image enhancement methods applied in this work provided promising segmentation results for weed detection and did not need large amount of data for model training, which is suitable for site-specific weed management.

Keywords

• Weed detection
• semantic segmentation
• deep learning
• precision agriculture
• image processing