An image segmentation method based on deep learning for damage assessment of the invasive weed Solanum rostratum Dunal.

• A deep convolutional neural network for the detection of Solanum rostratum Dunal. • A method for UAV image cropping to improve the accuracy and speed of weed pixel segmentation. • A method for calculating coverage that can be used to assess the extent of invasive weed damage. • Analyzed the effect of UAV flight height for weeds detection. Solanum rostratum Dunal is a common invasive weed that can cause significant harm to local natural environments and ecosystems. The prerequisite for preventing and managing the invasion of Solanum rostratum Dunal is timely detection and reasonable assessment of its damage level. Therefore, this paper proposes a deep learning-based image segmentation method for the detection of the invasion degree of Solanum rostratum Dunal. The Solanum rostratum Dunal images are acquired by UAV and then cropped into sub-images of the same size following a specific processing method. The sub-images are fed into a U-Net based convolutional neural network DeepSolanum-Net for processing. The pixels belonging to the Solanum rostratum Dunal plants are extracted and labeled after the sub-images pass through the DeepSolanum-Net. All the processed sub-images are stitched and reduced to the size of the original image, and all the target pixels belonging to the Solanum rostratum Dunal in the original image are segmented out from the background image. The coverage rate and the area covered by the Solanum rostratum Dunal on the ground are calculated based on the image segmentation results and the flight altitude of the UAV. A field test is executed and the test results demonstrate that the recognition precision of effective pixels of Solanum rostratum Dunal plants reach 89.95% and the recall rate reach 90.3% when the proposed method is used. [ABSTRACT FROM AUTHOR]