Crop disease is widely considered as one of the most pressing challenges for food crops, and therefore an accurate crop disease detection algorithm is highly desirable for its sustainable management. The recent use of remote sensing and deep learning is drawing increasing research interests in wheat yellow rust disease detection. However, current solutions on yellow rust detection are generally addressed by RGB images and the basic semantic segmentation algorithms (e.g., UNet), which do not consider the irregular and blurred boundary problems of yellow rust area therein, restricting the disease segmentation performance. Therefore, this work aims to develop an automatic yellow rust disease detection algorithm to cope with these boundary problems. An improved algorithm entitled Ir-UNet by embedding irregular encoder module (IEM), irregular decoder module (IDM) and content-aware channel re-weight module (CCRM) is proposed and compared against the basic UNet while with various input features. The recently collected dataset by DJI M100 UAV equipped with RedEdge multispectral camera is used to evaluate the algorithm performance. Comparative results show that the Ir-UNet with five raw bands outperforms the basic UNet, achieving the highest overall accuracy (OA) score (97.13%) among various inputs. Moreover, the use of three selected bands, Red-NIR-RE, in the proposed Ir-UNet can obtain a comparable result (OA: 96.83%) while with fewer spectral bands and less computation load. It is anticipated that this study by seamlessly integrating the Ir-UNet network and UAV multispectral images can pave the way for automated yellow rust detection at farmland scales.
Bibliographical noteFunding: This work was supported by the Fundamental Research Funds for the China Central Universities of USTB (FRF-DF-19-002), Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB: BK20BE014. This work was also partially supported by UK Science and Technology Facilities Council (STFC) under Newton fund with Grant ST/V00137X/1.
Data Availability StatementNo new data were created or analyzed in this study. Data sharing is
not applicable to this article.
- deep learning
- crop disease detection
- multispectral imagery
- unmanned aerial vehicle (UAV)
- LEAF CHLOROPHYLL CONTENT
- VEGETATION INDEX