Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning

Abstract

Plant disease poses a serious threat to global food security. Accurate, high-throughput methods of quantifying disease are needed by breeders to better develop resistant plant varieties and by researchers to better understand the mechanisms of plant resistance and pathogen virulence. Northern leaf blight (NLB) is a serious disease affecting maize and is responsible for significant yield losses. A Mask R-CNN model was trained to segment NLB disease lesions in unmanned aerial vehicle (UAV) images. The trained model was able to accurately detect and segment individual lesions in a hold-out test set. The mean intersect over union (IOU) between the ground truth and predicted lesions was 0.73, with an average precision of 0.96 at an IOU threshold of 0.50. Over a range of IOU thresholds (0.50 to 0.95), the average precision was 0.61. This work demonstrates the potential for combining UAV technology with a deep learning-based approach for instance segmentation to provide accurate, high-throughput quantitative measures of plant disease.

Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data

“…Since no prior studies have attempted to map VFFs using automated methods, we are not able to relate our findings to any prior studies that have explored this specific task; however, our findings do reinforce those of Tier et al [6] and Behrens et al [48], which note the value of CNNs for extracting features from digital terrain data. More broadly, this study supports prior findings that CNNs in general and Mask R-CNN specifically are of great value for mapping features with a unique spatial, contextual, or textural signature and that may not be spectrally separable from other classes or features [51,52,84,85].…”

“…Zhao et al [37] found that Mask R-CNN outperformed UNet for pomegranate tree canopy segmentation. Stewart et al [52] used the method to detect lesions on maize plants from northern leaf blight using unmanned aerial vehicle (UAV) data. Given the small number of studies that have applied this algorithm to remotely sensed data, there is a need for further exploration of this algorithm within the discipline.…”

“…For example, Zhang et al [51] note optimal performance after eight epochs when using Mask R-CNN to predict artic ice wedge extents from high spatial resolution aerial imagery, and Stewart al. [52] used only six epochs to map northern leaf blight lesions from UAV data. For a complete explanation of these metrics see [38] and [39].…”

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

“…Deep learning (DL) algorithms have been successfully applied in various aspects in remote sensing and have contributed to domains such as image classification, object auto-detection, image fusion, and registration [ 74 ]. The supervised DL model commonly requires a greater number of training data and it has more layers and depth than ML [ 75 ]. DL can be used in monitoring the growth conditions of maize and yield predictions as it can obtain higher precision [ 76 ].…”

Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM

“…The semantic segmentation networks based on CNNs are widely applied in the recognition of buildings [38][39][40][41], the extraction of cadastral boundaries [42] and the land use or land cover change [43,44]. The applications are also expanded to the recognition of the agricultural plants [45], pests and diseases [46,47], especially the Refs. [48] introduced the attention mechanism to realize a better segmentation by inhibiting the low-level features noise throughout the high-level features.…”