Abstract
Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 × 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.
Highlights
Stripe rust caused by the fungus Puccinia striiformis Westend. f. sp. tritici Eriks. (Pst) is one of the major diseases that lead to severe yield losses in wheat crops
This study differs from studies that focus on smart phone usage (Lu et al, 2017; Rupavatharam et al, 2018), because in this case the user operates like a monitoring system when directing the smartphone toward the plant anomaly of interest
Deep residual networks (ResNet-18) proved suitable to identify symptoms of the Pst disease from high resolution imagery of wheat canopies with an overall accuracy of 77% in this study
Summary
Stripe rust caused by the fungus Puccinia striiformis Westend. f. sp. tritici Eriks. (Pst) is one of the major diseases that lead to severe yield losses in wheat crops. Deep Learning Stripe Rust Detection is currently worsening because Pst has adapted to warmer conditions promoting its wider global propagation with devastating effects in major wheat-producing areas in China, Northern Africa, the Middle East, and India (Milus et al, 2009; Hovmøller et al, 2010). The use of disease-resisting cultivars is an effective and ecologically feasible way to control Pst (Chen et al, 2014; Park, 2016). New Pst races are known to appear rapidly that overcome major resistance genes in wheat varieties (Hubbard et al, 2015). If sensors were available that were able to detect Pst outbreaks reliably in the fields in early development phases, it would help to control and reduce the use of fungicides more efficiently (Tackenberg et al, 2018)
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