Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Affordable High Throughput Field Detection of Wheat Stripe Rust Using Deep Learning with Semi-Automated Image Labeling

Zhou Tang
* ,
Meinan Wang
,
Michael Schirrmann
ORCID logo ,
Karl-Heinz Dammer
,
Xianran Li
,
Robert Brueggeman
,
Sindhuja Sankaran
,
Arron Carter
ORCID logo ,
Michael Pumphrey
,
Yang Hu
* ORCID logo ,
Xianming Chen
* ,
Zhiwu Zhang
ORCID logo
Version 1 : Received: 18 April 2022 / Approved: 19 April 2022 / Online: 19 April 2022 (07:44:29 CEST)

A peer-reviewed article of this Preprint also exists.

Tang, Z.; Wang, M.; Schirrmann, M.; Dammer, K.-H.; Li, X.; Brueggeman, R.; Sankaran, S.; Carter, A. H.; Pumphrey, M. O.; Hu, Y.; et al. Affordable High Throughput Field Detection of Wheat Stripe Rust Using Deep Learning with Semi-Automated Image Labeling. Computers and Electronics in Agriculture, 2023, 207, 107709. https://doi.org/10.1016/j.compag.2023.107709. Tang, Z.; Wang, M.; Schirrmann, M.; Dammer, K.-H.; Li, X.; Brueggeman, R.; Sankaran, S.; Carter, A. H.; Pumphrey, M. O.; Hu, Y.; et al. Affordable High Throughput Field Detection of Wheat Stripe Rust Using Deep Learning with Semi-Automated Image Labeling. Computers and Electronics in Agriculture, 2023, 207, 107709. https://doi.org/10.1016/j.compag.2023.107709.

Abstract

Stripe rust ​​(caused by Puccinia striiformis f. sp. tritici) is one of the most devastating diseases of wheat and causes large-scale epidemics and severe yield loss. Applying fungicides during early epidemic development is crucial to controlling the disease but is often challenged by resource-limited human visual scouting. Deep learning has the potential to process images and videos captured from affordable devices to empower high-throughput phenotyping for early detection of stripe rust for timely application of fungicides and improve control efficiency. Here, we developed RustNet, a neural network-based image classifier, for efficiently monitoring fields for stripe rust. RustNet was built on a ResNet-18 architecture pre-trained with ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) dataset using transfer learning. RGB images and videos of multiple wheat fields with different wheat types (winter and spring wheat), conditions (irrigated and non-irrigated), and locations were acquired using smartphones or unmanned aerial vehicles near the canopy. A semi-automated image labeling approach was conducted to improve labeling efficiency by combining automated machine labeling and human correction. Cross-validations across multiple categories (sensor platforms, wheat types, and locations) achieved Area Under Curve from 0.72 to 0.87. Independent validation on a published dataset from Germany achieved accuracies ranging from 0.79 to 0.86. The visualization of the last convolutional layer of RustNet demonstrated the identification of pixels with stripe rust. RustNet is freely available at https://zzlab.net/RustNet.

Keywords

Plant disease; Machine vision; UAV; Smartphone; Convolutional Neural Network

Subject

Biology and Life Sciences, Agricultural Science and Agronomy

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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