Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning

Northern leaf blight (NLB) can cause severe yield loss in maize; however, scouting large areas to accurately diagnose the disease is time consuming and difficult. We demonstrate a system capable of automatically identifying NLB lesions in field-acquired images of maize plants with high reliability. This approach uses a computational pipeline of convolutional neural networks (CNNs) that addresses the challenges of limited data and the myriad irregularities that appear in images of field-grown plants. Several CNNs were trained to classify small regions of images as containing NLB lesions or not; their predictions were combined into separate heat maps, then fed into a final CNN trained to classify the entire image as containing diseased plants or not. The system achieved 96.7% accuracy on test set images not used in training. We suggest that such systems mounted on aerial- or ground-based vehicles can help in automated high-throughput plant phenotyping, precision breeding for disease resistance, and reduced pesticide use through targeted application across a variety of plant and disease categories.

Chad DeChant, Tyr Wiesner-Hanks, Siyuan Chen, Ethan L. Stewart, Jason Yosinski, Michael A. Gore, Rebecca J. Nelson, and Hod Lipson

Phytopathology 107, 2017

‘Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning’ (PDF)

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