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

    Abstract

    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.

    Authors

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

    Publication

    Phytopathology 107, 2017

    Full Paper

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

    Uber AI

    Comments
    Previous articleVariational Gaussian Dropout is not Bayesian
    Next articleES Is More Than Just a Traditional Finite-Difference Approximator
    Jason Yosinski
    Jason Yosinski is a founding member of Uber AI Labs and there leads the Deep Collective research group. He is known for contributions to understanding neural network modeling, representations, and training. Prior to Uber, Jason worked on robotics at Caltech, co-founded two web companies, and started a robotics program in Los Angeles middle schools that now serves over 500 students. He completed his PhD working at the Cornell Creative Machines Lab, University of Montreal, JPL, and Google DeepMind. He is a recipient of the NASA Space Technology Research Fellowship, has co-authored over 50 papers and patents, and was VP of ML at Geometric Intelligence, which Uber acquired. His work has been profiled by NPR, the BBC, Wired, The Economist, Science, and the NY Times. In his free time, Jason enjoys cooking, reading, paragliding, and pretending he's an artist.