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The food production system is vulnerable to diseases more than ever, and the threat is increasing in an era of climate change that creates more favorable conditions for emerging diseases. Fortunately, scientists and engineers are making great strides to introduce farming innovations to tackle the challenge. Unmanned aerial vehicle (UAV) remote sensing is among the innovations and thus is widely applied for crop health monitoring and phenotyping. This study demonstrated the versatility of aerial remote sensing in diagnosing yellow rust infection in spring wheats in a timely manner and determining an intervenable period to prevent yield loss. A small UAV equipped with an aerial multispectral sensor periodically flew over, and collected remotely sensed images of, an experimental field in Chacabuco (−34.64; −60.46), Argentina during the 2021 growing season. Post-collection images at the plot level were engaged in a thorough feature-engineering process by handcrafting disease-centric vegetation indices (VIs) from the spectral dimension, and grey-level co-occurrence matrix (GLCM) texture features from the spatial dimension. A machine learning pipeline entailing a support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) was constructed to identify locations of healthy, mild infection, and severe infection plots in the field. A custom 3-dimensional convolutional neural network (3D-CNN) relying on the feature learning mechanism was an alternative prediction method. The study found red-edge (690–740 nm) and near infrared (NIR) (740–1000 nm) as vital spectral bands for distinguishing healthy and severely infected wheats. The carotenoid reflectance index 2 (CRI2), soil-adjusted vegetation index 2 (SAVI2), and GLCM contrast texture at an optimal distance d = 5 and angular direction θ = 135° were the most correlated features. The 3D-CNN-based wheat disease monitoring performed at 60% detection accuracy as early as 40 days after sowing (DAS), when crops were tillering, increasing to 71% and 77% at the later booting and flowering stages (100–120 DAS), and reaching a peak accuracy of 79% for the spectral-spatio-temporal fused data model. The success of early disease diagnosis from low-cost multispectral UAVs not only shed new light on crop breeding and pathology but also aided crop growers by informing them of a prevention period that could potentially preserve 3–7% of the yield at the confidence level of 95%.
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Detecting intra-field variation in rice yield with UAV imagery and deep learning
Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural systems, but guidance for this integration is currently limited. Here we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas. Both deep learning strategies showed improvements upon traditional statistical learning approaches including linear regression and gradient boosted decision trees. First, we explicitly accounted for variation across developmental stages using a 3D convolutional neural network (CNN) architecture that captures both spatial and temporal dimensions of UAV images from multiple time points throughout one growing season. 3D-CNNs achieved low prediction error on the test set, with a Root Mean Squared Error (RMSE) of 8.8% of the mean yield. For the second strategy, a 2D-CNN, we considered only spatial relationships among pixels for image features acquired during a single flyover. 2D-CNNs trained on images from a single day were most accurate when images were taken during booting stage or later, with RMSE ranging from 7.4 to 8.2% of the mean yield. A primary benefit of convolutional autoencoder-like models (based on analyses of prediction maps and feature importance) is the spatial denoising effect that corrects yield predictions for individual pixels based on the values of vegetation index and thermal features for nearby pixels. Our results highlight the promise of convolutional autoencoders for UAV-based yield prediction in rice.
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- PAR ID:
- 10319169
- Date Published:
- Journal Name:
- Frontiers in plant science
- ISSN:
- 1664-462X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fpls.2022.716506
