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.

A High-Throughput Phenotyping System Using Machine Vision to Quantify Severity of Grapevine Powdery Mildew

“…The datasets used for evaluating GPMNet were acquired after the training dataset and included different grape germplasm, although Chardonnay was included in both. This approach ensured that the testing dataset was independent of the training dataset and perhaps provided a more rigorous test than randomly dividing a single image database into training, validation and test images as is commonly done [10][11][12]. Having a CNN that generalizes well to new accessions reduces the need to continually retrain the CNN, and is an important consideration when working with diverse breeding germplasm with a broad set of grape leaf characteristics.…”

“…Recent advances in deep learning CNNs have brought their performance to levels that rival human observers for correctly classifying labeled images. CNNs have been successfully applied to many biological classification problems including the classification of leaf images for species identification and the detection of different diseases and stresses [10][11][12].…”

A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning

“…In addition, if the seeds are not severely wet (seed color does not change and seeds do not swell), the relative humidity has no influence on the method of hyperspectral imaging combined deep learning. At present, deep networks have been successfully applied to plant disease identification [36][37][38], drought monitoring [39], land type classification [40], weed detection [41], and other areas of agriculture. To date, there are few reports on the identification of soybean seed varieties by deep learning, and whether it has advantages that is also unknown.…”

Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data

“…Each of these annotations consisted of a line drawn down the principal axis of a lesion. With these line annotations, we trained convolutional neural networks (CNNs) to recognize NLB lesions in images taken by hand with 96.7% accuracy (DeChant et al, 2017) and in aerial field images with 95.0% accuracy . Delineating lesion boundaries with polygons would be ideal, as such annotations can ultimately yield much more precise image segmentation than lower-resolution annotations (Bell et al, 2015).…”