Deep Learning Diagnostics of Gray Leaf Spot in Maize under Mixed Disease Field Conditions

Abstract

Maize yields worldwide are limited by foliar diseases that could be fungal, oomycete, bacterial, or viral in origin. Correct disease identification is critical for farmers to apply the correct control measures, such as fungicide sprays. Deep learning has the potential for automated disease classification from images of leaf symptoms. We aimed to develop a classifier to identify gray leaf spot (GLS) disease of maize in field images where mixed diseases were present (18,656 images after augmentation). In this study, we compare deep learning models trained on mixed disease field images with and without background subtraction. Performance was compared with models trained on PlantVillage images with single diseases and uniform backgrounds. First, we developed a modified VGG16 network referred to as "GLS_net" to perform binary classification of GLS, which achieved a 73.4% accuracy. Second, we used MaskRCNN to dynamically segment leaves from backgrounds in combination with GLS_net to identify GLS, resulting in a 72.6% accuracy. Models trained on PlantVillage images were 94.1% accurate at GLS classification with the PlantVillage testing set but performed poorly with the field image dataset (55.1% accuracy). In contrast, the GLS_net model was 78% accurate on the PlantVillage testing set. We conclude that deep learning models trained with realistic mixed disease field data obtain superior degrees of generalizability and external validity when compared to models trained using idealized datasets.

Plant phenotyping relevance

トウモロコシ葉の病徴画像から灰色斑点病を識別する深層学習手法を開発・比較し、背景分離や外部妥当性も評価しているため、植物病害フェノタイピング手法が中心です。

Code and data availability

The paper publicly releases its two paper-specific phenotyping image assets: the In-field (IF) dataset of 2332 field-captured, expert-labelled maize leaf images on Kaggle, and the IFL leaf-segmentation annotation dataset on segments.ai. The supplementary materials contain only performance figures/tables, not datasets,