Towards high throughput in-field detection and quantification of wheat foliar diseases using deep learning

Reliable, quantitative information on the presence and severity of crop diseases is essential for site-specific crop management and resistance breeding. Successful analysis of leaves under naturally variable lighting, presenting multiple disorders, and across phenological stages is a critical step towards high-throughput disease assessments directly in the field. Here, we present a dataset comprising 422 high resolution images of flattened leaves captured under variable outdoor lighting with polygon annotations of leaves, leaf necrosis and insect damage as well as point annotations of Septoria tritici blotch (STB) fruiting bodies (pycnidia) and rust pustules. Based on this dataset, we demonstrate the capability of deep learning for keypoint detection of pycnidia ( F 1 = 0 . 76 ) and rust pustules ( F 1 = 0 . 77 ) combined with semantic segmentation of leaves ( I o U = 0 . 96 ), leaf necrosis ( I o U = 0 . 77 ) and insect damage ( I o U = 0 . 69 ) to reliably detect and quantify the presence of STB, leaf rusts, and insect damage on symptom level under natural outdoor conditions. An analysis of intra- and inter-annotator agreement on selected images demonstrated that the proposed method achieved a performance close to that of annotators in the majority of the scenarios. We validated the generalization capabilities of the proposed method by testing it on images of unstructured canopies acquired directly in the field and without manual interaction with single leaves. This enables significantly higher throughput and automated data acquisition, which is critical to harness the full potential of image-based disease assessments. Model predictions were in good agreement with visual assessments of in-focus regions in these images, despite the presence of new challenges such as variable orientation of leaves and more complex lighting. This underscores the principle feasibility of diagnosing and quantifying the severity of foliar diseases under field conditions using the proposed imaging setup and image processing methods. By demonstrating the ability to diagnose and quantify the severity of multiple diseases in highly complex field scenarios, we lay the groundwork for high-throughput in-field assessments of foliar diseases that can support resistance breeding and the implementation of core principles of precision agriculture. • A dataset of 422 high-resolution images of flattened leaves under variable outdoor lighting was used to train deep learning models for keypoint detection and semantic segmentation, achieving high accuracy in quantifying STB pycnidia (F1=0.76), rust pustules (F1=0.77), leaf necrosis (IoU=0.77), insect damage (IoU=0.69), and leaf areas (IoU=0.96). • The proposed method demonstrated strong generalization capabilities in diagnosing and quantifying multiple foliar diseases in complex field conditions. • This work lays the foundation for efficient, non-invasive in-field foliar disease phenotyping, supporting precision agriculture and resistance breeding.