Plant Disease Segmentation Networks for Fast Automatic Severity Estimation Under Natural Field Scenarios

Abstract

The segmentation of plant disease images enables researchers to quantify the proportion of disease spots on leaves, known as disease severity. Current deep learning methods predominantly focus on single diseases, simple lesions, or laboratory-controlled environments. In this study, we established and publicly released image datasets of field scenarios for three diseases: soybean bacterial blight (SBB), wheat stripe rust (WSR), and cedar apple rust (CAR). We developed Plant Disease Segmentation Networks (PDSNets) based on LinkNet with ResNet-18 as the encoder, including three versions: ×1.0, ×0.75, and ×0.5. The ×1.0 version incorporates a 4 × 4 embedding layer to enhance prediction speed, while versions ×0.75 and ×0.5 are lightweight variants with reduced channel numbers within the same architecture. Their parameter counts are 11.53 M, 6.50 M, and 2.90 M, respectively. PDSNetx0.5 achieved an overall F1 score of 91.96%, an Intersection over Union (IoU) of 85.85% for segmentation, and a coefficient of determination (R2) of 0.908 for severity estimation. On a local central processing unit (CPU), PDSNetx0.5 demonstrated a prediction speed of 34.18 images (640 × 640 pixels) per second, which is 2.66 times faster than LinkNet. Our work provides an efficient and automated approach for assessing plant disease severity in field scenarios.

Plant phenotyping relevance

植物病害画像から病斑割合と病害重症度を推定する画像セグメンテーション手法を開発し、野外データセット、精度、速度を評価しており、植物表現型取得法が中心である。

Code and data availability

The paper's field-scenario plant disease image dataset (SBB, WSR, CAR with three-color pixel labels) is publicly released on Kaggle via DOI, as stated in the Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited.