Image-based field monitoring of Cercospora leaf spot in sugar beet by robust template matching and pattern recognition

Abstract

This paper presents a novel image algorithm using template matching and pattern recognition frameworks for monitoring Cercospora leaf spot (CLS) development on sugar beets on a single leaf scale under real field conditions. Due to the variety and complexity of the open field, it is a great challenge to achieve continuous and robust foliar disease observation in real field conditions. We propose a novel and compact algorithm, composed of two frameworks and a post-processing. The algorithm has continuous and highly discriminative capabilities for observing the process of disease in a single leaf from plant-level time sequence images. The first framework is based on robust template matching by orientation code matching (OCM), which implements successive tracking of a single leaf from a beet plant against severe illumination changes and non-rigid plant movements. The second framework uses a pattern recognition method of support vector machine (SVM) for achieving further disease classification from clutter field background. Prior to SVM, we propose a three feature combination of L∗, a∗, Entropy×Density, which has strong discrimination power to classify CLS disease from the clutter scene containing sandy soil, leaves, leaf stalks, and specular reflection. Additionally, post-processing is introduced to filter false positive noise to enhance the precision of the classification. Field experiment results demonstrate the feasibility and applicability of the proposed algorithm for disease monitoring under real field conditions. Meanwhile, comparative results with other conventional matching methods and feature combinations show the effectiveness of our proposed algorithm in both foliage tracking and disease classification.