Leaf disease image retrieval with object detection and deep metric learning

Abstract

Rapid identification of plant diseases is essential for effective mitigation and control of their influence on plants. For plant disease automatic identification, classification of plant leaf images based on deep learning algorithms is currently the most accurate and popular method. Existing methods rely on the collection of large amounts of image annotation data and cannot flexibly adjust recognition categories, whereas we develop a new image retrieval system for automated detection, localization, and identification of individual leaf disease in an open setting, namely, where newly added disease types can be identified without retraining. In this paper, we first optimize the YOLOv5 algorithm, enhancing recognition ability in small objects, which helps to extract leaf objects more accurately; secondly, integrating classification recognition with metric learning, jointly learning categorizing images and similarity measurements, thus, capitalizing on prediction ability of available image classification models; and finally, constructing an efficient and nimble image retrieval system to quickly determine leaf disease type. We demonstrate detailed experimental results on three publicly available leaf disease datasets and prove the effectiveness of our system. This work lays the groundwork for promoting disease surveillance of plants applicable to intelligent agriculture and to crop research such as nutrition diagnosis, health status surveillance, and more.