PhytoPatholoBot: Autonomous Ground Robot for Near-Real-Time Disease Scouting in the Vineyard

Abstract

The grape and wine industry suffers substantial losses annually due to diseases like downy mildew and grapevine leafroll-associated virus 3. Effective control of these diseases hinges on precise and timely diagnosis, which is often hindered by the shortage of highly skilled disease scouts. This highlights the urgent need for alternative, scalable solutions. We introduce PhytoPatholoBot (PPB), a fully autonomous ground robot equipped with a custom imaging system and onboard analysis pipeline for near-real-time disease detection and severity quantification, enabling rapid disease assessments in vineyards. The imaging system uses active illumination to enhance image quality and consistency, addressing a key challenge in ensuring the generalizability of analysis models. The analysis pipeline incorporates a disease mapping near-real-time model, a custom segmentation model designed for deployment on low-power edge computing devices, allowing near-real-time inference. PPB was deployed in both research and commercial vineyards for field-based disease scouting. Experimental results demonstrated that its disease detection and severity quantification performance was comparable to those of experienced human scouts and advanced offline computer vision models, while maintaining high computational efficiency and low-power consumption suited to field robots. PPB's ability to map disease progression over the growing season and manage multiple disease types in previously unseen vineyards highlights its potential to advance agricultural research and improve vineyard disease management practices.