Vine disease detection in UAV multispectral images with deep learning segmentation approach

TL;DR

This paper presents a deep learning segmentation method using UAV multispectral images, combining visible and infrared data for accurate vine disease detection, aiming to improve vineyard management and reduce chemical inputs.

Contribution

It introduces a novel image registration technique and a CNN-based segmentation approach that effectively classifies diseased vine areas using fused multispectral UAV images.

Findings

Over 92% detection accuracy at grapevine level

87% accuracy at leaf level

Effective fusion of visible and infrared images

Abstract

One of the major goals of tomorrow's agriculture is to increase agricultural productivity but above all the quality of production while significantly reducing the use of inputs. Meeting this goal is a real scientific and technological challenge. Smart farming is among the promising approaches that can lead to interesting solutions for vineyard management and reduce the environmental impact. Automatic vine disease detection can increase efficiency and flexibility in managing vineyard crops, while reducing the chemical inputs. This is needed today more than ever, as the use of pesticides is coming under increasing scrutiny and control. The goal is to map diseased areas in the vineyard for fast and precise treatment, thus guaranteeing the maintenance of a healthy state of the vine which is very important for yield management. To tackle this problem, a method is proposed here for vine disease detection using a deep learning segmentation approach on Unmanned Aerial Vehicle (UAV) images. The method is based on the combination of the visible and infrared images obtained from two different sensors. A new image registration method was developed to align visible and infrared images, enabling fusion of the information from the two sensors. A fully convolutional neural network approach uses this information to classify each pixel according to different instances, namely, shadow, ground, healthy and symptom. The proposed method achieved more than 92% of detection at grapevine-level and 87% at leaf level, showing promising perspectives for computer aided disease detection in vineyards.