Automatic Detection of Tulip Breaking Virus (TBV) Using a Deep Convolutional Neural Network

Abstract

Tulip crop production in the Netherlands suffers from severe economic losses caused by virus diseases such as the Tulip Breaking Virus (TBV). Infected plants which can spread the disease by aphids must be removed from the field as soon as possible. As the availability of human experts for visual inspection in the field is limited, there is an urgent need for a rapid, automated and objective method of screening. From 2009-2012, we developed an automatic machine-vision-based system, using classical machine-learning algorithms. In 2012, the experiment conducted a tulip field planted at production density of 100 and 125 plants per square meter, resulting in images with overlapping plants. Experiments based on multispectral images resulted in scores that approached results obtained by experienced crop experts. The method, however, needed to be tuned specifically for each of the data trails, and a NIR band was needed for background segmentation. Recent developments in artificial intelligence and specifically in the area of convolutional neural networks, allow the development of more generic solutions for the detection of TBV. In this study, a Faster R-CNN network is applied on part of the data from the 2012 experiment. The outcomes show that the results are almost the same compared to the previous method using only RGB data.