An Automated System for Plant-Level Disease Rating in Real Fields

Cercospora leaf spot (CLS) is the most serious disease in sugar beet plants that significantly reduces the sugar yield throughout the world. Therefore the current focus of the researchers in agricultural domain is to find sugar beet cultivars that are highly resistant to CLS. To measure their resistance, CLS is manually observed and rated in a large variety of sugar beet by different human experts over a period of a few months. Unfortunately, this procedure is laborious and subjective. Therefore, we propose a novel computer vision system, CLS Rater, to automatically and accurately rate CLS of plant images in the real field to the "USDA scale" of 0 to 10. Given a set of plant images captured by a tractor-mounted camera, CLS Rater extracts multi-scale super pixels, where in each scale a novel histogram of importances feature representation is proposed to encode both the within-super pixel local and across-super pixel global appearance variations. These features at different super pixel scales are then fused for learning a bagging M5P regress or that estimates the rating for each plant image. We test our system on the field data collected over a period of two months under different day lighting and weather conditions. Experimental results show CLS Rater to be highly consistent with a rating error of 0.65, which demonstrates higher consistency than the rating standard deviation of 1.31 by the human experts.