On developing and enhancing plant-level disease rating systems in real fields

Abstract

Cercospora leaf spot (CLS) is one of the most serious diseases of sugar beet worldwide, and if uncontrolled, causes nearly complete defoliation and loss of revenue for beet growers. The beet sugar industry continuously seeks CLS-resistant sugar beet cultivars as one strategy to combat this disease. Normally human experts manually observe and rate the resistance of a large variety of sugar beet plants over a period of a few months. Unfortunately, this procedure is laborious and the labels vary from one expert to another resulting in disagreements on the level of resistance. Therefore, we propose a novel computer vision system, CLS Rater, to automatically and accurately rate plant images in the real field to the “USDA scale” of 0–10. Given a set of plant images captured by a tractor-mounted camera, CLS Rater extracts multi-scale superpixels, where in each scale a novel Histogram of Importances feature encodes both the within-superpixel local and across-superpixel global appearance variations. These features at different superpixel scales are then fused for learning a regressor that estimates the rating for each plant image. We further address the issue of the noisy labels by experts in the field, and propose a method to enhance the performance of the CLS Rater by automatically calibrating the experts ratings to ensure consistency. We test our system on the field data collected from two years over a two-month period for each year, under different lighting and weather conditions. Experimental results show that both the CLS Rater and the enhanced CLS Rater to be highly consistent with the rating errors of 0.65 and 0.59 respectively, which demonstrates a higher consistency than the rating standard deviation of 1.31 by human experts.