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.

Plant-Disease Introduction
Plant-Disease Introduction

We release the part of our collected dataset that was used in our experiments. To obtain a copy of this dataset, please email Yousef Atoum at atoumyou[at]msu[dot]edu with the subject: "CLS dataset download".

Publications

  • On Developing and Enhancing Plant-Level Disease Rating Systems in Real Fields
    Yousef Atoum, Muhammad Jamal Afridi, Xiaoming Liu, J. Mitchell McGrath, Linda E. Hanson
    Pattern Recognition, , Dec. 2015
    Bibtex | PDF
  • @article{ on-developing-and-enhancing-plant-level-disease-rating-systems-in-real-fields,
      author = { Yousef Atoum and Muhammad Jamal Afridi and Xiaoming Liu and J. Mitchell McGrath and Linda E. Hanson },
      title = { On Developing and Enhancing Plant-Level Disease Rating Systems in Real Fields },
      booktitle = { Pattern Recognition },
      month = { December },
      year = { 2015 },
    }
  • An Automated System for Plant-level Disease Rating in Real Fields
    Muhammad Jamal Afridi, Xiaoming Liu, J. Mitchell McGrath
    Proc. 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden, Aug. 2014 (Oral, Acceptance rate 14%, Best Industry Related Paper Award runner-up)
    Bibtex | PDF
  • @inproceedings{ an-automated-system-for-plant-level-disease-rating-in-real-fields,
      author = { Muhammad Jamal Afridi and Xiaoming Liu and J. Mitchell McGrath },
      title = { An Automated System for Plant-level Disease Rating in Real Fields },
      booktitle = { Proc. 22nd International Conference on Pattern Recognition },
      address = { Stockholm, Sweden },
      month = { August },
      year = { 2014 },
    }