Identification of citrus disease using color texture features and discriminant analysis

The citrus industry is an important constituent of Florida's overall agricultural economy. Proper disease control measures must be undertaken in citrus groves to minimize losses. Technological strategies using machine vision and artificial intelligence are being investigated to achieve intelligent farming, including early detection of diseases in groves, selective fungicide application, etc. This research used the color co-occurrence method (CCM) to determine whether texture based hue, saturation, and intensity (HSI) color features in conjunction with statistical classification algorithms could be used to identify diseased and normal citrus leaves under laboratory conditions. Normal and diseased citrus leaf samples with greasy spot, melanose, and scab were evaluated. The leaf sample discriminant analysis using CCM textural features achieved classification accuracies of over 95% for all classes when using hue and saturation texture features. Data models that relied on intensity features suffered a reduction in classification accuracy when categorizing leaf fronts. due to the darker pigmentation of the leaf fronts. This reduction was not experienced on the leaf backs where the lighter pigmentation clearly revealed the disease discoloration. Although, high accuracies were achieved when using an unreduced dataset consisting of all HSI texture features, the overall best performer was determined to be a reduced data model that relied on hue and saturation features. This model was selected due to reduced computational load and the elimination of intensity features, which are not robust in the presence of ambient light variation. (c) 2006 Elsevier B.V. All rights reserved.