Knowledge-based interpretation of outdoor natural color scenes
Knowledge-based interpretation of outdoor natural color scenes
Practical Handbook on Image Processing for Scientific Applications
Practical Handbook on Image Processing for Scientific Applications
Computers and Electronics in Agriculture
New method to assess barley nitrogen nutrition status based on image colour analysis
Computers and Electronics in Agriculture
Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach
Computers and Electronics in Agriculture
Segmentation and classification of tobacco seedling diseases
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Automatic citrus canker detection from leaf images captured in field
Pattern Recognition Letters
Automatic recognition of quarantine citrus diseases
Expert Systems with Applications: An International Journal
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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.