STATISTICAL AND NEURAL NETWORK CLASSIFIERS FOR CITRUS DISEASE DETECTION USING MACHINE VISION

Abstract

ABSTRACT. 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 a texture analysis method termed the color co-occurrence method (CCM) to determine whether classification algorithms could be used to identify diseased and normal citrus leaves. Normal and diseased citrus leaf samples with greasy spot, melanose, and scab were collected in the field and brought to the laboratory for the development of suitable segmentation and classification algorithms. Four feature models were created for classification analysis using varying subsets of a 39-variable texture feature set. The classification strategies used were based on a Mahalanobis minimum distance classifier, using the nearest neighbor principle, as well as neural network classifiers based on the back-propagation algorithm and radial basis functions. The leaf sample discriminant analysis using the Mahalanobis statistical classifier and the CCM textural analysis achieved classification accuracies of over 95 % for all classes (99 % mean accuracy) when using hue and saturation texture features. Likewise, a back-propagation neural network algorithm achieved accuracies of over 90 % for all classes (95 % mean accuracy) when using hue and saturation features. It was concluded that the Mahalanobis statistical classifier and the back-propagation neural network classifier performed equally well when using ten hue and saturation texture features selected through a stepwise variable reduction method. Future studies will seek to apply the developed algorithms in a natural citrus grove environment