Artificial neural networks to identify naturally existing disease severity status

Classification is a central endeavour in Biology. Heterogeneity of biological systems makes classification more challenging, but this is crucial for effective disease control and management. This study is a computational modelling attempt to classify a plant disease using visual symptoms to ease crop management programmes. Weligama coconut leaf wilt disease (WCLWD), a phytoplasma-borne coconut disease characterised by three foliar symptoms (flaccidity (bending of leaflets), yellowing and marginal necrosis) found in Sri Lanka, was used to demonstrate its applicability. Self-organising map (SOM) was optimised to discover naturally existing categories of WCLWD using foliar symptoms. Ward clustering of SOM identified three distinct disease categories. Results agreed with the nature of disease progression and are supported by K-means clustering. Conversion of SOM clusters to a parsimonious multi-layer perceptron (MLP) supported by a novel efficient network pruning algorithm regenerated identical results proving that precise models can be developed for WCLWD classification using these approaches. The MLP (100 %) outperformed counter propagation (CP) neural network (91 %) in generalisation ability indicating the validity of the MLP model. The study identified flaccidity as the most influential symptom followed by yellowing and necrosis. Comparison of our results with expert decision on disease severity classification revealed 73.45 % correspondence. In-depth investigation into the results from the two approaches using statistical methods revealed that when multiple symptoms are blended, expert decisions rely more on the intensely visible symptom and mainly on a single dimension, whereas the SOM/MLP classifier more accurately captures the average, variation and multi-dimensionality in data indicating that the model is more realistic and capable than the naked eye in detecting the disease.