Detection of Huanglongbing in Florida using fluorescence imaging spectroscopy and machine-learning methods

In this study, we combine a fluorescence imaging technique and two machine-learning methods to discriminate Huanglongbing (HLB) disease from zinc-deficiency stress on samples from Florida, USA. Two classification methods, support vector machine (SVM) and artificial neural network (ANN), are used. Our classification results present high accuracy for both classification methods: 92.8% for SVM and 92.2% for ANN. The results from Florida are also compared to results from São Paulo State, Brazil. This comparison indicates that the present technique can be applied to discriminate HLB from zinc deficiency in both states.