In this work, we have combined fluorescence imaging spectroscopy (FIS) and supervised learning methods to identify and discriminate between citrus canker, Huanglongbing, and other leaf symptoms. Our goal is to differentiate these diseases and nutrient conditions without prior eye assessment of symptoms. Five supervised learning methods were evaluated. Our results show that by combining FIS with a convolutional neural network (AlexNet), it is possible to identify the disease of a sample with up to 95% accuracy. An enormous gain of time and a substantial cost reduction were achieved by this approach compared to polymerase chain reaction-based methods.