The time required for the identification of pathogens is an important determinant of infection-related mortality rates and disease spread for species of relevance in agriculture. Conventional identification methods require a processing time of at least one to twenty days. Therefore, inaccurate empirical treatments are often provided while awaiting further identification, such that most cases progress with further aggravation of symptoms, contamination of other animals or plants, generating economic loss from decreased yield, and increased mitigation costs. Thus, there is a need for innovative, non-destructive, and rapid analytical techniques that provide reagent-free, portable, reliable, and holistic approaches to detect diseases in real-time. Vibrational spectroscopy techniques have shown the capacity to provide relevant information for disease detection. In near infrared spectroscopy (NIRS), the absorbance from a sample is measured across several hundred wavelengths in the near infrared band (750-2500 nm) and is directly influenced by the number and type of chemical bonds present. In order to make NIRS an effective tool for field-based studies, a simplified procedure is needed such that NIRS can be used in minimally processed samples found in situ. Here, experiments involving the agricultural important bovine herpesvirus-1 (BoHV-1), bovine respiratory syncytial virus (BRSV), Mannheimia haemolytica (MH), Xanthomonas citri pv. malvacearum (Xcm) and Rhizoctonia solani (Rs) were carried out to determine if biological spectral signatures can be differentiated between samples from two classes (i.e., healthy vs. sick, control sample vs. test sample). The specific objectives were to (1) create a spectral library for each evaluated pathogen and disease, (2) identify and establish the characteristic NIR spectral signatures and trends by aquaphotomics and chemometrics-based MVA methods, (3) generate and evaluate models for discriminating representative spectra, (4) provide new biochemical information by the correlation of the results with pathogen development and disease states in living systems, and (5) support the groundwork for a portable, fast, non-destructive, and accurate diagnostic tool capable of reducing the existing time necessary for pathogen and disease detection.