In computer vision research, the construction of image datasets is a critical process, given the need for robust experimentation frameworks that ensure the quality and validity of the resulting conclusions and performance measurements in each particular study. Therefore, experimental datasets must optimize their statistical, visual and computational properties through an adequate selection of representative and useful visual data, according to the specific research question being addressed. This paper proposes a dataset construction protocol for ad hoc acquired images in a particular Machine Learning application: tomato crop health assessment.