The support and development of the primary agri-food sector is receiving increasing attention. The complexity of modern farming issues has lead to the widespread penetration of Integrated Pest Management (IPM) Decision Support Systems (DSS). IPM DSSs are heavily dependent on numerous conditions of the agro-ecological environment used for cultivation. To test and validate IPM DSSs, permanent crops, such as olive cultivation, are very important, thus this work focuses on the pest that is most potentially harmful to the olive tree and fruit: the olive fruit fly. Existing research has indicated a strong dependency on both temperature and relative humidity of the olive fruit fly’s population dynamics but has not focused on the localised environmental/climate conditions (microclimates) related to the pest’s life-cycle. Accordingly, herein we utilise a collection of a wide-range of integrated sensory and manually tagged datasets of environmental, climate and pest information. We then propose an effective and efficient two-stage assignment of sensory records into clusters representing microclimates related to the pest’s life-cycle, based on statistical data analysis and neural networks. Extensive experimentation using the two methods was applied and the results were very promising for both parts of the proposed methodology. The identified microclimates in the experimentation were shown to be consistent with intuitive and real data collected in the field, while their qualitative evaluation also indicates the applicability of the proposed method to real-life uses.