Decision support systems (DSS) are emerging technologies in agriculture aiding farmers in adopting more sustainable and precise field protection measures. In particular, two parasites, Diabrotica virgifera and Ostrinia nubilalis, are significantly impacting the quality of corn products. However, the efficacy of these treatments relies on their application during specific phenological stages of the insects. Furthermore, these pests are commonly controlled using chemical products, known to have detrimental effects on the environment due to co-toxicity with bees. Therefore, the development of mathematical models plays a crucial role in predicting different stages of insect growth, optimising treatment efficiency. In this study, we present two mathematical models aimed at improving effectiveness and reducing chemical treatments. The models achieve this by predicting the cumulative emergence curve of adult insects and the prediction of egg deposition timing, enhancing effectiveness by reducing ovicidal treatments. The average prediction error for insect phenological state emergence is found to be 1.7 days for Diabrotica virgifera and 4.5 days for Ostrinia nubilalis. Furthermore, the proposed models are sufficiently flexible to be adapted to different agronomic practices and can accommodate different climatic conditions that can influence pathogen development. Finally, the algorithms have been calibrated in the North of Italy and have demonstrated increased efficacy in larvicide coverage, reduced reliance on chemical treatments, and simplified field management, leading to significant time savings.