Abstract

Traditional manpower-based approaches to apple disease control face growing challenges due to the increased frequency of anomalous weather events, attributable to climate change, and labor shortages caused by aging rural populations. Specifically, the prevalence of high-temperature and high-humidity environments has led to a surge in domestic apple pests and diseases, resulting in significant economic losses and a sharp decline in total apple production. This paper proposes an AI-based prediction model that utilizes agro-meteorological data and a specialized dataset, focused on the "15-day window before and after the first precipitation," for the proactive forecasting of three major apple diseases: Marssonina blotch, White rot, and Anthracnose. The proposed method consistently demonstrated superior performance across all target diseases in terms of prediction accuracy, sensitivity, and specificity. Notably, the model achieved a sensitivity of 0.9750 for Anthracnose and a specificity of 0.9355 for White rot, validating its potential for practical on-farm application. This study is expected to advance eco-friendly precision agriculture by enabling farmers to optimize pest control timing and reduce unnecessary pesticide usage through the proactive prediction of disease outbreaks based on meteorological variations.