Vivi Nur Wijayaningrum, Vipkas Al Hadid Firdaus, Mamluatul Hani'ah, Noprianto, Agung Nugroho Pramudhita, Rokhimatul Wakhidah
Crop yields are strongly affected by weather factors including temperature, humidity, rainfall, and wind, which play critical roles in plant development, irrigation management, and harvest outcomes. Reliable weather forecasting is thus crucial for enabling data-driven horticultural decisions and minimizing production risks. Conventional prediction techniques often struggle with non-stationary and multivariate time-series data and typically rely on a single preprocessing or feature engineering method. This study proposes a comprehensive framework integrating multiscenario preprocessing, scaling, and data augmentation with Bidirectional Long Short-Term Memory (BiLSTM) modeling to predict daily weather parameters. Preprocessing strategies include interpolation, mean, median, and k-nearest neighbors (KNN) imputation, while scaling methods involve Min-Max and Standard normalization. Data augmentation techniques such as sliding windows, Gaussian noise injections, and seasonal encoding are applied to enhance temporal pattern learning and model generalization. The framework evaluates multiple combinations of imputation, scaling, and augmentation strategies to identify optimal pipelines for each weather parameter. Results show that the best scenario, which combines Median imputation, Min-Max scaling, and Seasonal augmentation with a window size of 7, achieves the lowest RMSE of 0.0833 for temperature and an R2 of 0.5151, indicating substantial variance explained. Wind speed also shows robust performance under similar preprocessing, whereas rainfall and relative humidity remain challenging to forecast due to high variability. The findings highlight the importance of systematic preprocessing and augmentation for improving predictive accuracy. This framework provides a robust and adaptive tool for supporting precision agriculture, enabling more informed management decisions amid varying weather conditions. © 2025 IEEE.
Politeknik Negeri Malang, Department of Information Technology, Malang, Indonesia