Integration of machine learning and deep learning methods for sunflower yield prediction

Practical experience in yield forecasting demonstrates that it is a complex multifactorial task requiring precise and reliable methods for resolution. The use of machine learning and deep learning is crucial for achieving better results in digital agronomy. This paper is dedicated to the development of an intelligent system for sunflower yield forecasting. Based on the analysis of scientific publications and practical experience, the main issues of data processing are summarized, and schemes for their resolution are proposed. The main stages of the work included the study of the current state of digital agronomy, the selection of an approach, the development of a data processing method, the software implementation of the model, and testing. The key challenges were the limited data sets and the complexity of choosing the optimal approach to avoid overfitting. Combining different analysis methods allowed the creation of a powerful system that surpasses traditional approaches, though it requires more data for training. The study's conclusions show that machine learning and deep learning methods, such as LightGBM and U-Net, along with the proposed data processing methods, achieve high accuracy in forecasting. The model demonstrated the ability to generalize knowledge to new fields and to build detailed yield maps. Further research includes the development of a method for generating combinations of plant care options, the adaptation of computer vision methods with optimized algorithms to reduce computational complexity, and the expansion of the system's functionality to include cybersecurity aspects. The proposed system significantly enhances the efficiency of sunflower yield forecasting, contributing to the development of digital agronomy.