AGRICULTURE TEST CLASSIFICATION METHOD BASED ON DYNAMIC FUSION OF MULTIPLE FEATURES

Abstract

Agriculture plays a vital role in ensuring food security and sustainable development. Accurate and efficient classification of agricultural test data, such as soil quality, crop health, and nutrient content, is essential for precision farming. Traditional classification methods often rely on single-feature analysis, which limits accuracy due to variations in environmental conditions and data complexity. To overcome this limitation, this project proposes an Agriculture Test Classification Method based on Dynamic Fusion of Multiple Features. The system integrates multiple data features—such as soil moisture, temperature, texture, crop image features, and nutrient parameters—using a dynamic fusion algorithm that adapts the feature weights according to the data context. Machine learning and deep learning models, such as Random Forest and Convolutional Neural Networks (CNN), are employed to enhance classification accuracy. The fusion mechanism dynamically adjusts based on feature importance and correlation, resulting in a more robust and generalizable classification system. This approach supports intelligent decision-making in agriculture, promoting higher productivity and resource optimization.