Analysis of Leaf cover on Raspberry Fruits Based on Hyperspectral Techniques Combined with Machine Learning Models

The aim of this study is to explore the potential application of hyperspectral technology in detecting the problem of fruit cover in the orchard. Three types of hyperspectral data were collected using a hyperspectral instrument to cover raspberry fruits with leaves. Machine learning models were used to classify and regress covered and uncovered fruits. The results show that hyperspectral technology can effectively differentiate fruits under different cover conditions, with spectral intensity data performing better in addressing cover issues. Random forest (RF) and multilayer perceptron (MLP) models demonstrated high accuracy in classification analysis, with MLP achieving a ROC AUC value of 0.99 on full-band data. Regression analysis also revealed a significant correlation between degree of coverage and spectral features, highlighting in particular the high explanatory power of light intensity data in predicting degree of coverage. This study not only confirms the application value of hyperspectral technology in precision agriculture, but also provides new technical support for intelligent orchard management and automated harvesting. Future research will focus on improving the generalisation ability of the models, integrating multi-source data to further improve the accuracy of coverage detection, and exploring the development of real-time monitoring and automatic control systems to achieve comprehensive intelligence in orchard management.