High-resolution monitoring of individual plants is crucial for improving decision-making processes in precision agriculture, particularly when it comes to assessing development, nutrition, and health status. Deep Convolutional Neural Networks (DCNNs) have proven to be highly effective in classifying vegetation components from point cloud data based on geometric features. Combining radiometric information with geometric data can further improve classification accuracy. The fusion of LiDAR and spectral data has proved effectiveness for detailed plant discrimination. However, some challenges remain in fusing terrestrial LiDAR and multispectral data, with few studies focusing exclusively on ground-based sensor integration for plant-level classification. In this study, we propose using a Multi-layer Perceptron (MLP) architecture to classify terrestrial multispectral and LiDAR point cloud data collected around an apple tree. The model was trained and tested using datasets obtained via geometric alignment between multispectral images and LiDAR point clouds. Despite using a lightweight architecture with over 98% fewer parameters compared with architectures described in the literature, our approach achieved accuracies above 94%, comparable to state-of-the-art methods, and outperforming orbital fusion techniques. Among the spectral bands evaluated, the combination of image bands near 490 and 735 nm showed the best balance between accuracy and generalisation, consistently discriminating between leaf, wood, and fruit classes with over 90% of accuracy. These results demonstrate the potential of combining terrestrial data fusion with efficient MLP models for achieving precise plant-level classification in precision agriculture.