Practical Evaluation of UAV Flight Altitude for Rice Growth-Stage Classification Using NDVI Images and Convolutional Neural Networks

The declining and aging agricultural workforce in Japan has increased the need for labor-saving technologies that can support crop management. In this study, we used an unmanned aerial vehicle (UAV) equipped with a multispectral sensor to acquire NDVI images of paddy rice fields and classified rice growth stages using a Convolutional Neural Network (CNN). For practical UAV-based monitoring, flight altitude is an important factor because it affects both image resolution and field coverage. Therefore, this study evaluated the effect of UAV flight altitude on CNN-based growth-stage classification using NDVI images acquired at altitudes of 30, 60, and 100 m above ground level. Two types of altitude images were evaluated: simulated-altitude images generated by down-sampling 30 m images and actual-altitude images acquired at each flight altitude. When the CNN was trained and tested using simulated-altitude images, the test accuracies were 85.1% at 60 m and 83.6% at 100 m. When the CNN was trained using actual-altitude images, the test accuracies were 86.5% at 60 m and 87.8% at 100 m. Actual-altitude images at 60 and 100 m were available for only 8 of the 25 observation dates. In addition, when the model trained only on simulated-altitude images was applied to actual-altitude images, the accuracy at 100 m decreased to 0.5%, indicating a strong domain shift between simulated and actual high-altitude imagery. These results suggest that higher-altitude UAV imagery can support rice growth-stage classification when training data include images acquired at the actual operational altitude, whereas down-sampled images should not be regarded as a complete substitute for actual high-altitude images. The results should be interpreted with caution because the dataset was imbalanced across growth stages, with only one observation date representing the heading stage, and because the actual-altitude dataset was limited to selected observation dates.