Influence of solar irradiance on hyperspectral imaging-based plant recognition for autonomous weed control

Canopy reflectance in the visible and near infrared region (384–810 nm) was examined to discriminate between plant species, grown under various sunlight intensities, using ground-based hyperspectral imaging technology. Black nightshade and pigweed were grown with processing tomatoes under two levels of solar irradiance. The canonical Bayesian classifiers based on the full spectral range (400–795 nm) achieved an overall classification accuracy of 88.2% for the low solar irradiance treatment and 95.1% for the high solar irradiance treatment, using internal cross-validation analysis. The plant species exposed to higher solar irradiance were more easily distinguished in the feature space. The classifier trained with the plants grown in the low solar irradiance treatment was more robust to varying sunlight conditions; and correctly identified 88.7% of the plants for both sunlight intensity growing conditions. Global calibration achieved an optimum classification rate of 90% over the studied range of solar irradiance using 270 plants in the global domain as the training samples. It provided an alternative method to mitigate the effect on species discrimination and to improve classification robustness due to changing canopy optical properties across variation in solar irradiance during plant establishment.