Machine learning techniques for accessing the chlorophyll content and fluorescence in maize based on UAV multispectral sensor

Le résumé fourni par la source

Canopy chlorophyll content (CCC) and chlorophyll fluorescence (ChlF) are key indicators of crop physiological status and productivity. Advances in unmanned aerial vehicle (UAV)-based multispectral sensing, integrated with machine learning (ML), have opened new possibilities for precise and non-destructive monitoring of these traits. This study evaluated the performance of thirty vegetation indices (VIs) and six ML algorithms for estimating CCC and ChlF in maize during kharif season and rabi season at Tamil Nadu Agricultural University, Coimbatore, India. Regression analyses revealed that red-edge indices, such as RECI, NDRE, MTCI, and LCI, consistently outperformed greenness indices. Among them, RECI and NDRE achieved the highest R 2 values (0.893 for CCC and 0.829 for ChlF) and the lowest RMSE values (0.058 for CCC and 0.021 for ChlF) during kharif season. In contrast, during rabi season, MTCI for CCC and LCI for ChlF showed the best performance, with R 2 values of 0.783 and 0.883, and RMSE values of 0.081 and 0.018, respectively. Among machine learning algorithms, Partial Least Squares Regression (PLSR) achieved the highest predictive accuracy for CCC (R 2 = 0.891, RMSE = 0.063, MAE = 0.045) and ChlF (R 2 = 0.889, RMSE = 0.017, MAE = 0.013) during kharif season. However, Random Forest (RF) outperformed during rabi season, achieving the highest accuracy for CCC (R 2 = 0.623, RMSE = 0.083, MAE = 0.066) and ChlF (R 2 = 0.867, RMSE = 0.015, MAE = 0.011), respectively. In contrast, K-Nearest Neighbours (KNN) consistently underperformed in predicting both CCC and ChlF across kharif season and rabi season. Overall, this study demonstrated the potential of integrating UAV-based multispectral vegetation indices (VIs) with ML algorithms for accurate and scalable estimation of maize CCC and ChlF under varying seasonal conditions, highlighting their applicability for precision crop monitoring and sustainable agricultural management. However, further validation across different crop seasons and multiple locations remain limited.

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