Enhanced Tobacco Yield Prediction Using Spatial Information and Exogenous Variable-driven Machine Learning Models
B. Samuel Naik
Banaras Hindu University (BHU), Varanasi, UttarPradesh, 221 005, India.
V C Karthik
ICAR-Indian Agricultural Research Institute, New Delhi-110 012, India.
Veershetty
ICAR-Indian Agricultural Research Institute, New Delhi-110 012, India.
B S Varshini
ICAR-Indian Agricultural Research Institute, New Delhi-110 012, India.
A S B Sujith
ICAR-Indian Agricultural Research Institute, New Delhi-110 012, India.
Halesha P
Banaras Hindu University (BHU), Varanasi, UttarPradesh, 221 005, India.
S Govinda Rao
Department of Statistics and Computer Applications, ANGRAU Agricultural College Naira, Srikakulam, Andhra Pradesh-522 101, India.
G. H. Harish Nayak *
University of Agriculture Sciences, Dharwad, Karnataka-580 005, India.
*Author to whom correspondence should be addressed.
Abstract
Remote sensing technology has been essential in studying the relationship between tobacco canopy spectral characteristics and biomass yield. This study has been conducted in Garnepudi, Andhra Pradesh, employed satellite imagery obtained between 2015 and 2023 to extract vegetation indices (VI’s). Accurately predicting yield is crucial for India's economy. This study investigates the efficacy of various predictive models for tobacco yield forecasting using multiple vegetation indices: Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI) and Leaf Surface Water Index (LSWI). The models assessed include traditional parametric approaches (ARIMAX, MLR), machine learning techniques (ANN, SVR, RFR), and advanced ensemble methods like XGBoost. The results highlight XGBoost as the most accurate model, consistently delivering the lowest error metrics, including RMSE and MAE, across all vegetation indices. Specifically, XGBoost achieved the best performance with LAI showing RMSE of 86.657, MAE of 58.324, sMAPE of 14.354, MASE of 1.001, and QL of 29.162 respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in tobacco yield prediction. This study highlights the significant role of remote sensing technology in capturing crop development patterns and accurately forecasting tobacco yield, thereby offering valuable insights for agricultural planning and decision-making. The study also addresses challenges such as data quality and model generalization, providing a comprehensive view of the research impact and future directions.
Keywords: Machine learning, vegetation indices, tobacco, yield prediction, XGBoost