Early prediction of cassava mosaic disease onset based on remote sensing and climatic data

• Early CMD prediction successfully implemented in cassava plantations across Thailand. • Integrated weather data and satellite imagery enable early detection of CMD onset. • Relative humidity below 70.65% after the greenup stage significantly influences CMD. • CMD onset day can be predicted with about 69.2 % probability within a ± 15-day window. Cassava Mosaic Disease (CMD) is a severe viral infection affecting cassava crops, spread by whiteflies and infected plant material, causing significant yield losses globally. Traditional CMD detection methods are labor-intensive and slow. This study developed a data-driven predictive model using remote sensing and climatic data to forecast CMD onset in cassava crops. We applied a derivative approach to identify local minima at the cassava growing season’s onset, referred to as the Start of Season (SOS), by differentiating the Normalized Difference Vegetation Index (NDVI) time series data from Sentinel-2 L2A satellite imagery. When compared to conventionally estimated SOS, this method achieved a Root Mean Square Error (RMSE) of 3 days. Climatic data, aggregated over various temporal periods before and after the SOS, were used as predictors for CMD onset through a multistage predictive modeling approach. This approach incorporated ensemble variable selection using five methods: extreme gradient boosting, random forest, support vector machine, artificial neural network, and logistic regression to identify the most relevant climatic variables. Additionally, we employed a CART regression tree approach to estimate the most predictive climatic features associated with CMD onset. Findings indicated that relative humidity conditions of ≤ 70.65 % for at least four consecutive days within the first ten days after SOS were crucial for maximizing the accuracy of CMD onset date predictions, achieving an average ensemble prediction error of 17.7 days (measured by RMSE). The extreme gradient boosting algorithm outperformed the other models, achieving the highest accuracy in predicting CMD onset dates, with a success rate of 61.5 % and an RMSE of 8 days. Our findings indicate that integrating climatic factors, especially by establishing a timeframe based on the remote sensing-derived SOS date, is essential for identifying key CMD triggering factors and disease onset, applicable at both the farm scale and across broader satellite-covered regions.