Analysis and prediction of rubber tree phenological changes during Pestalotiopsis infection using Sentinel-2 imagery and random forest

Abstract

The fungus Pestalotiopsis sp. causes aberrant leaf fall, resulting in an annual reduction in rubber production. The loss of latex yield has a significant impact on the income of smallholder farmers. The objective of this study is to estimate NDVI using reflectance from the upper canopy based on Sentinel-2 NDVI time series data and daily climate data, analyze phenological changes of rubber trees using a harmonic model and time-lag cross-correlation, and develop a prediction model of vegetation indices using the random forest regressor (RFR) algorithm. The study was conducted on the rubber plantation of the Indonesian Rubber Research Institute in Sembawa, Indonesia. Three rubber clones aged 10 to 11 years were used to study the wintering trend. The study found that the overall trend of the NDVI decreased significantly from 2019 to 2022. The cycle of defoliation and refoliation changed due to disease and climate change. The time lag effects of vegetation index and climate variables are crucial for predicting vegetation dynamics. Increasing temperature and changing precipitation play significant roles in influencing pathogen incidence. The mean absolute percentage error (MAPE) was used to evaluate the trend of the vegetation indices. The RFR algorithm predicts the vegetation indices with a MAPE of 6.07%, 5.96%, and 8.18% of BPM24, GT1, and RRIC 100, respectively. Our finding indicated that the analysis and prediction model can be used to understand the phenological pattern and predict the wintering pattern to prevent disease spread and minimize latex yield loss due to infestation outbreaks.