Time Series Analysis of Sugarcane Plant Height and Planting Area Identification Based on DPRVI and RF Algorithm

Abstract

With the modernisation of agriculture, the time series analysis of sugarcane growth and the identification of planting areas play an important role in the investigation and detection of agriculture. The study conducted a time series analysis of sugarcane plant height and identification of planting areas based on dual polarisation radar vegetation index and random forest algorithm. Firstly, dual-polarisation radar vegetation index data of sugarcane were obtained using Sentienl-1A radar remote sensing data for time-series analysis of sugarcane plant height. Then the random forest model was applied to the regression and classification of Sentinel-2A remote sensing images for sugarcane plantation area identification. According to the findings, sugarcane field 4 had an R2 value of 0.835 in the quadratic regression model inversion, and the average absolute difference between the model’s predicted plant height and the actual value was only 7.3%. It shows that the dual-polarization radar vegetation index has great reliability and is capable of accurately predicting sugarcane plant height during the pre-growth stage. The user and producer accuracies are as high as 0.911 and 0.989, respectively, and the overall accuracies and Kappa coefficients are as high as 0.976 and 0.932, respectively, indicating that the Random Forest with multi-temporal phase and multi-feature is able to effectively identify sugarcane planting areas. The accuracy under multi-temporal phase and multi-feature is better than the single-temporal phase. It offers a trustworthy way for developing contemporary agricultural policy and development planning.