Improving multi-crop area assessment through Bootstrapping: A focus on tomato fields

Abstract

Accurate crop mapping and automatic identification of different crops using remote sensing data and methods are vital to modern agriculture and provide farmers with valuable real-time information on crop type, health and yield in a geographic area leading to improved crop management practices and ultimately increasing efficiency, productivity, and profitability. This paper presents a multi-temporal classification method for automatic identification of different crops, especially tomato fields, using optical PlanetScope satellite images. Random Forest algorithm was used to create crop map of the study area and it was combined with the area adjustment algorithms to identify tomato area with a satisfied accuracy. The reliability of the test data used in accuracy assessments was determined using the Bootstrapping algorithm which estimates classification accuracy by creating multiple samples and assessing accuracy on each, yielding a mean accuracy and its variability. The resulting data was then used to adjust the tomato cultivation area, with a 95% confidence interval between 4493.20 ha and 5336.44 ha. The study showed the effectiveness of Random Forest classification for producing precise crop maps from satellite imagery and the utility of Bootstrapping algorithm for ensuring that the test data accurately represents the region, while highlighting the significance of area estimation adjustments and standard deviations for making well-informed decisions about satellite image-based crop maps.