Abstract
Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.
Highlights
Remote sensing (RS) is a potential source of data for site-specific crop monitoring, providing spatial and temporal information
Orbital images are commonly used in agriculture to identify spectral variations resulting from soil and crop characteristics at a large-scale, supporting diagnostics for agronomical crop parameters and helping farmers to make better management decisions
All fields of the study site had the same variety of sugarsugarcane, SP83-2847 (4th ratoon), planted with a row spacing of 1.5 m in Argisol [33], cane, SP83-2847 (4th ratoon), planted with a row spacing of 1.5 m in Argisol [33], and it and it was mechanically harvested in the late season
Summary
Remote sensing (RS) is a potential source of data for site-specific crop monitoring, providing spatial and temporal information. Over the years, orbital images were used to delimit management zones for annual crops [1], monitor within-field yield variability for many crops such as corn [2] and cotton [3], map vineyard variability [4], plan the wheat harvest [5], develop crop growth model [6], and map grasslands biomass [7,8], among others. The empirical models to predict agronomical parameters based on spectral information may have spatial and temporal restrictions for application across different fields and seasons [10,11,12,13]. Yield maps are essential to better understand the within-field variability, to delimit management zones, and improve site-specific management strategies [10,14,15]. Sugarcane yield maps are usually obtained from data collected directly by monitors on harvesters that present some limitations, such
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