# Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
## 1 Introduction  
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. For example, 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. Some of the main limitations related to orbital images are the lack of ground truth data (calibration) and the measurement accuracy of the agronomical variables [9]. Furthermore, 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]. For the sugarcane crop, the assessment of the spatial variability is challenging due to the limited adapted solutions, mainly for yield mapping. 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, suchas the required calibration, accessibility of data processing for farmers or users, and a lack of knowledge to manage data from more than one harvester [16]. One additional challenge for sugarcane yield mapping based on yield monitors compared to grains is the high-resolution data (due to slow traveling speed of the harvester and narrow row spacing) combined with high biomass variability and noise from the yield monitor system. These limitations have guided the interest on RS techniques to monitor the sugarcane yield. Several studies reported the strength of linear correlation between the image-based vegetation indices (VIs) and sugarcane yield forecast [17; 18; 19], but none of these were applied using yield data generated by harvesters at the field level. No technique is widely adopted yet for generating yield maps in sugarcane from imagery information and the current practice to relate biomass estimated by VIs and stalk yield is not consistent regarding spatial and temporal resolutions. Other strategies for sugarcane yield estimation have relied on mathematical models for regional scales that consider the meteorological and crop management variables at a low spatial resolution [20], which is insufficient for precision agriculture (PA) purposes. Sugarcane crop has an important economic role for Brazilian agribusiness with about 9 M ha, especially for the Sao Paulo state that corresponds to 53.70% of national sugarcane production (Agricultural Economics Institute--IEA, 2019), where this study was developed. One of the initial research studies regarding spectral patterns of reflectance for sugarcane was conducted by [PERSON] et al. [21] that found strong correlations between the red spectral band (648 nm) and production parameters (leaf area index--LAI and total sugarcane biomass) until the end of the vegetative stage. [PERSON] et al. [22] listed different applications of RS techniques for sugarcane, such as disease detection, crop health status, and nutrition scouting by identifying patterns in spectral data throughout its phenological stages and orbital images. [PERSON] et al. [23] found a relationship between the (Normalized Difference Vegetation Index) NDVI and the concentration of leaf-tissue nutrients when monitoring sugarcane yield, and they observed changes in light canopy reflectance according to the concentration of leaf-tissue nutrients.