Data processing within rows for sugarcane yield mapping

Abstract

The mapping of sugarcane yield is still not as widely available as it is for grain crops. Sugarcane harvesters cut and process the cane in a single or maximum of two rows, facilitating the monitoring of cane yield and its behavior on a small scale. This study tested a method for sugarcane yield data cleaning, investigating if the data recording frequency influences the characterization of yield variations in mapping high-resolution spatial data within a single row. Four data sets from yield monitors of single row harvesting were used. A cleaning process with global and anisotropic filtering in a single sugarcane row was applied. The local outlier cleaner compares the yield value of a point with its nearest neighbors within the same row. Even after the elimination of outliers, there is great variation in yield between the rows, and this variation is much smaller in a single row. A frequency of 2 Hz was required for identifying and characterizing small yield variations within the sugarcane rows whilst other frequencies tried (0.2 and 0.1 Hz) resulted in loss of information on yield variability within the row. The difference between the root mean square error (RMSE) of ordinary kriging (OK) and inverse distance weighting (IDW) techniques is large enough to suggest the use of an individual yield line. Individual yield lines saved information in the data generated by the yield monitor unlike IDW and OK interpolation methods which omitted information over short distances within the rows and compromised the quality of high-resolution maps.

High-Resolution Imagery Data to Assess the Spatial Variability of Sugarcane Fields/ Dados De Imagens De Alta Resolução Para Avaliação Da Variabilidade Espacial De Talhões De Cana-De-Açúcar

“…The frequency of data acquisition was 0.20 Hz, and the travel speed of the harvester was about 1.25 m s -1 . The original yield data were filtered to eliminate the outliers using the methodology described by Maldaner and Molin (2020), and the interpolation was performed using Vesper 1.6 software (MINASNY et al, 2006) considering the kriging method, which considers the spatial dependence between the points. The semivariogram model adopted was that with the lowest Root Mean Square Error (RMSE).…”

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

“…The original data were converted to sugarcane yield based on the weight distribution of the haulage and filtered, according to the methodology described by Maldaner and Molin [44]. The removal of a significant amount of points is expected due to the associated error on the yield data due to the sugarcane flow stabilization time and elevator time [44].…”

“…The original data were converted to sugarcane yield based on the weight distribution of the haulage and filtered, according to the methodology described by Maldaner and Molin [44]. The removal of a significant amount of points is expected due to the associated error on the yield data due to the sugarcane flow stabilization time and elevator time [44]. Although filtering yield data is a common practice in PA for any crop, issues related with high spatial density and data noise make the adequate filtering of yield monitor data, which is especially important for sugarcane, with a significant amount of raw data being possibly deleted as a result [44].…”

“…The removal of a significant amount of points is expected due to the associated error on the yield data due to the sugarcane flow stabilization time and elevator time [44]. Although filtering yield data is a common practice in PA for any crop, issues related with high spatial density and data noise make the adequate filtering of yield monitor data, which is especially important for sugarcane, with a significant amount of raw data being possibly deleted as a result [44]. The semivariogram and the interpolation of the data were carried out using Vesper 1.6 software [45] with the ordinary kriging method (spatial resolution of 10.0 m × 10.0 m).…”

“…The observed SD for the filtered yield data from the 2018/2019 and 2019/2020 crop seasons were 7.06 and 9.55 Mg ha −1 . Despite the exclusion of more than 50% of the original data, which was expected due to the local and global filtering [44], the number of points per hectare was 255, thus, meeting the requirement of at least 23 samples ha −1 according to the calculated grid (Table 5). Table 5 summarizes the best-adjusted variogram model used for interpolating yield data with an RMSE of 0.53 and 1.41 Mg ha −1 for 2018/2019 and 2019/2020 sugarcane growing seasons.…”