Methodology for identification, characterization and removal of errors on yield maps.

Author(s)

Leonardo Afonso Angeli Menegatti

• Document type: Master's Dissertation
• Press: Piracicaba. , gráficos, ilustrações, tabelas.
• Institution: Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz (ESALA/BC)
• Defense date: 2002-04-30

Examining board members

Jose Paulo Molin; Marcos Milan; Antonio Mauro Saraiva

• Advisor: Jose Paulo Molin
• Field of knowledge: Agronomical Sciences - Agricultural Engineering
• Indexed in: Banco de Dados Bibliográficos da USP-DEDALUS; Biblioteca Digital de Teses e Dissertações - USP
• Location: Universidade de São Paulo. Biblioteca Central da Escola Superior de Agricultura Luiz de Queiroz; ESALQ-BC t631.3; M541m 79476

Abstract

Precision farming is a set of technologies that provide the capacity to manage the yield based on spacialized information. Yield map is an important information as it describes the answer of the crop to the inputs and soil conditions. There are several yield monitors in the market to collect yield information automatically. Due to the automation of the collection, some errors may be collected together with good data, and the elimination of those errors from the data set represents information quality. This work proposes the development a filtering routine of raw data to eliminate errors. The first step is the analysis and characterization of the errors present on data from six different commercial yield monitors. The errors found are positioning errors, represented by points outside the field. Small positioning errors cannot be detected by this methodology. It was found points with null or no yield, null or no grain moisture and points with the same position. Points with swath width different from the total swath width were considered as area measuring errors because they take account the interpretation of the operator about the actual swath width. For the characterization of the combine filling time it was developed a methodology to identify the running direction of the machine, allowing the estimation of the distance required to fill the threshing mechanism of the combine. The distance during the filling time was found to be between 0 and 44m. Yield outlier limits were established, and values over and under the limits were found in the data. Based on the characteristics of each error, a filtering routine was developed. The routine has seven steps, each one acting over different errors. At the first step, positioning errors are eliminated from the data set. The steps from 2 to 5 eliminate points with null or no yield, null or no grain moisture, points with swath width different from the total swath width and points with null distance. The sixth step acts over the filling time error, eliminating all points recorded during the space required for filling up the threshing mechanism of the combine. A search algorithm was developed to identify and eliminate these errors. The seventh step state outlier limits and remove from the data set the values outside of the limits. The filtering process improved the semivariance analysis and the final quality of yield maps. (AU)

• FAPESP's process: 00/00500-3 - Definition of protocols for analysis, interpretation and presentation of information in precision agriculture via maps
• Grantee: Leonardo Afonso Angeli Menegatti
• Support Opportunities: Scholarships in Brazil - Master