A New Approach to Statistical Process Control: Identification of Outliers in Yield Maps

Danilo Tedesco De Oliveira,Leonardo Bernache,Murilo Aparecido Voltarelli,Luan Pereira De Oliveira,Rouverson Pereira Da Silva

doi:10.5539/jas.v10n8p334

Abstract

The tools of precision agriculture are of utmost importance in the Brazilian agribusiness, enabling increases in yields and reducing production costs. The use of harvest monitoring systems makes it possible due the possibility to identify pontual problems in an area, however, it becomes necessary to be working properly so it does not acquire incorrect information. Therefore, the purpose with this study was to propose a new approach to identify discrepant points in harvesting maps using statistical process control, as well as to define the best multiple of the standard deviation to identificate these points. The work was conducted during the soybean harvesting at São Geronimo farm in an area of 38 hectares in the municipality of Candido Mota, located in the the state of São Paulo. For gathering information, it was used a Stara crop monitoring system (model Topper Maps) set to record information during harvest in each three second. The productivity data were used to generate an individual control chart to identify points that were out of control so they could be removed. Two standard deviation multiples, that presented an average productivity closer to the average real productivity of the area, were selected. The multiples of the deviations that came closest were the 2σ and 3σ. Two multiples of standard deviation presented an average yield closer to the average real yield of the area. Individual control charts can be used to set control limits and identify possible discrepancies. The multiple of standard deviation 3σ presented information with greater reliability.

Highlights

There are technologies that monitor point-to-point productivity at harvest, as the harvest monitors allied to sensors coupled to machines that collect information in large quantities at short intervals of time
There was an increase in the use of the PA, mainly with the use of harvest maps to contribute to the monitoring of productivity and yield of the crop
Statistical Process Control (SPC) has, as one of the main objectives, the elimination of variability or part of it (Hessler, Camargo, & Dorion, 2009). This variability can be identified through graphs called Control Charts, which serve to verify when a process is stable or unstable through points inside or outside the control limits

Summary

Introduction

There are technologies that monitor point-to-point productivity at harvest, as the harvest monitors allied to sensors coupled to machines that collect information in large quantities at short intervals of time. Not all the information collected demonstrates the real productivity of the field, and errors in the recording of information are common (Molin, Cremonini, Menegatti, & Gimenez, 2000). Some of these errors are eliminated by computer-generated mapping software. Statistical Process Control (SPC) has, as one of the main objectives, the elimination of variability or part of it (Hessler, Camargo, & Dorion, 2009) This variability can be identified through graphs called Control Charts, which serve to verify when a process is stable or unstable through points inside or outside the control limits. The objective with this study was to propose a new approach to identify discrepant points in harvest maps using statistical process control through individual values control charts, to define the best multiple of the standard deviation to perform the identification of these points

Mechanized Harvest With Harvest Monitor

Results and Discussion

Conclusions