Automatic Filtering of Sugarcane Yield Data
Sugarcane mechanized harvesting generates large volumes of data that are used to monitor harvesters’ functionalities. The dynamic interaction of the machine-onboard instrumentation–crop system introduces discrepant and noisy values into the data, requiring outlier detectors to support this complex a...
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MDPI AG
2024-12-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/6/4/275 |
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| author | Eudocio Rafael Otavio da Silva José Paulo Molin Marcelo Chan Fu Wei Ricardo Canal Filho |
| author_facet | Eudocio Rafael Otavio da Silva José Paulo Molin Marcelo Chan Fu Wei Ricardo Canal Filho |
| author_sort | Eudocio Rafael Otavio da Silva |
| collection | DOAJ |
| description | Sugarcane mechanized harvesting generates large volumes of data that are used to monitor harvesters’ functionalities. The dynamic interaction of the machine-onboard instrumentation–crop system introduces discrepant and noisy values into the data, requiring outlier detectors to support this complex and empirical decision. This study proposes an automatic filtering technique for sugarcane harvesting data to automate the process. A three-step automated filtering algorithm based on a sliding window was developed and further evaluated with four configurations of the maximum variation factor <i>f</i> and six SW sizes. The performance of the proposed method was assessed by using artificial outliers in the datasets with an outlier magnitude (OM) of ±0.01 to ±1.00. Three case studies with real crop data were presented to demonstrate the effectiveness of the proposed filter in detecting outliers of different magnitudes, compared to filtering by another method in the literature. In each dataset, the proposed filter detected nearly 100% of larger (OM = ±1.00 and ±0.80) and medium (OM = ±0.50) magnitudes’ outliers, and approximately 26% of smaller outliers (OM = ±0.10, ±0.05, and ±0.01). The proposed algorithm preserved wider ranges of data compared to the comparative method and presented equivalent results in the identification of regions with different productive potentials of sugarcane in the field. Therefore, the proposed method retained data that reflect sugarcane yield variability at the row level and it can be used in practical application scenarios to deal with large datasets obtained from sugarcane harvesters. |
| format | Article |
| id | doaj-art-a4577b7eb2eb4b239d2d1bea5ff0c84a |
| institution | OA Journals |
| issn | 2624-7402 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | AgriEngineering |
| spelling | doaj-art-a4577b7eb2eb4b239d2d1bea5ff0c84a2025-08-20T02:01:01ZengMDPI AGAgriEngineering2624-74022024-12-01644812483010.3390/agriengineering6040275Automatic Filtering of Sugarcane Yield DataEudocio Rafael Otavio da Silva0José Paulo Molin1Marcelo Chan Fu Wei2Ricardo Canal Filho3Laboratory of Precision Agriculture (LAP), Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba 13418-900, BrazilLaboratory of Precision Agriculture (LAP), Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba 13418-900, BrazilLaboratory of Precision Agriculture (LAP), Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba 13418-900, BrazilLaboratory of Precision Agriculture (LAP), Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba 13418-900, BrazilSugarcane mechanized harvesting generates large volumes of data that are used to monitor harvesters’ functionalities. The dynamic interaction of the machine-onboard instrumentation–crop system introduces discrepant and noisy values into the data, requiring outlier detectors to support this complex and empirical decision. This study proposes an automatic filtering technique for sugarcane harvesting data to automate the process. A three-step automated filtering algorithm based on a sliding window was developed and further evaluated with four configurations of the maximum variation factor <i>f</i> and six SW sizes. The performance of the proposed method was assessed by using artificial outliers in the datasets with an outlier magnitude (OM) of ±0.01 to ±1.00. Three case studies with real crop data were presented to demonstrate the effectiveness of the proposed filter in detecting outliers of different magnitudes, compared to filtering by another method in the literature. In each dataset, the proposed filter detected nearly 100% of larger (OM = ±1.00 and ±0.80) and medium (OM = ±0.50) magnitudes’ outliers, and approximately 26% of smaller outliers (OM = ±0.10, ±0.05, and ±0.01). The proposed algorithm preserved wider ranges of data compared to the comparative method and presented equivalent results in the identification of regions with different productive potentials of sugarcane in the field. Therefore, the proposed method retained data that reflect sugarcane yield variability at the row level and it can be used in practical application scenarios to deal with large datasets obtained from sugarcane harvesters.https://www.mdpi.com/2624-7402/6/4/275outliermachine learningprecision agriculturesliding window |
| spellingShingle | Eudocio Rafael Otavio da Silva José Paulo Molin Marcelo Chan Fu Wei Ricardo Canal Filho Automatic Filtering of Sugarcane Yield Data AgriEngineering outlier machine learning precision agriculture sliding window |
| title | Automatic Filtering of Sugarcane Yield Data |
| title_full | Automatic Filtering of Sugarcane Yield Data |
| title_fullStr | Automatic Filtering of Sugarcane Yield Data |
| title_full_unstemmed | Automatic Filtering of Sugarcane Yield Data |
| title_short | Automatic Filtering of Sugarcane Yield Data |
| title_sort | automatic filtering of sugarcane yield data |
| topic | outlier machine learning precision agriculture sliding window |
| url | https://www.mdpi.com/2624-7402/6/4/275 |
| work_keys_str_mv | AT eudociorafaelotaviodasilva automaticfilteringofsugarcaneyielddata AT josepaulomolin automaticfilteringofsugarcaneyielddata AT marcelochanfuwei automaticfilteringofsugarcaneyielddata AT ricardocanalfilho automaticfilteringofsugarcaneyielddata |