Identification of commercial blocks of outstanding performance of sugarcane using data mining
| Autor: | Luiz Henrique Antunes Rodrigues, Paulo Rodrigues Peloia |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0106 biological sciences
Engineering business.industry Agriculture (General) Decision tree Context (language use) 04 agricultural and veterinary sciences Agricultural engineering 01 natural sciences Agricultural and Biological Sciences (miscellaneous) S1-972 Identification (information) Block (programming) Yield (wine) 040103 agronomy & agriculture Forensic engineering 0401 agriculture forestry and fisheries regression tree Precision agriculture yield variability business Cluster analysis Hectare 010606 plant biology & botany clustering |
| Zdroj: | Engenharia Agrícola, Vol 36, Iss 5, Pp 895-901 (2016) Engenharia Agrícola v.36 n.5 2016 Engenharia Agrícola Associação Brasileira de Engenharia Agrícola (SBEA) instacron:SBEA Engenharia Agrícola, Volume: 36, Issue: 5, Pages: 895-901, Published: OCT 2016 |
| ISSN: | 0100-6916 |
| Popis: | In order to achieve more efficient agricultural production systems, studies relating to the patterns of influence factors on commercial blocks of outstanding performance can be performed to assist management practices. The performance is considered to be the difference between the yield of a given block and the average yield of the homogeneous group that it belongs to. The methods available to identify these outstanding blocks are usually subjective. The aim of this study was to propose an objective and repeatable approach to identify outstanding performance blocks. The proposed approach consisted of performance determination, using regression trees, and the classification of these blocks by k-means clustering. This approach was illustrated using a sugarcane model. The main factors influencing the tonnes of cane per hectare (TCH) and total recoverable sugar (TRS) yields were found to be crop age and water availability during ripening, respectively. These were used to create potential yield groups, and blocks with high and low performance were identified. The proposed approach was found to be valid in the identification of outstanding sugarcane blocks, and it can be applied to different crops or in the context of precision agriculture. |
| Databáze: | OpenAIRE |
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