Early cherry fruit pathogen disease detection based on data mining prediction
| Autor: | Miloš Ilić, Siniša Ilić, Srdjan Jovic, Stefan Panic |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
Disease detection Computer science 02 engineering and technology Horticulture Machine learning computer.software_genre 01 natural sciences 0202 electrical engineering electronic engineering information engineering Complex problems Data processing biology business.industry Information technology Forestry biology.organism_classification Automation Computer Science Applications Variable (computer science) Information and Communications Technology 020201 artificial intelligence & image processing Artificial intelligence business Agronomy and Crop Science computer Monilinia laxa 010606 plant biology & botany |
| Zdroj: | Computers and Electronics in Agriculture. 150:418-425 |
| ISSN: | 0168-1699 |
| Popis: | Today’s world depends largely on information and communication technologies. These technologies are in use in different areas of human life and work. Each day, more examples of possible application of information and communications technology are being discovered. In most cases, computer science is used to solve complex problems which have mathematical background. The most important and challenging job in agriculture is plant protection. This is due to its complexity and the lack of specialized tools that could predict when the conditions for specific infections are fulfilled. In this paper authors use different mathematics-based techniques for data processing and prediction of possible fruit disease infection. Six significant weather variables and one variable representing the month in the year are selected as predictor variables. Implemented techniques are compared with each other in order to select the best. Prediction includes two most important diseases of cherry fruit: monilinia laxa and coccomyces hiemalis. Data sets used in this research include data of eight year time period, collected over the region of Toplica in Serbia. The best achieved prediction accuracy is 95.8%. Additionally, the same implemented methods can be applied on other fruit species and other diseases for which data are known. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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