Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data
While past research has emphasized the importance of late blight infection detection and classification, anticipating the potato late blight infection is crucial from the economic point of view as it helps to significantly reduce the production cost. Furthermore, it is necessary to minimize the expo...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7864 |
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| author | Parama Bagchi Barbara Sawicka Zoran Stamenkovic Dušan Marković Debotosh Bhattacharjee |
| author_facet | Parama Bagchi Barbara Sawicka Zoran Stamenkovic Dušan Marković Debotosh Bhattacharjee |
| author_sort | Parama Bagchi |
| collection | DOAJ |
| description | While past research has emphasized the importance of late blight infection detection and classification, anticipating the potato late blight infection is crucial from the economic point of view as it helps to significantly reduce the production cost. Furthermore, it is necessary to minimize the exposure of potatoes to harmful chemicals and pesticides due to their potential adverse effects on the human immune system. Our work is based on the precise classification of late blight infections in potatoes in European countries using real-time data from 1980 to 2000. To predict the potato late blight outbreak, we incorporated several hybrid machine learning models, as well as a unique combination of stacking classifier and logistic regression, achieving the highest prediction accuracy of 87.22%. Further enhancements of these models and the use of new data sources may lead to a higher late blight prediction accuracy and, consequently, a higher efficiency in managing potatoes’ health. |
| format | Article |
| id | doaj-art-8999cccc59eb435899f2b8b2eb1d974f |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-8999cccc59eb435899f2b8b2eb1d974f2024-12-13T16:33:01ZengMDPI AGSensors1424-82202024-12-012423786410.3390/s24237864Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological DataParama Bagchi0Barbara Sawicka1Zoran Stamenkovic2Dušan Marković3Debotosh Bhattacharjee4Department of CSE, RCC Institute of Information Technology, Beliaghata, Kolkata 700015, IndiaDepartment of Plant Production Technology and Commodity Science, University of Life Sciences in Lublin, 20-950 Lublin, PolandInstitute of Computer Science, University of Potsdam, An der Bahn 2, 14476 Potsdam, GermanyFaculty of Agronomy in Čačak, University of Kragujevac, 32000 Čačak, SerbiaDepartment of CSE, Jadavpur University, Kolkata 700032, IndiaWhile past research has emphasized the importance of late blight infection detection and classification, anticipating the potato late blight infection is crucial from the economic point of view as it helps to significantly reduce the production cost. Furthermore, it is necessary to minimize the exposure of potatoes to harmful chemicals and pesticides due to their potential adverse effects on the human immune system. Our work is based on the precise classification of late blight infections in potatoes in European countries using real-time data from 1980 to 2000. To predict the potato late blight outbreak, we incorporated several hybrid machine learning models, as well as a unique combination of stacking classifier and logistic regression, achieving the highest prediction accuracy of 87.22%. Further enhancements of these models and the use of new data sources may lead to a higher late blight prediction accuracy and, consequently, a higher efficiency in managing potatoes’ health.https://www.mdpi.com/1424-8220/24/23/7864potato late blightmachine learningstacking classifierlogistic regressionprediction modelscrop health management |
| spellingShingle | Parama Bagchi Barbara Sawicka Zoran Stamenkovic Dušan Marković Debotosh Bhattacharjee Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data Sensors potato late blight machine learning stacking classifier logistic regression prediction models crop health management |
| title | Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data |
| title_full | Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data |
| title_fullStr | Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data |
| title_full_unstemmed | Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data |
| title_short | Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data |
| title_sort | potato late blight outbreak a study on advanced classification models based on meteorological data |
| topic | potato late blight machine learning stacking classifier logistic regression prediction models crop health management |
| url | https://www.mdpi.com/1424-8220/24/23/7864 |
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