Bagchi Parama, Sawicka Barbara, Stamenkovic Zoran, Marković Dušan, Bhattacharjee Debotosh
Department of CSE, RCC Institute of Information Technology, Beliaghata, Kolkata 700015, India.
Department of Plant Production Technology and Commodity Science, University of Life Sciences in Lublin, 20-950 Lublin, Poland.
Sensors (Basel). 2024 Dec 9;24(23):7864. doi: 10.3390/s24237864.
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.
虽然过去的研究强调了晚疫病感染检测和分类的重要性，但从经济角度来看，预测马铃薯晚疫病感染至关重要，因为这有助于显著降低生产成本。此外，有必要尽量减少马铃薯接触有害化学物质和农药的机会，因为它们可能对人体免疫系统产生不利影响。我们的工作基于对欧洲国家1980年至2000年实时数据的马铃薯晚疫病感染的精确分类。为了预测马铃薯晚疫病的爆发，我们纳入了几种混合机器学习模型，以及堆叠分类器和逻辑回归的独特组合，实现了87.22%的最高预测准确率。这些模型的进一步改进和新数据源的使用可能会提高晚疫病预测的准确性，从而提高马铃薯健康管理的效率。
