A stacking-based model for the spread of Botryosphaeria laricina

IF 3.4 2区 农林科学 Q1 FORESTRY
Hongwei Zhou, Shibo Zhang, Meng Xie, Xiaodong Li, Yifan Chen, Wenhao Dai
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引用次数: 0

Abstract

Botryosphaeria laricina (larch shoot blight) was first identified in 1973 in Jilin Province, China. The disease spread rapidly and caused considerable damage because its pathogenesis was unknown at the time and there were no effective controls or quarantine methods. At present, it shows a spreading trend, but most research can only conduct physiological analyses within a relatively short period, combining individual influencing factors. Nevertheless, methods such as neural network models, ensemble learning algorithms, and Markov models are used in pest and disease prediction and forecasting. However, there may be fitting issues or inherent limitations associated with these methods. This study obtained B. laricina data at the county level from 2003 to 2021. The dataset was augmented using the SMOTE algorithm, and then algorithms such as XGBoost were used to select the significant features from a combined set of 12 features. A new stacking fusion model has been proposed to predict the status of B. laricina. The model is based on random forest, gradient boosted decision tree, CatBoost and logistic regression algorithms. The accuracy, recall, specificity, precision, F1 value and AUC of the model reached 90.9%, 91.6%, 90.4%, 88.8%, 90.2% and 96.2%. The results provide evidence of the strong performance and stability of the model. B. laricina is mainly found in the northeast and this study indicates that it is spreading northwest. Reasonable means should be used promptly to prevent further damage and spread.

Abstract Image

基于堆叠的幼虫传播模型
落叶松枝枯病(Botryosphaeria laricina)于 1973 年首次在中国吉林省被发现。由于当时尚不清楚其发病机理,也没有有效的防治或检疫方法,因此该病传播迅速,造成了巨大损失。目前,该病呈蔓延趋势,但大多数研究只能在较短时间内结合个别影响因素进行生理学分析。不过,神经网络模型、集合学习算法和马尔可夫模型等方法已被用于病虫害预测和预报。然而,这些方法可能存在拟合问题或固有的局限性。本研究获得了 2003 年至 2021 年县级的幼虫数据。使用 SMOTE 算法对数据集进行增强,然后使用 XGBoost 等算法从 12 个特征的组合集中选择重要特征。研究人员提出了一种新的堆叠融合模型来预测 B. laricina 的状况。该模型基于随机森林、梯度提升决策树、CatBoost 和逻辑回归算法。该模型的准确率、召回率、特异性、精确度、F1 值和 AUC 分别达到了 90.9%、91.6%、90.4%、88.8%、90.2% 和 96.2%。这些结果证明了该模型的强大性能和稳定性。B. laricina 主要分布在东北地区,本研究表明它正在向西北地区扩散。应及时采取合理措施,防止其进一步危害和扩散。
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来源期刊
CiteScore
7.30
自引率
3.30%
发文量
2538
期刊介绍: The Journal of Forestry Research (JFR), founded in 1990, is a peer-reviewed quarterly journal in English. JFR has rapidly emerged as an international journal published by Northeast Forestry University and Ecological Society of China in collaboration with Springer Verlag. The journal publishes scientific articles related to forestry for a broad range of international scientists, forest managers and practitioners.The scope of the journal covers the following five thematic categories and 20 subjects: Basic Science of Forestry, Forest biometrics, Forest soils, Forest hydrology, Tree physiology, Forest biomass, carbon, and bioenergy, Forest biotechnology and molecular biology, Forest Ecology, Forest ecology, Forest ecological services, Restoration ecology, Forest adaptation to climate change, Wildlife ecology and management, Silviculture and Forest Management, Forest genetics and tree breeding, Silviculture, Forest RS, GIS, and modeling, Forest management, Forest Protection, Forest entomology and pathology, Forest fire, Forest resources conservation, Forest health monitoring and assessment, Wood Science and Technology, Wood Science and Technology.
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