利用 CNN-LSTM 方法预测黄瓜霜霉病

Q2 Agricultural and Biological Sciences Agriculture Pub Date : 2024-07-16 DOI:10.3390/agriculture14071155
Yafei Wang, Tiezhu Li, Tianhua Chen, Xiaodong Zhang, Mohamed Farag Taha, Ning Yang, Hanping Mao, Qiang Shi
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引用次数: 0

摘要

开发早期预测技术对控制霜霉病和促进黄瓜生产具有重要意义。本研究通过融合病害定量信息和环境数据,提出了黄瓜霜霉病预测方法。首先,用便携式孢子捕捉器收集试验期间黄瓜霜霉病孢子数量,记录黄瓜霜霉病叶面积占黄瓜全部叶面积的比例,作为黄瓜植株的发病率。温室内的环境数据由温室内的气象站监测和记录。温室外的环境数据由温室前的气象站监测和记录。然后,根据皮尔逊相关系数法分析了黄瓜霜霉病的影响因素。确定了温室黄瓜霜霉病预警模型的影响因素。最后,利用 CNN-LSTM(卷积神经网络-长短期记忆)算法建立了黄瓜霜霉病发病率预测模型。结果表明,CNN-LSTM 网络模型的平均绝对误差(MAE)、平均平方误差(MSE)、均方根误差(RMSE)和判定系数(R2)分别为 0.069、0.0098、0.0991 和 0.9127。在所有测试集中,预测值与真实值之间的最大误差为 16.9398%。所有测试集的预测值与真实值之间的最小误差为 0.3413%。所有测试集的预测值与真实值之间的平均误差为 6.6478%。使用 Bland-Altman 方法分析了测试集的预测值和真实值,95.65% 的测试集数据在 95% 的一致性区间内。这项工作可作为建立温室作物气传病害早期预测模型的基础。
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Cucumber Downy Mildew Disease Prediction Using a CNN-LSTM Approach
It is of great significance to develop early prediction technology for controlling downy mildew and promoting cucumber production. In this study, a cucumber downy mildew prediction method was proposed by fusing quantitative disease information and environmental data. Firstly, the number of cucumber downy mildew spores during the experiment was collected by a portable spore catcher, and the proportion of cucumber downy mildew leaf area to all cucumber leaf area was recorded, which was used as the incidence degree of cucumber plants. The environmental data in the greenhouse were monitored and recorded by the weather station in the greenhouse. Environmental data outside the greenhouse were monitored and recorded by a weather station in front of the greenhouse. Then, the influencing factors of cucumber downy mildew were analyzed based on the Pearson correlation coefficient method. The influencing factors of the cucumber downy mildew early warning model in greenhouse were identified. Finally, the CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) algorithm was used to establish the cucumber downy mildew incidence prediction model. The results showed that the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and determination coefficient (R2) of the CNN-LSTM network model were 0.069, 0.0098, 0.0991, and 0.9127, respectively. The maximum error between the predicted value and the true value for all test sets was 16.9398%. The minimum error between the predicted value and the true value for all test sets was 0.3413%. The average error between the predicted and true values for all test sets was 6.6478%. The Bland–Altman method was used to analyze the predicted and true values of the test set, and 95.65% of the test set data numbers were within the 95% consistency interval. This work can serve as a foundation for the creation of early prediction models of greenhouse crop airborne diseases.
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来源期刊
Agriculture
Agriculture Agricultural and Biological Sciences-Horticulture
CiteScore
1.90
自引率
0.00%
发文量
4
审稿时长
11 weeks
期刊介绍: The Agriculture (Poľnohospodárstvo) is a peer-reviewed international journal that publishes mainly original research papers. The journal examines various aspects of research and is devoted to the publication of papers dealing with the following subjects: plant nutrition, protection, breeding, genetics and biotechnology, quality of plant products, grassland, mountain agriculture and environment, soil science and conservation, mechanization and economics of plant production and other spheres of plant science. Journal is published 4 times per year.
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