Study on the Identification of Mildew Disease of Cuttings at the Base of Mulberry Cuttings by Aeroponics Rapid Propagation Based on a BP Neural Network
Study on the Identification of Mildew Disease of Cuttings at the Base of Mulberry Cuttings by Aeroponics Rapid Propagation Based on a BP Neural Network
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DOI：
10.3390/agronomy13010106
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发表时间：
2023-01-01
期刊：
影响因子：
3.7
通讯作者：
Wang, Liang
中科院分区：
文献类型：
--
作者：
Guo, Yinan;Gao, Jianmin;Wang, Liang
在雾培快繁过程中准确检测插穗病害对提高插穗生根率和成活率至关重要。本文提出利用图像处理技术，以桑葚插条生长数据为数据集，采用BP神经网络对桑树快速繁殖过程中枝条根部的霉变进行识别，并提取纹理和颜色特征。设计了一种智能控制雾培系统，根据霉变率来控制整个快繁培养箱的环境温度和湿度，从而提高雾培快繁时间，以及生根率和成活率。为了区分所提取的特征，他们进行分类和识别使用构造的BP神经网络模型。结果表明，经过三轮训练后，神经网络的性能在验证集中均方误差最小，因此选择第三轮训练的模型作为最佳模型。模型的训练效果表明，BP神经网络模型具有良好的稳定性，能够准确地识别桑葚插穗根区病害。利用MATLAB对神经网络进行训练后，回归结果显示训练集拟合曲线的相关系数R为0.98，测试集拟合曲线的相关系数R为0.98，验证集拟合曲线的相关系数R为0.99，表明预测结果与实际结果吻合较好。研究结果表明，该研究方法在桑树雾培快繁过程中对桑葚插穗健康状况的鉴定方面表现优异，能够快速准确地鉴定出受霉变影响的桑葚插穗，准确率达到80%。本研究为雾培快繁工厂和智能化苗圃提供了技术参考。
Accurate detection of cutting diseases in the process of aeroponic rapid propagation is very important for improving the rooting rate and survival rate of cuttings. This paper proposes to use image processing, with a dataset of the growth of mulberry cuttings and a backward propagation (BP) neural network, to identify mildew on the roots of mulberry branches in the process of rapid propagation, before extracting texture and color features. An intelligent control aeroponics system was designed to control the ambient temperature and humidity of the entire rapid propagation incubator according to the mildew rate, thereby improving the rapid propagation time of aeroponics, as well as the rooting and survival rates. In order to distinguish the extracted features, they were classified and identified using a constructed BP neural network model. The results indicated that the performance of the neutral network showed the lowest mean square error in the validation set after three rounds of training; therefore, the model of the third round was chosen as the best model. Furthermore, the training effect of the model revealed that the BP neural network model had good stability and could accurately identify diseases in the root zone of mulberry cuttings. After using MATLAB for neural network training, the regression results revealed correlation coefficients R of 0.98 for the fitting curve of the training dataset, 0.98 for the fitting curve of the test set, and 0.99 for the fitting curve of the validation set, indicating that the prediction results aligned well with the actual results. It can be concluded that research method described in this paper had excellent performance in identifying the health status of mulberry cuttings during the aeroponics rapid propagation process, and it was able to quickly and accurately identify mulberry cuttings affected by mildew disease with an accuracy rate of 80%. This research provides a technical reference for aeroponics rapid propagation factories and intelligent nurseries.