背景:灰疫病(GB)是茶叶的一种重要疾病,对产量和质量都构成严重威胁。在这项研究中,模拟了GB病病原分离株(DDZ-6)的叶片感染过程。正常叶子的高光谱图像,感染的叶子没有症状,收集轻度和中度症状的感染叶。结合卷积神经网络(CNN),长短期记忆(LSTM),和支持向量机(SVM)算法,GB疾病的早期检测模型,建立了抗性品种快速筛选模型。通过在现场条件下收集数据集,验证了该方法的通用性。
结果:可见的红光带显示出对GB疾病的明显反应,通过严格的筛选过程利用无信息变量消除(UVE)识别出三个敏感带,竞争性自适应重加权抽样(CARS),和连续投影算法(SPA)。693、727和766nm波段是GB的高度敏感指标。在理想条件下,CARS-LSTM模型在早期检测GB方面表现出色,达到92.6%的准确率。然而,在现场条件下,与CNN集成的693和727nm波段的组合提供了最有效的早期检测模型,达到87.8%的准确率。为了筛选抗GB的茶叶品种,SPA-LSTM模型非常出色,达到82.9%的准确率。
结论:本研究为具有检测功能的GB疾病仪器提供了核心算法,这对茶园GB病的早期预防具有重要意义。©2024化学工业学会。
BACKGROUND: Gray blight (GB) is a significant disease of tea leaves, posing a severe threat to both the yield and quality. In this study, the process of leaf infection by a pathogenic isolate of the GB disease (DDZ-6) was simulated.
Hyperspectral images of normal leaves, infected leaves without symptoms, and infected leaves with mild and moderate symptoms were collected. Combining convolution neural network (CNN), long short-term memory (LSTM), and support vector machine (SVM) algorithms, the early detection model of GB disease, and the rapid screening model of resistant varieties were established. The generality of this method was verified by collecting datasets under field conditions.
RESULTS: The visible red-light band demonstrated a pronounced responsiveness to GB disease, with three sensitive bands identified through rigorous screening processes utilizing uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the successive projections algorithm (SPA). The 693, 727, and 766 nm bands emerged as highly sensitive indicators of GB. Under ideal conditions, the CARS-LSTM model excelled in early detection of GB, achieving an accuracy of 92.6%. However, under field conditions, the combination of 693 and 727 nm bands integrated with a CNN provided the most effective early detection model, attaining an accuracy of 87.8%. For screening tea varieties resistant to GB, the SPA-LSTM model excelled, achieving an accuracy of 82.9%.
CONCLUSIONS: This study provides a core algorithm for a GB disease instrument with detection capabilities, which is of great importance for the early prevention of GB disease in tea plantations. © 2024 Society of Chemical Industry.