Knauer Uwe, Warnemünde Sebastian, Menz Patrick, Thielert Bonito, Klein Lauritz, Holstein Katharina, Runne Miriam, Jarausch Wolfgang
Department of Agriculture, Ecotrophology and Landscape Development, Anhalt University of Applied Sciences, 06406 Bernburg, Germany.
Cognitive Processes and Systems, Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, Germany.
Sensors (Basel). 2024 Dec 4;24(23):7774. doi: 10.3390/s24237774.
Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human observers consider multiple symptoms, but these are difficult to measure automatically in the field. Therefore, the potential of hyperspectral imaging in combination with data analysis by machine learning algorithms was investigated to detect the symptoms solely based on the spectral signature of collected leaf samples. In the growing seasons 2019 and 2020, a total of 1160 leaf samples were collected. Hyperspectral imaging with a dual camera setup in spectral bands from 400 nm to 2500 nm was accompanied with subsequent PCR analysis of the samples to provide reference data for the machine learning approaches. Data processing consists of preprocessing for segmentation of the leaf area, feature extraction, classification and subsequent analysis of relevance of spectral bands. The results show that imaging multiple leaves of a tree enhances detection results, that spectral indices are a robust means to detect the diseased trees, and that the potentials of the full spectral range can be exploited using machine learning approaches. Classification models like rRBF achieved an accuracy of 0.971 in a controlled environment with stratified data for a single variety. Combined models for multiple varieties from field test samples achieved classification accuracies of 0.731. Including spatial distribution of spectral data further improves the results to 0.751. Prediction of qPCR results by regression based on spectral data achieved RMSE of 14.491 phytoplasma per plant cell.
苹果增殖病是欧洲水果生产中最重要的病害之一。早期且可靠的检测能使果农做出适当反应并防止病害进一步传播。人工观察的传统表型分析方法考虑多种症状，但这些症状在田间难以自动测量。因此，研究了高光谱成像结合机器学习算法进行数据分析的潜力，以仅根据采集的叶片样本的光谱特征来检测症状。在2019年和2020年生长季，共采集了1160个叶片样本。使用双相机设置在400纳米至2500纳米光谱波段进行高光谱成像，并随后对样本进行PCR分析，以为机器学习方法提供参考数据。数据处理包括用于叶片区域分割的预处理、特征提取、分类以及随后对光谱波段相关性的分析。结果表明，对一棵树的多个叶片进行成像可提高检测结果，光谱指数是检测患病树木的可靠手段，并且使用机器学习方法可以利用整个光谱范围的潜力。像rRBF这样的分类模型在单一品种的分层数据的受控环境中准确率达到0.971。来自田间测试样本的多个品种的组合模型分类准确率达到0.731。纳入光谱数据的空间分布可将结果进一步提高到0.751。基于光谱数据通过回归预测qPCR结果，每株植物细胞的植原体均方根误差为14.491。
