Early diagnosis of greenhouse cucumber downy mildew in seedling stage using chlorophyll fluorescence imaging technology

Cucumber downy mildew, caused by Pseudoperonospora cubensis, typically remains hidden from view during its initial infection stage. To address this issue, this study proposed an early diagnosis model for greenhouse cucumber downy mildew. This study was conducted under controlled conditions, utilising a large-scale mobile chlorophyll fluorescence imaging system to monitor the samples during their seedling stage on a daily basis from the first day of inoculation. A total of 98 sets of fluorescence parameter values and corresponding fluorescence images were collected. Machine learning methods, such as recursive feature elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) regression with L1 regularisation, were used to screen the chlorophyll fluorescence parameter Ft_D3, whose corresponding the chlorophyll fluorescence images were used as inputs to the proposed model, following by employing a convolutional neural network (CNN) transfer learning method to the early detection task of cucumber downy mildew in fluorescence images. The study improved the topology structure of ResNet50, the network model with a learning rate of 0.001 and 16 cycles as the optimal feature extractor. The results indicated that the enhanced network displayed improved performance in early detection of cucumber downy mildew compared with other CNNs. Infected leaves were distinguished from healthy leaves in the early stages of infection, specifically 3 days before the appearance of symptoms. The accuracy of the model in the task of early diagnosis of downy mildew was 94.76%. This study presents an efficient approach for the photosynthetic characterisation and early identification of cucumber downy mildew.