Chlorophyll Fluorescence Imaging Combined with Active Oxygen Metabolism for Classification of Similar Diseases in Cucumber Plants

It is a great challenge to identify different cucumber diseases at early stages based on conventional methods due to complex and similar symptoms. By contrast, chlorophyll fluorescence is an early indicator of membrane changes or disturbances during plant growth. This research aimed to propose an effective method for the identification of brown spot (BS) and anthracnose (AN) in cucumbers based on chlorophyll fluorescence imaging, and to interpret the relationship between fluorescence response and different diseases coupled with active oxygen metabolism analysis. Support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost) were used to classify the different disease degrees of brown spot and anthracnose in cucumber plants. XGBoost is more effective for this study, with a classification accuracy greater than 90% for diseased cucumbers. Additionally, the XGBoost classification model was validated by the different disease degrees of cucumber plants, and the five-class classification accuracies were 88.2%, 85.0%, 75.0%, 65.0% and 75.0% for Healthy, BS-slight, BS-severe, AN-slight, and AN-severe, respectively. The diseased cucumbers had a higher level of reactive oxygen species (ROS) accumulation than the healthy cucumbers, and the activity levels of the ROS-scavenging enzymes of anthracnose were higher than those of brown spot. The analysis of fluorescence parameters and the discrimination model for different diseases were well linked to the active oxygen metabolism analysis. These results demonstrate the potential of chlorophyll fluorescence imaging combined with active oxygen metabolism analysis for the detection of cucumber diseases, regarding different disease types and disease degrees.