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