Non-Invasive Diagnosis of Nitrogen and Phosphorus in Hydrangea macrophylla at Seedling Stage Using RGB Images

Rapid and accurate diagnosis of nitrogen (N) and phosphorus (P) is crucial for Hydrangea macrophylla nursery management. Traditional methods are time-consuming, and existing non-destructive studies rarely target ornamental plants or support joint N-P diagnosis at the early growth stage. A total of 339 RGB images were captured from potted hydrangeas grown under varying N and P levels at the seedling stage, with 65 phenotypic traits (color, texture, and morphology) extracted. Nutritional status (deficient, optimal, and surplus) was categorized with reference to plant nutrition indices. Discriminant models were then developed using four machine learning algorithms: convolutional neural network (CNN), support vector machine (SVM), random forest (RF), and probabilistic neural network (PNN). The model performances were evaluated using overall accuracy, precision, recall, F1-score, and Cohen’s Kappa coefficient (κ). As a result, CNN achieved 82.65% accuracy (κ = 0.7392) for N classification, and SVM reached 83.65% accuracy (κ = 0.7357) for P classification. Color-related traits dominated the top five contributing features, indicating a stronger correlation with N and P status. This work offers a practical solution for real-time, low-cost, and non-destructive nutrient diagnosis, supporting precision fertilization and enhancing environmental sustainability in nursery production.