A deep learning-based mobile decision support system for optimizing stress induced fruiting in Citrus reticulata

Purpose This study proposes a deep learning-based decision support system for stress induction management in tangerine cultivation. While current techniques depend heavily on subjective visual information, we offer an objective, data-driven mechanism to determine whether a branch is likely to respond favorably to stress induction. Design/methodology/approach Leaf images were used as input, as color reflects photosynthetic pigment composition linked to readiness for stress-induced flowering. A dataset of 516 images, acquired under controlled conditions, was used to train the model. The system predicts flowering success into three categories: low, moderate and high. Four convolutional neural networks architectures (VGG16, ResNet50, MobileNet and Inception) were evaluated using Adam and Adamax with 10-fold cross-validation. Beyond standard transfer learning, the modeling pipeline integrates task-specific fine-tuning and red-, green- and blue-based colorimetric interpretation. Performance analysis includes receiver operating characteristic and gradient-weighted class activation mapping, with metrics summarized using accuracy, precision, recall and F1-score. Model selection was based on class-wise robustness and model size. Findings We found that architectural choice strongly affects predictive performance. The inception provides the highest accuracy-efficiency ratio (accuracy = 0.90, F1-Score = 0.89, precision = 0.92, recall = 0.88 and model size = 57 Megabytes), beating larger models and being lightweight enough for mobile application. The trained models were implemented into a mobile application prototype for real-time inference. The low flowering-success category remains difficult to detect due to its high intra-class variability. Originality/value This decision support system provides a practical approach to use an artificial intelligence-driven flowering analysis straight in stress induction workflows for more accurate and efficient cultivation management.