PII-CNN-LSTM: A multi-modal deep learning framework integrating novel pollination importance index for predicting optimal apple pollination windows

Abstract

Pollination optimization in apple orchards faces increasing challenges from climate variability and declining pollinator populations, necessitating precision timing strategies. This study introduces a novel Pollination Importance Index (PII) integrated with a hybrid multi-task deep learning framework (PII-CNN-LSTM) to identify critical pollination windows. The PII dynamically quantifies pollination potential by incorporating flower receptivity, resource availability, biotic stress, and pollinator activity across five apple flower growth stages. The PII-CNN-LSTM architecture simultaneously performs growth stage classification and importance prediction through CNN spatial feature extraction and LSTM temporal modeling, enhanced by attention mechanisms and residual connections. Comparative evaluation against PII-CNN-BiLSTM, PII-CNN-GRU, and PII-CNN-TCN architectures demonstrated superior performance with 97% classification accuracy and minimal prediction error (validation loss: 0.0065, MAE: 0.0505). The model achieved exceptional full-bloom stage identification (99% F1-score), corresponding to its dominant 61.5% contribution to overall pollination importance. Cross-validation using 2024–2025 ground truth data and real-time drone deployment confirmed robust generalizability with temporal correlations exceeding 0.94. The framework successfully identified the critical pollination window from 3rd to 9th days, with optimal intervention timing at 5th to 7th days when importance scores exceeded 0.40. This biologically-grounded temporal precision enables targeted deployment of pollination resources during peak receptivity periods, reducing the need for continuous monitoring and intervention throughout the entire flowering season. The biologically-grounded approach provides scalable, data-driven decision support for precision agriculture, representing a significant advancement in agricultural automation and orchard productivity optimization. • Developed Pollination Importance Index (PII) integrating key pollination factors. • Identified optimal pollination window at days 5–7 with >0.94 temporal correlations. • PII-CNN-LSTM achieved 97% accuracy, outperforming BiLSTM, GRU, and TCN models. • Real-time drone deployment achieved 90% accuracy with YOLOv8s-PII-CNN-LSTM pipeline. • Six-channel fusion combining RGB imagery, PII score, image labels, and temporal sequences.