References
-
Li, H. E., Yan Jiang, J. & Mo, D. Monitoring of key Camellia Oleifera phenology features using field cameras and deep learning. Comput. Electron. Agric. 219, 6, 108–148 (2024).
-
Kumar, B. & Kumar, A. A Novel Adaptive Flower Pollination Algorithm for Maximum Power Tracking of Photovoltaic Systems Under Dynamic Shading Conditions. Iran. J. Sci. Technol. 1 (3), 1–17 (2024).
-
Rondinel-Mendoza, K. V. & Lorite, J. Tracking Phenological Changes over 183 Years in Endemic Species of a Mediterranean Mountain (Sierra Nevada, SE Spain) Using Herbarium Specimens. Plants 13 (2), 522–542 (2024).
-
Estrada Vasconez, J. S., Fu, L. & Cheein, F. A. Deep Learning based flower detection and counting in highly populated images: A peach grove case study. J. Agric. Food Res. 15, 1 (2024).
-
Selvanarayanan, R., Rajendran, S. & Alotaibi, Y. Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques. CMES-Computer Model. Eng. Sci. 1 (1), 1–23 (2024).
-
Houtman, W. & Siagkris, L. Automated flower counting from partial detections: Multiple hypothesis tracking with a connected-flower plant model. Comput. Electron. Agric. 1, 2, 106346–106366 (2024).
-
Zhang., C. & Valente, J. Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard. ISPRS J. Photogrammetry Remote Sens. 2 (2), 256–273 (2024).
-
Sorte, L. X., B & Ferraz, C. T. Coffee leaf disease recognition based on deep learning and texture attributes. Procedia Comput. Sci. 1 (3), 135–144 (2019).
-
Hicks, D., Baude, M., Kratz, C., Ouvrard, P. & Stone, G. Deep learning object detection to estimate the nectar sugar mass of flowering vegetation. Ecol. Solutions Evid. 2, 12099–12112 (2021).
-
Prado, S. G., Collazo, J. A. & Irwin, R. E. Resurgence of specialized shade coffee cultivation: effects on pollination services and quality of coffee production. Agric. Ecosyst. Environ. 3 (1), 567–575 (2018).
-
Darwin, B., Dharmaraj, P., Prince, S. & Popescu, D. E. Recognition of bloom/yield in crop images using deep learning models for smart agriculture: A review. Agronomy 4 (1), 646–675 (2021).
-
Meyers, L., Cordero, J. R. & Bravo, C. C. Towards Automatic Honey Bee Flower-Patch Assays with Paint Marking Re-Identification. Agriculture 11, 1 (2023).
-
Nagy, R., Muranyi, E. & Biron Molnar, P. Assessment of Bioactive Profile of Sorghum Brans under the Effect of Growing Conditions and Nitrogen Fertilization. Agriculture 4 (1), 760–772 (2023).
-
Chiron, G., Gomez-Kramer, P. & Menard, M. It is detecting and tracking honeybees in 3D at the beehive entrance using stereo vision. EURASIP J. Image Video Process. 1 (3), 1–17 (2013).
-
Jemaa, H., Bouachir, W. & Leblon, R. UAV-Based Computer Vision System for Orchard Apple Tree Detection and Health Assessment. Remote Sens. 14, 3558–3577 (2023).
-
Zhang, M., Zhao, J. & Hoshino, Y. Deep learning-based high-throughput detection of in vitro germination to assess pollen viability from microscopic images. J. Exp. Bot. 21 (1), 6551–6562 (2023).
-
Gao, C. et al. A Novel Multinozzle Targeting Pollination Robot for Clustered Kiwifruit Flowers Based on Air–Liquid Dual-Flow Spraying. J. Field Robot. 1:4, 1–26 (2025).
-
Shrestha, R., Rijal, M., Smith, T. & Gu, Y. FloPE: Flower Pose Estimation for Precision Pollination. arXiv preprint arXiv. 25, 03 (2025).
-
Smith, T. et al. Design of stickbug: a six-armed precision pollination robot. International Conference on Intelligent Robots and Systems, 69–75. (2024).
-
Bhattarai, U. et al. A vision-based robotic system for precision pollination of apples. Comput. Electron. Agric. 234, 110158 (2025).
-
Kong, C. et al. Towards Closing the Loop in Robotic Pollination for Indoor Farming via Autonomous Microscopic Inspection. arXiv preprint arXiv. 2409, 12311 (2024).
-
Singh, R., Seneviratne, L. & Hussain, I. Robust pollination for tomato farming using deep learning and visual servoing. Robotica, 1–24. (2024).
-
Duc Tai, N., Minh Trieu, N. & Truong Thinh, N. Modeling positions and orientations of cantaloupe flowers for automatic pollination. Agriculture 14 (5), 746 (2024).
-
Pinheiro, I. et al. Deep learning based approach for actinidia flower detection and gender assessment. Sci. Rep. 14 (1), 24452 (2024).
-
Shuo, W. & Jizhan, L. Research Progress on Efficient Pollination Technology of Crops. Agronomy 11, 2 (2022).
-
Ratnayake, M. N., Dyer, A. G. & Dorin, A. Tracking individual honeybees among wildflower clusters with computer vision-facilitated pollinator monitoring. PLoS ONE. 2 (4), 239–250 (2021).
-
Mu, X., He, L. & Heinemann, P. Mask R-CNN-based apple flower detection and king flower identification for precision pollination. Smart Agricultural Technol. 4 (2), 100–131 (2023).
-
Srinivas, C. et al. Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images. J. Healthc. Eng. 1 (1), 1–27 (2022).
-
Zhao, X. et al. An Estimation of the Leaf Nitrogen Content of Apple Tree Canopies Based on Multispectral Unmanned Aerial Vehicle Imagery and Machine Learning Methods. Agronomy 14 (3), 552–575 (2024).
-
Wang, C. et al. Assisting the Planning of Harvesting Plans for Large Strawberry Fields through Image-Processing Method Based on Deep Learning. Agriculture 14 (4), 560–593 (2024).
-
Chen, R. et al. A novel framework to assess apple leaf nitrogen content: Fusion of hyperspectral reflectance and phenology information through deep learning. Comput. Electron. Agric. 219, 108816 (2024).
-
Patil, N., Bhise, A. & Tiwari, R. K. Fusion deep learning with pre-postharvest quality management of grapes within the realm of supply chain management. Sci. Temper. 15 (1), 1764–1772 (2024).
-
Datt, R. M. & Kukreja, V. Neural Network Model for Predicting Apple Yield Based on Arrival of Phenological Stage in Conjunction with Leaf Disease, Soil and Weather Parameters. SN Comput. Sci. 5 (1), 141–162 (2024).
-
Mekhtiche, M. A. et al. Visual tracking in unknown environments using fuzzy logic and dead reckoning. Int. J. Adv. Rob. Syst. 13, 2, 53–79 (2016).
-
Selvanarayanan, R., Rajendran, S., Algburi, S., Ibrahim Khalaf, O. & Hamam, H. Empowering coffee farming using counterfactual recommendation based RNN driven IoT integrated soil quality command system. Sci. Rep. 14 (1), 6269–6280 (2024).
-
Malathi, P. et al. November. A Machine Learning Approach for Predictive Analysis of Jasmine Flower Yield and Plant Health Monitoring. In 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC) (pp. 127–132). IEEE. (2024).
-
Srivastava, Y. et al. July. Apple Fruit Flower Detection Using Auto-Encoder Convolutional Neural Network. In 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) (pp. 1–10). IEEE. (2024).
-
Monikapreethi, S. K., Geetha, D., Narayana Moorthy, G. & Monisha, R. October. YOLOv8-based Agricultural Robots for Precision Pollination and Yield Prediction. In 2025 International Conference on Sustainable Communication Networks and Application (ICSCN) (pp. 544–549). IEEE. (2025).
-
Mu, X., He, L., Heinemann, P., Schupp, J. & Karkee, M. Mask R-CNN based apple flower detection and king flower identification for precision pollination. Smart Agricultural Technology, 4, p.100151. (2023).
-
Gao, C. et al. A novel pollination robot for kiwifruit flower based on preferential flowers selection and precisely target. Computers and Electronics in Agriculture, 207, p.107762. (2023).
-
Sapkota, R. et al. Robotic pollination of apples in commercial orchards. arXiv preprint arXiv:2311.10755. (2023).
-
Mu, X. & He, L. Mask R-CNN based king flowers identification for precise apple pollination. In 2021 ASABE Annual International Virtual Meeting (1). American Society of Agricultural and Biological Engineers. (2021).
