Inspection of pollination transfer and success in coffee flowering detection using intersection over union based cascade RCNN in a vision environment.

The intricate process of coffee blossoming, pollination transfer, and successful development is crucial for creating every exquisite cup of coffee. During the flowering stage of coffee plants, delicate white flowers with a pleasant fragrance appear briefly, providing a limited opportunity for effective pollination. At this stage, the stigma of the flower, which is the female reproductive organ, becomes receptive and prepared to receive pollen. Existing research found methods such as machine learning and image analysis for monitoring crop pollination. Manual image annotation is conducted on pollen count disregarding spatial component of pollen collection which is essential for successful pollination. However, use of these strategies on coffee flowers by their complex structure and continuous changed in flowering stages. The paper represents novel methodologies by introducing methodological approach “IoU-AI” to monitor pollen transmission and success of pollination in coffee flower. IoU-AI utilize high resolution coffee flower image accurately track and detect floral organs by offering insight to pollen transfer. IoU-AI employs deep learning models to detect and observe floral components like stigma and anthers. Further computes overlap between structures and estimate pollen transmission. The flower detection accuracy was evaluated against ground truth measurements. The accuracy of coffee flower detection ranged from 94.77% to 85.34% for flower stages ranging from 20% to 100% blooming.