The advancement in technology and intense usage of various devices like laptop, tablet, mobile phone etc. have reduced the existence of natural pollinators likes bees. This decline of bees is also attributed to various factors like high temperature due to climate change, extensive usage of fertilizers and insecticide to enhance the crop yield. The yield, type and the quality of crop depends to a great extent on the pollination In this context, the probable solution is to use technology to mimic pollinators. The identification of the Hibiscus flower is the very first step in deciding whether the flower is ready for pollination. In this paper, we have designed a framework for detecting Hibiscus flower and its parts through TensorFlow implementations for YOLOV3 and SSD MobileNet. The simulation study proves that the data set is appropriately trained in YOLOV3 and SSD MobileNet with the loss reducing in successive iterations. Finally, both training and validation sets converge justifying that the training is appropriate. The results also shows that the accuracy of both YOLOV3 and SSD MobileNet increases with the increase in the epochs.
- SSD MobileNet
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Mahesh, M., Rohan, R., Padmapriya, V., Sujatha, D.N. (2022). A Framework to Detect Hibiscus Flower Using YOLOV3 and SSD MobileNet. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-16-3690-5_64
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Online ISBN: 978-981-16-3690-5
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