Abstract
This paper addresses the labor-intensive and wasteful nature of the traditional sugarcane bud cutting method. To overcome these challenges, the proposed approach leverages YOLOv5 technology for sugarcane bud identification. The machine-learning model is trained with diverse data samples, enabling it to accurately distinguish between sugarcane buds and other elements in the images. The implementation yields the best of 79% accuracy in bud detection. By automating the process, the proposed method significantly reduces labor and time requirements while minimizing sugarcane wastage. This innovation presents promising implications for the sugarcane industry, as it streamlines bud identification and optimizes resource utilization. Furthermore, the adoption of YOLOv5 technology can potentially extend to other agricultural domains, offering opportunities for enhanced crop management and sustainable farming practices.
Central University of Karnataka, Kalaburagi.
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Acknowledgement
Authors thank the Ministry of Electronics and Information Technology (MeitY), New Delhi for granting Visvesvaraya Ph.D. fellowship through awardee no. MEITY-PHD-1674209407515 and Dated: 02/01/2023.
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Sindhe, P.S., Hegadi, R.S. (2024). Sugarcane Bud Detection Using YOLOv5. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2026. Springer, Cham. https://doi.org/10.1007/978-3-031-53082-1_17
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DOI: https://doi.org/10.1007/978-3-031-53082-1_17
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