March 29, 2024, 4:42 a.m. | Md. Simul Hasan Talukder, Sharmin Akter, Abdullah Hafez Nur

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.18870v1 Announce Type: cross
Abstract: Sugarcane, a key crop for the world's sugar industry, is prone to several diseases that have a substantial negative influence on both its yield and quality. To effectively manage and implement preventative initiatives, diseases must be detected promptly and accurately. In this study, we present a unique model called sugarcaneNet2024 that outperforms previous methods for automatically and quickly detecting sugarcane disease through leaf image processing. Our proposed model consolidates an optimized weighted average ensemble of …

abstract arxiv classification cs.ai cs.cv cs.lg disease diseases ensemble industry influence key lasso negative pre-trained models quality type world

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