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Rice Plant Leaf Disease Detection Using Machine Learning

N. Vidya Sagar and P. Venkata Siva

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Sagar, N.V. and Siva, P.V., 2021. Rice Plant Leaf Disease Detection Using Machine Learning. United International Journal for Research & Technology (UIJRT), 2(10), pp.98-104.

Abstract

Rice (Oryza Sativa) is a sort of cereal grain that is devoured as a staple food by practically 50% of the universes populace, all the more explicitly in Africa and Asia. The Harvests of Oryza Sativa are presented to both abiotic stresses like the cold, dry season, saltiness, and so forth, and biotic anxieties like creepy crawlies, bugs, bacterial, viral and contagious infections. Moreover, it has become the most moving undertaking for the rancher to distinguish the sort of sickness the harvest has influenced with and which indeed influences the yield of the yield if not ideal identified. This paper gives the potential arrangements utilizing different AI strategies and the near investigation of calculations diagnosing the kind of infection which has influenced the yield depends on the harvests picture information and moreover it gives as of late introduced methods their presentation measure. A portion of the huge illnesses influencing the O. Sativa crop are itemized as follows: A parasitic illness that contaminates the whole yield that can be handily distinguished in the beginning phases as it shows up on the underlying seedling leaves like earthy coloured oval or round spots. The purpose for this is Bipolaris Oryzae a kind of organism, which drops yield as well as influences grain. It spreads across the field from one plant to another plant through the air.

Keywords: Machine learning, Rice plant leaf, Desease detection, Grain, Crop illness.

References

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