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Home > Archives > Volume 20, No 7 (2022) > Article

DOI: 10.14704/nq.2022.20.7.NQ33479

Proposed Automatic Gene Identification and Replacement Framework using Machine Learning

Mukesh Perumala, Vasumathi D

Abstract

Agriculture and the enterprises that support it play an important role in the economy of several countries, such as India. There is little doubt that agricultural research is primarily focused on the fertilizer industry at the moment. The most significant change, on the other hand, can be brought about in the industry by reducing the amount of money lost as a result of the destruction of crops caused by a variety of plant diseases. As a result, the objective of this work is to determine, through the use of computational approaches, the extent to which plant genes have undergone genetic improvement. This article presents a series of algorithms for determining the sequences of immunological genes, causal genes for plant diseases, and improved genetic sequences. All of this is accomplished without changing the species or any other characteristics that are used to distinguish the plants themselves. This work leads to an accuracy rate of 99.6 percent during the genetic enhancements of plants, which in turn leads to a decrease in plant diseases and, by extension, losses due to plant diseases, so enhancing agriculture and making it a field that is more computer-aided.

Keywords

Gene Identification, Hill Climbing, Genetic Search, Positional Gene Replacement, Gene Replacement

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