Maneuvering Machine Learning Algorithms to Presage the Attacks of Fusarium oxysporum on Cotton Leaves

Abstract

Web technologies have reached unprecedented levels during this time of modernization. Significant and relevant technological stacks like IoT (Internet of Things), ML (Machine Learning), and AI-influenced crawling and cradling (Artificial Intelligence). These categories are beneficial. In this work, we would try to make use of the notion of Machine Learning Algorithms to predict the attack of Fusarium oxysporum on the leaves of the Cotton plant. It’s a type of ascomycete fungi that forms an infrageneric grouping called a section. All of the species, variations, and forms discovered by Wollenweber and Reinking are Elegans. It belongs to the Nectriaceae family. Many strains of the F. oxysporum complex are soil-borne plant pathogens, especially in agricultural settings, although their primary function in native soils may be as benign or even advantageous as plant endophytes or soil saprophytes. Many textile products are made from cotton. Cotton is used in a variety of products besides the textile industry, including gill nets, coffee filters, tarpaulins, cotton paper, and bookbinding. The cotton used to be used to make fire hoses. India and China are the major cotton producers in 2017, with an annual production of approximately 18.53 million tonnes and 17.14 million tonnes, respectively. The vast majority of this output is used by their textile businesses. This contributes a major portion of the economy. To strengthen the same, we can make use of certain prediction techniques that could foresee if the leaves of cotton suffering from the attack by the pathogens, making use of algorithms like ’Support Vector Machine’, ’Random Forest’, ’k - Nearest Neighbours’, and many more. Further, this work would also compare the efficacy of these algorithms in predicting the damage in the Cotton Leaves. All Codes, Data, and Supplementary Material are made available at https://github.com/Anurag-Dutta/Maneuvering-Machine-Learning-Algorithms-to-presage-the-attacks-of-Fusarium-oxysporum-on-Cotton-Leave