Prevention of Aflatoxin in Peanut Using Naive Bayes Model

Abstract

Peanut is widely cultivated as a food and oilseed crop. The cultivation of peanut has more health and economic benefits but it also has the most important challenges faced by peanut growers. The greatest destructive diseases are the arousal of plant pathogens like bacteria, fungi, viruses, and nematodes. This will result in the poor yields and hence it affects the quality of the production. The most common fungal diseases of peanut are the early leaf spot, late leaf spot and groundnut rust. The peanut yield losses are usually 50%. The available fungicide for the management of fungal diseases usually puts an additional burden on the growers. The alternative disease managements are cultural practices, planting resistant cultivars, usage of bio control agents, etc. can be useful in the management of diseases by reducing the frequency of application of fungicides. The situation will get aggravated if there’s a climatic change. The objective of this project is to find the growth of fungi in the peanut crop and warn the farmers in order to produce good production of Peanut. The growth of the organism is determined by different factors and the factors are plotted with KNN algorithm which determines the rapid growth of the organism in the nearby spots. The model is implemented using Naïve Bayes & alerted to the farmers about the possibility of the fungal growth.