Abstract: Agriculture is a cornerstone of global economic development, constituting 4% of global GDP and contributing over 25% to the GDP of the world's least developed countries[1,2]. Despite its significance, current food systems suffer from alarming levels of pollution, wasteful practices, and adverse impacts on both human health and the environment. In recent studies, 30% of the food produced globally is lost or wasted, which worsens the problems associated with food security, climate change, and environmental degradation.[3,4]. Addressing these issues and implementing effective strategies is essential for building a sustainable and resilient food system. Through innovative research on leaf disease identification, we aim to leverage artificial intelligence (AI) to tackle this pressing agricultural concern. This study evaluates the effectiveness of two classifiers, the Random Forest Classifier and Gaussian Naive Bayes (GaussianNB), in detecting leaf diseases. Additionally, we introduce novel parameters specifically designed for Gaussian Naive Bayes (GaussianNB) to enhance its performance in disease identification.

Keywords: GaussianNB, Random Forest Classifier, artificial intelligence (AI), Machine Learning(ML)

| DOI: 10.17148/IARJSET.2024.11480