Harnessing Machine Learning for Comparative Analysis of Nanomaterials in Agro-Environmental Applications

Abstract

This article explores the transformative potential of integrating nanomaterials (NM) and machine learning (ML) to address critical global challenges, particularly in agriculture sustainability and climate change mitigation. By conducting a comparative analysis of various nanomaterials and their applications in agriculture and environmental protection, we demonstrate how ML techniques can optimize the properties and functionalities of these materials. In agriculture, nanomaterials are used in developing nanofertilizers, nanopesticides, and nanosensors, which enhance crop yield, pest control, and soil health monitoring. In environmental applications, nanofilters help mitigate climate change-related issues. This research underscores the value of combining NM and ML to advance sustainable agro-environmental solutions, highlighting the role of interdisciplinary approaches in creating smarter, more efficient technologies. By leveraging advanced ML algorithms and AI, we can improve the specificity, sensitivity, and accuracy of nanomaterials, offering innovative solutions to challenges such as food security and environmental conservation.