An integrative approach to agricultural challenges: Predictive modeling, crop alternatives and automation

Document Type

Article

Publication Title

Sigma Journal of Engineering and Natural Sciences

Abstract

The present research will explore the possibility of an integrated method of enhancing agricultural practices in India that utilize machine learning, data science, and Internet of things (IoT) applications. This explorative research is a method of addressing the gap between traditional and modern farming technologies by integrating IoT and data science technologies and supplying the farmers with real-time monitoring and actionable insight and direction through data-driven decision-making. The study is separated into three sections that include predictive modeling, evaluation of business value, and Internet of things (IoT) implementation. This is the main finding of the study that helps develop a predictive model with up to 99% accuracy and make individual recommendations on crop choice and fertilizing in accordance with the soil qualities and environmental situation. Also, the Arduino-based system to an NPK sensor enables the real time checking of soil nutrients to optimize fertility of the soil and agricultural management. The results show that the possibilities of better harvest, fewer losses, and maximized returns can be achieved by using these technologies. The up-to-date interface created in the context of this study allows agriculture producers to make valid decisions and balances between tradition and innovation in the context of sustainable and resilient farming in India.

First Page

1663

Last Page

1682

DOI

10.14744/sigma.2025.00156

Publication Date

10-1-2025

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