Optimized Computational Intelligence Driven Decision-Making

6Crop Prediction by Implementing Machine Learning in an IoT-Based System

Vivian Rawade* and Shubham Sahoo

Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India

Abstract

In recent years, the agriculture industry has undergone significant transformation due to the integration of data-driven and IoT-based technologies. The integration of computing techniques such as machine learning, data analytics, wireless sensor networks, and the internet of things has demonstrated the significant potential of these technologies in the agriculture industry by increasing both the quantity and quality of agricultural production. To enhance the potential of these technologies, a prediction model has been developed that uses machine learning to determine the most suitable type of crop for a given set of atmospheric and soil data. Furthermore, insights from farmers have been collected through a local survey to understand the impact of these technologies. Despite their potential benefits when incorporated into traditional farming practices, challenges such as high costs, a lack of knowledge among farmers, and compatibility issues with existing farming practices must be addressed to maximize the potential of these technologies. Thus, efforts need to be made to provide farmers with education, training, and incentives to adopt these technologies. In doing so, the agriculture industry can harness various technologies to meet the increasing demand for food while ...