A Study on Smart Agriculture Monitoring Systems Using IoT and Machine Learning Technology

3 Mar

A Study on Smart Agriculture Monitoring Systems Using IoT and Machine Learning Technology

Authors- Assistant Professor Shilpa K, Assistant Professor Dr. V Ganesh Babu

Abstract-Although restricted technology hindered the use of old farming methods, agricultural technology in India has had a tremendous impact on living things. The purpose of this study is to investigate how agricultural practices have changed over time in ancient India. Real-time monitoring and adjustment of system settings to maximize plant growth and support farmers in their job is now possible with smart farming. The current agricultural practices in India have an impact on several Machine Learning (ML) techniques that forecast soil moisture by evaluating unprocessed field data together with variables like humidity and air temperature. These ML models predict soil moisture requirements with accuracy. The study’s main objectives are to evaluate variables such soil moisture, temperature, humidity, and rainfall as well as to discover plant diseases at various phases. With its sophisticated features, machine learning (ML) is a state-of-the-art technology for image analysis and data extraction, improving classification, segmentation, detection, and error-reduction accuracy. This research shows how machine learning (ML) models assist farmers in analyzing massive datasets, enhancing soil quality, and increasing crop yields by using Convolutional Neural Networks (CNN) for model performance evaluation.

DOI: /10.61463/ijset.vol.7.issue2.101