Machine Learning and Deep Learning Approaches for Smart Agricultural Systems

Machine Learning and Deep Learning Approaches for Smart Agricultural Systems

Julure Raviteja, T Virajitha, Gayatri Godavarthi

Assistant Professor, Department of CSE, Guru Nanak Institution Technical Campus, Hyderabad.

Assistant Professor, Department of CSE(DS), CMR Engineering College, Hyderabad.

Assistant Professor, Department of Emerging Technologies, Malla Reddy College of Engineering and Technology, Hyderabad.

Abstract: With the advent of smart agricultural systems, conventional farming methods have been utterly transformed by the incorporation of ML and DL. By utilizing cutting-edge technology for data analysis and decision-making, these systems improve sustainability, efficiency, and production. The use of ML and DL in crop monitoring, soil management, insect identification, and yield prediction are some of the agricultural areas explored in this work. As far as applying these technologies in agriculture is concerned, we go over the main techniques, present developments, and possible future prospects. If you live in a nation like India, where farming and agricultural output are major factors in the economy, you know how vital the agricultural sector is to the country's prosperity. Findings show that deep learning methods outperform machine learning when it comes to predicting total agricultural output. Some of the parameters used in the study include the following: year, crop, area, production, and the name of the state or district. The internet resource data.gov.in was used to get the dataset. A comparison of the Deep Learning Sequential model's performance with that of the Machine Learning Random Forest.

Keywords: Agriculture, Artificial Intelligence, Machine Learning, Crop Production, Deep Learning.