Back close

An Adaptive Neuro-fuzzy Inference System to Monitor and Manage the Soil Quality to Improve Sustainable Farming in Agriculture

Project Incharge:Dr. Remya S.
An Adaptive Neuro-fuzzy Inference System to Monitor and Manage the Soil Quality to Improve Sustainable Farming in Agriculture

The hybrid neuro model is equipped with the high learning capabilities of a neural network and the reasoning ability of fuzzy logic and comes up with a model for effectively correlating the values with the target. This predictive modeling benefits a variety of stakeholders. Accurate projections can assist governments to govern themselves more efficiently.Farmer can come up with their own ideas to increase their production rate in a professional and timely manner. As a result, investors can devise more profitable and effective investment plans. This study and analysis of predictive modeling aim to anticipate the quality of agricultural data by developing a hybrid predictive technique that combines artificial neural network and optimization techniques. 

Related Projects

Evaluating the Role of Energy Services for Enhanced Quality of Life
Evaluating the Role of Energy Services for Enhanced Quality of Life
A Cyber-Physical System for Leak Detection in Water Distribution Networks
A Cyber-Physical System for Leak Detection in Water Distribution Networks
Testing of a Water Hydration-Dehydration Unit
Testing of a Water Hydration-Dehydration Unit
Towards Next-generation Adaptable Computing
Towards Next-generation Adaptable Computing
Grid Integration of Renewable Energy Sources with Power Quality Improvement
Grid Integration of Renewable Energy Sources with Power Quality Improvement
Admissions Apply Now