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

Drug Repurposing Workbench
Drug Repurposing Workbench
AI & Digital Tools for Implementation Science
AI & Digital Tools for Implementation Science
Machine Fault Identification : A Unified Approach
Machine Fault Identification : A Unified Approach
An Efficient Scene Understanding System for Digital Farming to Detect Animal and Pest Attack Using Deep Learning 
An Efficient Scene Understanding System for Digital Farming to Detect Animal and Pest Attack Using Deep Learning 
Smart Energy Systems
Smart Energy Systems
Admissions Apply Now