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

Kernel Based Approaches for Context Based Image Annotation
Kernel Based Approaches for Context Based Image Annotation
Development of Ultra High Temperature Resistance Polymeric Nano Composite for Long Distance Space Applications
Development of Ultra High Temperature Resistance Polymeric Nano Composite for Long Distance Space Applications
Plant Growth Promoting Endophytic Bacteria from Mangrove Plants in South West Coast of Kerala
Plant Growth Promoting Endophytic Bacteria from Mangrove Plants in South West Coast of Kerala
Investigation into the surface integrity of the Titanium Alloys during high speed machining
Investigation into the surface integrity of the Titanium Alloys during high speed machining
Predictive Threat Evaluation in Complex IT Systems
Predictive Threat Evaluation in Complex IT Systems
Admissions Apply Now