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

Lightweight, Privacy-Preserving and Usable Security Solutions for Internet of Things
Lightweight, Privacy-Preserving and Usable Security Solutions for Internet of Things
Mitigation of dam induced flood disaster due to hydrological extremes (CoPI)
Mitigation of dam induced flood disaster due to hydrological extremes (CoPI)
Development of Text / Language Independent Speaker Recognition System
Development of Text / Language Independent Speaker Recognition System
Investigation of compatibility between portland pozzolana cement and admixtures in high performance concrete
Investigation of compatibility between portland pozzolana cement and admixtures in high performance concrete
Semantic Integration of Heterogeneous Sources 
Semantic Integration of Heterogeneous Sources 
Admissions Apply Now