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

Development of Surface-Modified Carbon Steel by employing Advanced Surface Engineering Technique
Development of Surface-Modified Carbon Steel by employing Advanced Surface Engineering Technique
Cow-kin: Smart and Precision Animal Farming for the Health and Welfare of the Cattle
Cow-kin: Smart and Precision Animal Farming for the Health and Welfare of the Cattle
Center of Excellence in Advanced Materials and Green Technologies
Center of Excellence in Advanced Materials and Green Technologies
Edge Preserving Image Fusion Using RMS Contrast and Linear Prediction Model
Edge Preserving Image Fusion Using RMS Contrast and Linear Prediction Model
ML based Visible Light Communication
ML based Visible Light Communication
Admissions Apply Now