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

Parallelizing Low Vision Feature Extraction through GPUs
Parallelizing Low Vision Feature Extraction through GPUs
Investigation on Carbon Nano Fiber Reinforced Polyether Ether Ketone/Polyether Imides as Polymer Composite Container for Long Time Nuclear Waste Disposal
Investigation on Carbon Nano Fiber Reinforced Polyether Ether Ketone/Polyether Imides as Polymer Composite Container for Long Time Nuclear Waste Disposal
ATrans-Care
ATrans-Care
Behzad-Vizing Conjecture on Graph Coloring for Product Graphs
Behzad-Vizing Conjecture on Graph Coloring for Product Graphs
Object Detection From Cluttered Image
Object Detection From Cluttered Image
Admissions Apply Now