About
SRPNet is a web application for digital image processing based on a convolutional deep neural network using the technique
of pixel-wise semantic segmentation. The research behind the SRPNet was conducted by the Soil Water Processes group located within the
Department of Agronomy at Kansas State University. Our goal is to provide a tool to characterize the land cover of agricutlural fields
to help conservation planners, farmers, ranchers, forest producers, and researchers making better decisions to improve soil health and
conserve soil and water resources. Accurately quantifying the proportion of each component covering the soil surface is an essential
step towards implementing improved management strategies for building soil health and improving soil and water conservation.
Team
Dishan Nahitiya - Master student in the Department of Computer Science - Kansas State Universtiy
Mohammad Bisheh - Doctoral student in the Department of Industrial Engineering - Kansas State Universtiy
Andres Patrignani - Assistant Professor in Soil Water Processes - Department of Agronomy - Kansas State Universtiy
Contact
For feedback or error reporting please contact andrespatrignani@ksu.edu
Support
This research was supported by the USDA NRCS Kansas Conservation Innovation Grant and NSF EPSCoR awards.
