Proceedings

Find matching any: Reset
Mon, J
Min, C
Add filter to result:
Authors
Thorp, K.R
White, J.W
Conley, M.M
Mon, J
Bronson, K.F
Kantipudi, K
Lai, C
Min, C
Chiang, R.C
Lai, C
Min, C
Chiang, R
Hafferman, A
Morgan, S
Topics
Proximal Sensing in Precision Agriculture
Big Data, Data Mining and Deep Learning
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Type
Oral
Year
2014
2018
Home » Authors » Results

Authors

Filter results3 paper(s) found.

1. Use Of Active Radiometers To Estimate Biomass, Leaf Area Index, And Plant Height In Cotton

Active radiometers have been tested extensively as tools to assess in-season nitrogen (N) status of crops like wheat (Triticum aestivum), corn (Zea mays), and cotton (Gossypium hirsutum).  Fewer studies target in-season plant growth parameters such as biomass, plant height or leaf area index (LAI).  Uses of this plant data include simulation modeling, total N uptake measurements, evapotranspiration (ET) estimates and irrigation... K.R. Thorp, J.W. White, M.M. Conley, J. Mon, K.F. Bronson

2. Weed Detection Among Crops by Convolutional Neural Networks with Sliding Windows

One of the primary objectives in the field of precision agriculture is weed detection. Detecting and expunging weeds in the initial stages of crop growth with deep learning technique can minimize the usage of herbicides and maximize the crop yield for the farmers. This paper proposes a sliding window approach for the detection of weed regions using convolutional neural networks. The proposed approach involves two processes: (1) Image extraction and labelling, (2) building and training our neural... K. Kantipudi, C. Lai, C. Min, R.C. Chiang

3. Precision Agriculture Research Infrastructure for Sustainable Farming

Precision agriculture is an emerging area at the intersection of engineering and agriculture, with the goal of intelligently managing crops at a microscale to maximize yield while minimizing necessary resource. Achieving these goals requires sensors and systems with predictive models to constantly monitor crop and environment status. Large datasets from various sensors are critical in developing predictive models which can optimally manage necessary resources. Initial experiments at University... C. Lai, C. Min, R. Chiang, A. Hafferman, S. Morgan