Proceedings

Find matching any: Reset
Kantipudi, K
Thompson, L
Add filter to result:
Authors
Luck, J
Parrish, J
Thompson, L
Krienke, B
Glewen, K
Ferguson, R.B
Kantipudi, K
Lai, C
Min, C
Chiang, R.C
Topics
Sensor Application in Managing In-season Crop Variability
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2016
2018
Home » Authors » Results

Authors

Filter results2 paper(s) found.

1. Liquid Flow Control Requirements for Crop Canopy Sensor-Based N Management in Corn: A Project SENSE Case Study

While on-farm adoption of crop canopy sensors for directing in-season nitrogen (N) application has been slow, research focused on these systems has been significant for decades. Much emphasis has been placed on developing and testing algorithms based on sensor output to predict N needs, but little information has been published regarding liquid flow control requirements on equipment used in conjunction with these sensing systems. Addition of a sensor-based system to a standard spray rate controller... J. Luck, J. Parrish, L. Thompson, B. Krienke, K. Glewen, R.B. Ferguson

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