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Massey, R
Melchiori, R.J
Marshall, J
Morandi, B
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Authors
Kemerer, A.C
Albarenque, S.M
Melchiori, R.J
Walsh, O.S
Belmont, K
McClintick-Chess, J
Marshall, J
Jackson, C
Thompson, C
Swoboda, K
Bresilla, K
Manfrini, L
Boini, A
Perulli, G
Morandi, B
Grappadelli, L.C
Kitchen, N.R
Ransom, C.J
Schepters, J.S
Hatfield, J.L
Massey, R
Topics
Applications of UAVs (unmanned aircraft vehicle systems) in precision agriculture
Precision Nutrient Management
Big Data, Data Mining and Deep Learning
In-Season Nitrogen Management
Type
Oral
Poster
Year
2014
2016
2018
2022
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Filter results4 paper(s) found.

1. Unmanned Aerial System To Determine Nitrogen Status In Maize

Maize field production shows spatial variability during vegetative crop growth that could be used to prescribe nitrogen variable rates. The use of portable sensors mounted on high-clearance applicators is well documented, however new UAS vehicle equipped with high resolution digital cameras could be used to determine crop spatial variability with the advantage of survey extensive field areas. To our knowledge, comparisons between vegetation indices obtained by a modified digital camera and... A.C. Kemerer, S.M. Albarenque, R.J. Melchiori

2. UAV-based Crop Scouting for Precision Nutrient Management

Precision agriculture – is one of the most substantial markets for the Unmanned Aerial Vehicles (UAVs). Mounted on the UAVs, sensors and cameras enable rapid screening of large numbers of experimental plots to identify crop growth habits that contribute to final yield and quality in a variety of environments. Wheat is one of the Idaho’s most important cereal crops grown in 42 of 44 Idaho counties. We are working on establishing a UAV-based methodology for in-season prediction of wheat... O.S. Walsh, K. Belmont, J. Mcclintick-chess, J. Marshall, C. Jackson, C. Thompson, K. Swoboda

3. Using Deep Learning - Convolutional Naural Networks (CNNS) for Real-Time Fruit Detection in the Tree

Image/video processing for fruit detection in the tree using hard-coded feature extraction algorithms have shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific... K. Bresilla, L. Manfrini, A. Boini, G. Perulli, B. Morandi, L.C. Grappadelli

4. Spatial and Temporal Factors Impacting Incremental Corn Nitrogen Fertilier Use Efficiency

Current tools for making crop N fertilizer recommendations are primarily based on plot and field studies that relate the recommendation to the economic optional N rate (EONR).  Some tools rely entirely on localized EONR (e.g., MRTN). In recent years, tools have been developed or adapted to  account for within-field variation in crop N need or variable within season factors. Separately, attention continues to elevate for how N fertilizer recommendations might account for environmental... N.R. Kitchen, C.J. Ransom, J.S. Schepters, J.L. Hatfield, R. Massey