Proceedings

Find matching any: Reset
Sharma, A
Schindelbeck, R
Saraswat, D
Agili, H
Pierce, F
Rabe, N
Sun, C
Add filter to result:
Authors
Pierce, F
Perry, E.M
Young, S.L
Collins, H.P
Carter, P.G
van Es, H
Sela, S
Marjerison, R
Moebiu-Clune, B
Schindelbeck, R
Moebius-Clune, D
Agili, H
Chokmani, K
Cambouris, A
Perron, I
Poulin, J
Saifuzzaman, M
Adamchuk, V.I
Huang, H
Ji, W
Rabe, N
Biswas, A
Jha, S
Saraswat, D
Ward, M.D
Ahmad, A
Aggarwal, V
Saraswat, D
El Gamal, A
Johal, G
Lee, J
song, S
Oh, S
Krishnaswamy, K
Sun, C
Adu-Gyamfi, Y
Nguyen, A
Sharma, A
Prasad, R
Topics
Precision Carbon Management
Decision Support Systems in Precision Agriculture
Applications of Unmanned Aerial Systems
Big Data, Data Mining and Deep Learning
Applications of Unmanned Aerial Systems
Precision Agriculture and Global Food Security
In-Season Nitrogen Management
Type
Oral
Poster
Year
2010
2016
2018
2022
2024
Home » Authors » Results

Authors

Filter results8 paper(s) found.

1. Performance Of The Veris Nir Spectrophotometer For Mapping Soil C In The Palouse Soils Of Eastern Washington

Recent advances in sensing technology have made measuring and mapping the dynamics of important soil properties that regulate carbon and nutrient budgets possible. The Veris Technologies (Salinas, KS) Near Infrared (NIR) Spectrometer is one of the first sensors available for collecting geo-referenced NIR soil spectra on-the-go. Field studies were conducted to evaluate the performance of the Veris NIR in wheat grown under both conventional and no-till management in the Palouse region of eastern... F. Pierce, E.M. Perry, S.L. Young, H.P. Collins, P.G. Carter

2. Comparing Adapt-N to Static N Recommendation Approaches for US Maize Production

Large temporal and spatial variability in soil N availability leads many farmers across the US to over apply N fertilizers in maize (Zea Mays L.) production environments, often resulting in large environmental N losses.  Static N recommendation tools are typically promoted in the US, but new dynamic model-based tools allow for more precise and adaptive N recommendations that account for specific production environments and conditions. This study compares two static N recommendation tools,... H. Van es, S. Sela, R. Marjerison, B. Moebiu-clune, R. Schindelbeck, D. Moebius-clune

3. Site-Specific Management Zones Delineation Using Drone-Based Hyperspectral Imagery

Conventional techniques (e.g., intensive soil sampling) for site-specific management zones (MZ) delineation are often laborious and time-consuming. Using drones equipped with hyperspectral system can overcome some of the disadvantages of these techniques. The present work aimed to develop a drone-based hyperspectral imagery method to characterize the spatial variability of soil physical properties in order to delineate site-specific MZ. Canonical correlation analysis (CCA) was used to extract... H. Agili, K. Chokmani, A. Cambouris, I. Perron, J. Poulin

4. Data Clustering Tools for Understanding Spatial Heterogeneity in Crop Production by Integrating Proximal Soil Sensing and Remote Sensing Data

Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected using these sensors may provide essential information for precision or site-specific management in a production field. In this paper, we introduced a new clustering technique was introduced and compared with existing clustering tools for determining relatively homogeneous... M. Saifuzzaman, V.I. Adamchuk, H. Huang, W. Ji, N. Rabe, A. Biswas

5. Analyzing Trends for Agricultural Decision Support System Using Twitter Data

The trends and reactions of the general public towards global events can be analyzed using data from social platforms, including Twitter. The number of tweets has been reported to help detect variations in communication traffic within subsets like countries, age groups and industries. Similarly, publicly accessible data and (in particular) data from social media about agricultural issues provide a great opportunity for obtaining instantaneous snapshots of farmers’ opinions and a method to... S. Jha, D. Saraswat, M.D. Ward

6. Deep Learning-Based Corn Disease Tracking Using RTK Geolocated UAS Imagery

Deep learning-based solutions for precision agriculture have achieved promising results in recent times. Deep learning has been used to accurately classify different disease types and disease severity estimation as an initial stage for developing robust disease management systems. However, tracking the spread of diseases, identifying disease hot spots within cornfields, and notifying farmers using deep learning and UAS imagery remains a critical research gap. Therefore, in this study, high resolution,... A. Ahmad, V. Aggarwal, D. Saraswat, A. El gamal, G. Johal

7. Smart Food Oases: Development of a Distributed Point-to-point Urban Food Ecosystem in Food Desert Areas

Urban agriculture has been getting much attention in the past decade as a solution to overcome food insecurity and accessibility of food for urban residents and to have better green environments in cities. Urban agriculture is expected to provide better nutrients to residents, reduce transportation and environmental costs, and help urban dwellers access food efficiently. The present study is to build a collaborative ecosystem among urban growers/producers and create bridges from these farmers... J. Lee, S. Song, S. Oh, K. Krishnaswamy, C. Sun, Y. Adu-gyamfi

8. Assess the Feasibility of Remote Sensing Vegetation Index for In-season N Status Evaluation with Nitrogen Measurement from Commercial Field

Nitrogen (N) fertilization plays a crucial role in corn production in the United States. Corn, being a major commodity crop, relies heavily on N fertilization throughout its growth cycle to achieve optimal yields and maintain profitability. During this period of rapid N uptake, it's imperative for farmers to supply sufficient N at the right time to support proper crop development. However, the use of N fertilizer comes with environmental considerations as it can be susceptible to loss through... A. Nguyen, A. Sharma, R. Prasad