Login

Proceedings

Find matching any: Reset
Landivar, J
Lemes Bosche, L
Add filter to result:
Authors
Lemes Bosche, L
Ciampitti, I
Bhandari, M
Landivar, J
Ghansah, B
Zhao, L
Landivar, J
Pal, P
Fernandez, O
Bhandari, M
Landivar-Scoot, J.L
Eldefrawy, M
Zhao, L
Landivar, J
Topics
In-Season Nitrogen Management
Artificial Intelligence (AI) in Agriculture
Data Analytics for Production Ag
Type
Poster
Oral
Year
2024
Home » Authors » Results

Authors

Filter results3 paper(s) found.

1. Developing a Decision Support Model for Informing N Fertilization in Corn

Assessing crop nitrogen (N) status is crucial for optimizing the application of N fertilizers in corn. The Critical Nitrogen Dilution Curve (CNDC) stands as a fundamental model supporting diagnostic tool for identifying the corn nitrogen (N) status. However, there is a need for efficient, non-destructive methods to estimate the crop N status. The objective of this study was to evaluate the potential of three handheld sensors: SPAD, LI-600, and Green Seeker to diagnose corn N deficiencies at early... L. Lemes bosche, I. Ciampitti

2. Cotton Yield Estimation Using High-resolution Satellite Imagery Obtained from Planet SkySat

Satellite images have been used to monitor and estimate crop yield. Over the years, significant improvements on spatial resolution have been made where ortho images can be generated at 30-centimeter resolution. In this study, we wanted to explore the potential use of Planet SKYSAT satellite system for cotton yield predictions. This system provided imagery data at 50 centimeters resolution, and we collected data 14 times during the season. The data were collected from two different cotton... M. Bhandari

3. Ground-based Imagery Data Collection of Cotton Using a Robotic Platform

In modern agriculture, technological advancements are pivotal in optimizing crop production and resource management. Integrating robotics and image processing techniques allows the efficient collection, analysis, and storage of high-resolution images crucial for monitoring crop health, identifying pest infestations, assessing growth stages, making precise management decisions and predicting yield potential. The objective of this project is to utilize the Farm-NG Amiga robot to develop an image... O. Fernandez, M. Bhandari, J.L. Landivar-scoot, M. Eldefrawy, L. Zhao, J. Landivar