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Ampatzidis, Y
Allphin, E
Aboutalebi, M
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Authors
Allphin, E
Kitchen, N.R
Suddeth, K.A
Thompson, A
Thompson, A
Boardman, D.L
Kitchen, N
Allphin, E
Ampatzidis, Y
Derival, M
Kakarla, S
Albrecht, U
Zhang, X
Zhou, C
Lee, W
Pourreza, A
Schueller, J.K
Liburd, O.E
Ampatzidis, Y
Zuniga-Ramirez, G
Sams, B
Aboutalebi, M
Sanchez, L
Dokoozlian, N
Bramley, R
Sams, B
Previtali, P
Mezger, J
Aboutalebi, M
Sanchez, L
Dokoozlian, N
Zhou, C
Ampatzidis, Y
Ampatzidis, Y
Zhou, C
Ampatzidis, Y
Guan, H
Liu, W
de Oliveira Costa Neto, A
Kunwar, S
Batuman, O
Topics
Spatial Variability in Crop, Soil and Natural Resources
Spatial Variability in Crop, Soil and Natural Resources
Precision Horticulture
Big Data, Data Mining and Deep Learning
Precision Horticulture
Artificial Intelligence (AI) in Agriculture
International Symposium on Robotics and Automation
Robotics and Automation with Row and Horticultural Crops
Type
Poster
Oral
Year
2010
2014
2018
2022
2024
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Filter results9 paper(s) found.

1. Nitrogen Loss In Corn Production Varies As A Function Of Topsoil Depth

  Understanding availability and loss potential of nitrogen for varying topsoil depths of poorly-drained claypan soil landscapes could help producers make improve decisions when managing crops for feed grain or bio-fuels.  While it has been well documented that topsoil depth on these soils plays an important role in storing water for crop growth, it is not well known how this same soil... E. Allphin, N.R. Kitchen, K.A. Suddeth, A. Thompson

2. Water And Nitrogen Use Efficiency Of Corn And Switchgrass On Claypan Soil Landscapes

Claypan soils cover a significant portion of Missouri and Illinois crop land, approximately 4 million ha. Claypan soils, characterized with a pronounced argilic horizon at or below the soil surface, can restrict nutrient availability and uptake, plant water storage, and water infiltration. These soil characteristics affect plant growth, with increasing depth of the topsoil above the claypan horizon having a strong positive correlation to grain crop production. In the case of low... A. Thompson, D.L. Boardman, N. Kitchen, E. Allphin

3. Evaluation of HLB-Infected Citrus Rootstocks Using Ground Penetrating Radar

Citrus production in Florida continues to decline steadily, since the arrival of Huanglongbing (HLB or citrus greening). HLB does not kill the tree, but HLB-infected trees become less productive. Since now, there is no cure for this disease. However, several strategies have been developed to manage and control HLB-infected citrus trees. We have developed and evaluated a heat thermotherapy system (short-term solution) for sustaining productivity of HLB-affected trees. This system heats the canopy... Y. Ampatzidis, M. Derival, S. Kakarla, U. Albrecht, X. Zhang

4. Strawberry Pest Detection Using Deep Learning and Automatic Imaging System

Strawberry growers need to monitor pests to determine the options for pest management to reduce damage to yield and quality.  However, manually counting strawberry pests using a hand lens is time-consuming and biased by the observer. Therefore, an automated rapid pest scouting method in the strawberry field can save time and improve counting consistency. This study utilized six cameras to take images of the strawberry leaf. Due to the relatively small size of the strawberry pest, six cameras... C. Zhou, W. Lee, A. Pourreza, J.K. Schueller, O.E. Liburd, Y. Ampatzidis, G. Zuniga-ramirez

5. Evaluation of a Single Transect Method for Collecting Grape Samples Based on Sentinel-2 Imagery for the Characterization of Overall Vineyard Performance

Commercial vineyards are streamed into different wine programs based on analysis of grape or juice samples collected from the field, but spatial and temporal variability can lead to sub-optimal tiering of grapes. This is a particularly difficult problem to overcome in the typically large vineyards of California’s Central Valley. Due to economic and laboratory constraints on sample collection, processing, and analysis, a single sample is often expected to represent the overall fruit quality... B. Sams, M. Aboutalebi, L. Sanchez, N. Dokoozlian, R. Bramley

6. Precision Tools for Monitoring Experimental Irrigation Treatments in California Vineyards

Precision farming techniques, such as zonal management and variable rate nutrient delivery, have been used to manage spatial variability in many crops. Wine grapes, and most permanent crops, have been slower than row crops or agronomic crops to take advantage of these techniques, though there are barriers to implementing these methods when compared to agronomic crops. The objective of this project is to show how a suite of monitoring and management tools can be used to evaluate the performance... B. Sams, P. Previtali, J. Mezger, M. Aboutalebi, L. Sanchez, N. Dokoozlian

7. AI-enabled 3D Vision System for Rapid and Accurate Tree Trunk Detection and Diameter Estimation

Huanglongbing (HLB) is the major threat to citrus production in Florida. Imidacloprid and oxytetracycline injections were proven to be effective in controlling HLB. The total amount of imidacloprid and oxytetracycline needs to be injected for the tree depending on the trunk diameter. Therefore, precisely measuring trunk diameter is important to effectively control the HLB. However, manually injecting imidacloprid or oxytetracycline and measuring the trunk diameter is time-consuming and labor-intensive.... C. Zhou, Y. Ampatzidis

8. Can AI and Automation Transform Specialty Crop Production?

... Y. Ampatzidis

9. Agrosense: AI-enabled Sensing for Precision Management of Tree Crops

Monitoring the tree inventory and canopy density and height frequently is critical for researchers and farm managers. However, it is very expensive and challenging to manually complete these tasks weekly. Therefore, a low-cost and artificial intelligence (AI) enhanced sensing system, Agrosense, was developed for tree inventory, canopy height measurement, and tree canopy density classification in this study. The sensing system mainly consisted of four RGB-D cameras, two Jetson Xavier NX, and one... C. Zhou, Y. Ampatzidis, H. Guan, W. Liu, A. De oliveira costa neto, S. Kunwar, O. Batuman