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Dean, R
Mahanta, S
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Authors
Dhal, S
Louis, J
O'Sullivan, N
Gumero, J
Soetan, M
Kalafatis, S
Lusher, J
Mahanta, S
Ottley, C
Kudenov, M
Balint-Kurti, P
Dean, R
Williams, C
Vincent, G
Kudenov, M
Balint-Kurti, P
Dean, R
Williams, C.M
Topics
Decision Support Systems
Big Data, Data Mining and Deep Learning
Artificial Intelligence (AI) in Agriculture
Type
Oral
Year
2022
2024
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Filter results3 paper(s) found.

1. An IoT-based Smart Real Time Sensing and Control of Heavy Metals to Ensure Optimal Growth of Plants in an Aquaponic Set-up

The concentration of heavy metals that needs to be maintained in aquaponic environments for habitable growth of plants has been a cause of concern for many decades now as it is not possible to eliminate them completely in a commercial set-up. Our goal is to design a cost-effective real-time smart sensing and actuation system in order to control the concentration of heavy metals in aquaponic solutions. Our solution consists of sensing the nutrient concentrations in the aquaponic solution, namely... S. Dhal, J. Louis, N. O'sullivan, J. Gumero, M. Soetan, S. Kalafatis, J. Lusher, S. Mahanta

2. Utilizing Hyperspectral Field Imagery for Accurate Southern Leaf Blight Severity Grading in Corn

Crop disease detection using traditional scouting and visual inspection approaches can be laborious and time-consuming. Timely detection of disease and its severity over large spatial regions is critical for minimizing significant yield losses. Hyperspectral imagery has been demonstrated as a useful tool for a broad assessment of crop health.  The use of spectral bands from hyperspectral data to predict disease severity and progression has been shown to have the capability of enhancing early... G. Vincent, M. Kudenov, P. Balint-kurti, R. Dean, C.M. Williams

3. Automated Southern Leaf Blight Severity Grading of Corn Leaves in RGB Field Imagery

Plant stress phenotyping research has progressively addressed approaches for stress quantification. Deep learning techniques provide a means to develop objective and automated methods for identifying abiotic and biotic stress experienced in an uncontrolled environment by plants comparable to the traditional visual assessment conducted by an expert rater. This work demonstrates a computational pipeline capable of estimating the disease severity caused by southern corn leaf blight in images of field-grown... C. Ottley, M. Kudenov, P. Balint-kurti, R. Dean, C. Williams