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Dean, R
Veiga, J.P
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Authors
Veiga, J.P
Cavalcante, D.S
Molin, J.P
Ottley, C
Kudenov, M
Balint-Kurti, P
Dean, R
Williams, C
Vincent, G
Kudenov, M
Balint-Kurti, P
Dean, R
Williams, C.M
Topics
Proximal Sensing in Precision Agriculture
Big Data, Data Mining and Deep Learning
Artificial Intelligence (AI) in Agriculture
Type
Oral
Year
2014
2024
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1. Measuring And Mapping Sugarcane Gaps

Sugarcane is an important crop in tropical regions of the world and especially for Brazil, the largest sugar supplier in the market, also running a domestic fleet of flex-fuel driven vehicles based on ethanol. Site specific production management can impact sugarcane production by increasing yield and reducing cost. Sugarcane fields are planted each five years, in average, and an important parameter that is measured after the planting operation is the gaps caused by problems during planting... J.P. Veiga, D.S. Cavalcante, J.P. Molin

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