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Kudenov, M
Jens, M
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Authors
Betz, A
Benny, H
Jens, M
Özyurtlu, M
Pflanz, M
Rachow-Autrum, T
Schischmanow, A
Scheele, M
Schrenk, J
Schrenk, L
Zude, M
Gebbers, R
Ottley, C
Kudenov, M
Balint-Kurti, P
Dean, R
Williams, C
Vincent, G
Kudenov, M
Balint-Kurti, P
Dean, R
Williams, C.M
Topics
Precision Horticulture
Big Data, Data Mining and Deep Learning
Artificial Intelligence (AI) in Agriculture
Type
Poster
Oral
Year
2012
2024
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Filter results3 paper(s) found.

1. OptiThin - Precision Fruiticulture by Tree-Specific Mechanical Thinning

Apple cultivars show biennial fluctuations in yields (alternate bearing). The phenomenon is induced by reduced yields in one year due to freeze damage, low pollination rate or other reasons. Consequently, trees develop many flower buds that blossom in the following year. The many flowers lead to a high number of small fruits that won’t be accepted on the market. Endogenous factors (phytohormones and carbohydrate allocation) subsequently establish the biennial cycle. The alternate bearing... A. Betz, H. Benny, M. Jens, M. Özyurtlu, M. Pflanz, T. Rachow-autrum, A. Schischmanow, M. Scheele, J. Schrenk, L. Schrenk, M. Zude, R. Gebbers

2. 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

3. 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