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Marin, A
Nkebiwe, M
Williams, C
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Authors
Cointault, F
Marin, A
Journaux, L
Miteran, J
Martin, R
Weinmann, M
Nkebiwe, M
Weber, N
Bradacova, K
Morad-Talab, N
Ludewig, U
Müller, T
Neumann, G
Raupp, M
Bradacova, K
Ottley, C
Kudenov, M
Balint-Kurti, P
Dean, R
Williams, C
Topics
Modeling and Geo-statistics
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Big Data, Data Mining and Deep Learning
Type
Oral
Year
2010
2024
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Filter results3 paper(s) found.

1. Wheat Growth Stages Discrimination Using Generalized Fourier Descriptors In Pattern Recognition Context

... F. Cointault, A. Marin, L. Journaux, J. Miteran, R. Martin

2. Bio-Effectors As a Promising Tool for Precision Agriculture and Integrated Plant Nutrition

Bio-effectors, such as microorganisms and active natural compounds, are of increasing interest as promising alternatives or substitutes to precarious agrochemicals. European and global markets (valued at 14.6 billion US$ in 2023) for agricultural biologicals (bio-pesticides, bio-fertilizers, and bio-stimulants) are predicted to grow at rates of more than 13.5 % per year. Improved availability and use efficiency of mineral nutrients, tolerance to abiotic stresses, yield and quality traits, as well... M. Weinmann, M. Nkebiwe, N. Weber, K. Bradacova, N. Morad-talab, U. Ludewig, T. Müller, G. Neumann, M. Raupp, K. Bradacova

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