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Marin, A
Nkebiwe, M
Williams, C
Watkins, E
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Authors
Cointault, F
Marin, A
Journaux, L
Miteran, J
Martin, R
Straw, C
Bolton, C
Young, J
Hejl, R
Friell, J
Watkins, E
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
Drainage Optimization and Variable Rate Irrigation
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Big Data, Data Mining and Deep Learning
Type
Oral
Poster
Year
2010
2022
2024
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Filter results4 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. Soil Moisture Variability on Golf Course Fairways Across the United States: an Opportunity for Water Conservation with Precision Irrigation

Fairways account for an average of 11.3 irrigated hectares on each of the 15,000+ golf courses in the US. Annual median water use per hectare on fairways is between ~2,800,000 and 14,000,000 liters, depending on the region. Conventional fairway irrigation relies on visual observation of the turfgrass, followed by secondary considerations of short-term weather forecasts, which oftentimes lead to “blanket” applications to the entire area. The concept of precision irrigation is a strategy... C. Straw, C. Bolton, J. Young, R. Hejl, J. Friell, E. Watkins

3. 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

4. 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