Authors
| Filter results3 paper(s) found. |
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1. Deriving Fertiliser VRA Calibration Based on Ground Sensing Data from Specific Field ExperimentsNitrogen (N) fertilisation affects both rice yield and quality. In order to improve grain yield while limiting N losses, providing N fertilisers during the critical growth stages is essential. NDRE is considered a reliable crop N status indicator, suitable to drive topdressing N fertilisation in rice. A multi-year experiment on different rice varieties (Gladio, Centauro, and Carnaroli) was conducted between 2011 and 2017 in Castello d’Agogna (PV), northwest Italy, with the aim of i) establishing... E. Cordero, D. Sacco, B. Moretti, E.F. Miniotti, D. Tenni, G. Beltarre, M. Romani, C. Grignani |
2. Organ Scale Nitrogen Map: a Novel Approach for Leaf Nitrogen Concentration EstimationCrop nitrogen trait estimations have been used for decades in the frame of precision agriculture and phenotyping researches. They are crucial information towards a sustainable agriculture and efficient use of resources. Remote sensing approaches are currently accurate tools for nitrogen trait estimations. They are usually quantified through a parametric regression between remote sensing data and the ground truth. For instance, chlorophyll or nitrogen concentration are accurately estimated using... A. Carlier, S. dandrifosse, B. Dumont, B. Mercatoris |
3. Sun Effect on the Estimation of Wheat Ear Density by Deep LearningEar density is one of the yield components of wheat and therefore a variable of high agronomic interest. Its traditional measurement necessitates laborious human observations in the field or destructive sampling. In the recent years, deep learning based on RGB images has been identified as a low-cost, robust and high-throughput alternative to measure this variable. However, most of the studies were limited to the computer challenge of counting the ears in the images, without aiming to convert... S. Dandrifosse, E. Ennadifi, A. Carlier, B. Gosselin, B. Dumont, B. Mercatoris |