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Deep learning for image-based weed detection in turfgrass


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Publication date: March 2019

Source: European Journal of Agronomy, Volume 104

Author(s): Gatien N. Falconnier, Etienne-Pascal Journet, Laurent Bedoussac, Anthony Vermue, Florent Chlébowski, Nicolas Beaudoin, Eric Justes

Abstract

Faba bean (Vicia Faba L.) is the second most widely grown grain legume in Europe after pea (Pisum Sativum L.) and presents several agronomic and environmental advantages when inserted in cropping systems (e.g. decreased dependency on synthetic fertilisers and N provision to the subsequent crop). However, yield variability due to several factors including heat and drought impede wide adoption of faba bean by farmers. Soil-crop models provide quantitative information to evaluate these processes and help to design innovative cropping system including legumes. The STICS model was chosen for its agro-environmental purpose and its genericity allowing crop rotation simulation and robustness for a wide range of pedoclimatic conditions. However, there is so far for STICS no parameterization for faba bean. We calibrated 38 crop related parameters based on literature, direct measurements and sequential estimation using the optimisation tool OptimiSTICS and a dataset of winter faba bean grown in two sites in France with contrasting soil conditions over several growing seasons (2002?2015). Data from 22 experimental plots were used for calibration and the remaining independent 13 plots were used for model evaluation. After calibration, the STICS model reproduced adequately phenology, Leaf Area Index and dynamic growth of above ground biomass, uptake of mineral N, N2 fixation and grain yield, with satisfactory model efficiency (0.56 to 0.81) and low relative bias (?7% to 2%). The model adequately reproduced the large observed variation in faba bean grain yield (0.42?4.70?t ha?1) and total N2 fixed at harvest (62?172?kg N ha?1) in the contrasted years and soil conditions of this study. Simulations indicated that water stress was the overriding factor driving yield and N2 fixation variability. Simulation of temporal crop growth and water stresses during grain onset and grain filling allowed a robust and credible agronomic diagnosis of the causes of this variability for faba bean crops not significantly damaged by pests and diseases. Water supply/demand ratio averaged over a period of six days preceding beginning of grain filling explained 78% of the observed grain yield variability while water deficit factor for N2 fixation averaged over a period of 20 days following the beginning of grain filling explained 83% of the variability of fixed N2 at harvest. Our work provides a first calibration and evaluation of the STICS model for faba bean. It offers the opportunity to quantify the ecosystem services associated with crop rotations including faba bean and the effect of climate change on the performance of such rotations.


COMENTARIOS

[16/07/2018] - Comienza el proyecto europeo WASTE4GREEN, en el que están involucradas 8 entidades de España, Portugal e Italia, entre las que se encuentra CTAEX

[18/06/2018] - CTAEX recibe el premio La Besana por su labor investigadora dirigida al sector agroalimentario extremeño

[07/06/2018] - CTAEX participa en el 13 Congreso Mundial del Tomate mostrando el camino de la innovación del campo a la mesa

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