Digital tools for a biomass prediction from a plant-growth model. Application to a weed control in wheat crop

Abstract : Weed control is essential for crop quality and yield. Usually, the main factors affecting decision-making of farmers in weeding are based on the presence of certain weed varieties and their density (plants/m2). However, the weed management at the scale of the crop cycle refers to other competition indicators, more reliable, such as plant biomass or leaf area index (LAI). Classically, plant biomass measurements are obtained by a destructive method of sampling, time consuming and it turns out to be unworkable in practice for farmers. This project proposes to study an innovative method of biomass evaluation which is based on the acquisition of plot images to determine the leaf area index at young stage. An ecophysiologial model (the crop-growth model Azodyn) was indirectly fed with visible (RGB) images, considered as input parameters, to predict the temporal evolution of the plant biomass until a date close to the acquisition date. First, the model is tested on wheat crop where projected leaf area index (PLAI) of both weed and wheat is determined from image processing. The PLAI is compared to classical measurements (leaf area index and aerial biomass) and a good correlation between the aerial biomass estimated with images and classical biomass measurements is obtained. Second, biomass prediction of the global weed community is estimated from the model using as input parameter initial aerial biomass derived from PLAI-aerial biomass correlation. This preliminary study is a part of a larger project which aims to develop a decision support tool to determine the deadline weeding intervention in a wheat crop taking into account the weed harmfulness on wheat crop through a biomass prediction. Results are encouraging for next studies of this project. Nevertheless, weed detection and biomass prediction can be increased looking at individually for each weed species.
Document type :
Poster communications
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https://hal-agrosup-dijon.archives-ouvertes.fr/hal-02068549
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Submitted on : Friday, March 15, 2019 - 9:27:35 AM
Last modification on : Saturday, March 16, 2019 - 1:19:51 AM

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  • HAL Id : hal-02068549, version 1

Citation

J. Merienne, Annabelle Larmure, Christelle Gée. Digital tools for a biomass prediction from a plant-growth model. Application to a weed control in wheat crop. 3rd AXEMA-EurAgEng Conference, Feb 2019, Villepinte, France. ⟨hal-02068549⟩

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