欧亚国际

较近，来自法国农业科学的科学家利用Hiphen公司开发的IoT发表了题为An automatic method based on daily in situ 2 images and deep learning to date wheat heading stage的文章，Hiphen公司也参与了该科研项目。Hiphen公司提供一体化的室外表型和服务解决方案。

An automatic method based on daily in situ 2 images and deep learning to date wheat

heading stage

Kaaviya Velumani1,2* , Simon Madec2 , Benoit de Solan3 , Raul Lopez-Lozano2 , Jocelyn Gillet1 , Jeremy Labrosse1 , Stephane Jezequel3 , Alexis Comar1 , Frédéric Baret2 

*Corresponding author: kvelumani@hiphen-plant.com; kaaviya.velumani@inra.fr

Hiphen SAS, 228, route de l’aérodrome – CS 40509, 84914 Avignon Cedex 9 – France

INRAE, Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l’aérodrome – CS 40509, 84914 Avignon Cedex 9 – France Arvalis, 228, route de l’aérodrome – CS 40509, 84914 Avignon Cedex 9 – France

Abstract

Accurate and timely observations of wheat phenology and, particularly, of heading date are instrumental for many scientific and technical domains such as wheat ecophysiology, crop breeding, crop management or precision agriculture. Visual annotation of the heading date in situ is a labour-intensive task that may become prohibitive in scientific and technical activities where high-throughput is needed. This study presents an automatic method to estimate wheat heading date from a series of daily images acquired by a fixed RGB camera in the field. A convolutional neural network (CNN) is trained to identify the presence of ears in small patches. The heading date is then estimated from the dynamics of the ear presence in the patches over time. The method is applied and validated over a large set of experimental sites located in different regions in France, covering three years with nine wheat cultivars. Results show that our method provides good estimates of the heading dates with a root mean square error close to 2 days when compared to the visual scoring from experts. It outperforms the predictions of a phenological model based on the ARCWHEAT crop model calibrated for our local conditions. The potentials and limits of the proposed methodology towards a possible operational implementation in agronomic applications and decision

support systems is finally further discussed.

Keywords

Phenology, Internet of things for Agriculture, Convolutional Neural Networks, field sensors, phenology modelling