欧亚国际

    基因领域的进步是提升作物产量很重要的一步。变化背景下，每年作物产量提升对养活较多人口必不可缺。考虑到环境条件的空间和时间多样性，在给定条件下选择或创造优化品系非常具有挑战性。公共和私营部门一直基于作物综合观测法进行育种项目科研，以便较好描述其功能以及相关基因控制。综合利用近地遥感技术与IoT技术、行走机器人以及无人驾驶飞行器（UAV），可采集大量的作物图像。利用深度学习方法，可追溯作物多个特性。这些特性可对与生物胁迫（疾病）以及非生物胁迫（气候、土壤）相关的重要生理性状进行描述。此处我们用传统神经网络（CNN）法，利用田间拍摄图像来评价多个性状。

    注：文章所用户外表型设备均为Hiphen公司开发。北京欧亚国际科技有限公司是Hiphen公司中国区总代理，全面负责其系列产品在中国的市场推广、销售和售后服务。 

Deep learning for interpreting images of crops acquired under field conditions

S. Madec1, G. Daubige 2, D. Dutartre3, S. Liu1,F. Baret1,

B. de Solan 2

1INRA, UMR EMMAH, Avignon, France

2 ARVALIS, Institut du végétal, Avignon, France

3 HiPHEN, Avignon, France 

    Genetic progress is one of the major leverage used to increase food production. Increasing the yearly genetic gain is therefore mandatory to feed the increasing human population under global change issues. Selecting or creating the optimal cultivar for a given locations is very challenging considering the very large spatial and temporal variability of environmental conditions. Over time, public and private sectors have developed breeding programs based on comprehensive observations of the crops to better describe its functioning and the associated genetic control. The combination of proximal sensing with the IoT technologies, rover robots and unmanned aerial vehicles (UAVs) allows gathering a large amount of images of the crops. Multiple attributes of the crops could then be retrieved using deep learning approaches. These attributes describe important physiological traits associated to biotic (diseases)and abiotic (climate, soil) stress. We illustrate here the use of Convolutional neural networks (CNN) to estimate those various traits from images taken in the fields.