Organic weed control is a vital to improve crop yield with a sustainable approach. In this work, a directed energy weed control robot prototype specifically designed for organic farms is proposed. The robot uses a novel distributed array robot (DAR) unit for weed treatment. Soybean and corn databases are built to train deep learning neural nets to perform weed recognition. The initial deep learning neural nets show a high performance in classifying crops. The robot uses a patented directed energy plant eradication recipe that is completely organic and UV-C free, with no chemical damage or physical disturbance to the soil. The deep learning can classify 8 common weed species in a soybean field under natural environment with up to 98% accuracy.

有机除草控制是提高农作物收益的可持续方法的关键所在。本文提出了一种专为有机农场设计的定向能量除草机器人原型。该机器人利用分布式阵列机器人(DAR)单元进行除草处理。对大豆和玉米数据库进行建模以训练深度学习神经网络进行除草识别。初始的深度学习神经网络在分类农作物方面表现出很高的性能。该机器人使用一种专利的有机除草配方，无紫外线C(UV-C)，并且对土壤没有化学损害或物理扰动。深度学习可以在自然环境下对大豆田中的8种常见杂草进行高达98%准确率的分类。

有机除草控制原型：基于定向能量和深度学习