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.

翻译：有机杂草控制对于以可持续方式提高作物产量至关重要。本研究提出了一种专为有机农场设计的定向能量杂草控制机器人原型。该机器人采用新型分布式阵列机器人单元进行杂草处理。通过构建大豆和玉米数据库来训练深度学习神经网络以实现杂草识别。初始深度学习神经网络在作物分类方面表现出高性能。该机器人采用已获专利的定向能量植物根除方案，该方案完全有机且不含UV-C紫外线，不会对土壤造成化学损害或物理干扰。该深度学习系统能在自然环境下对大豆田中的8种常见杂草物种进行分类，准确率高达98%。