Ready or Not? A Robot-Assisted Crop Harvest Solution in Smart Agriculture Contexts
Ready or Not? A Robot-Assisted Crop Harvest Solution in Smart Agriculture Contexts
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DOI：
10.1109/smartcomp58114.2023.00088
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Thai Thao Nguyen;Jesse Parron;Omar Obidat;A. R. Tuininga;Weitian Wang
中科院分区：
文献类型：
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作者：
Thai Thao Nguyen;Jesse Parron;Omar Obidat;A. R. Tuininga;Weitian Wang
随着机器人技术和人工智能（AI）技术在过去几年中变得越来越重要，它们将不可避免地成为各个行业的关键组成部分，这些行业将继续扩展到技术解决方案。特别地，农业工业已经发展到使用这种手段来最小化人类参与并减少耗时和昂贵的任务。基于此，我们开发了一个机器人辅助的作物成熟度识别和收获系统，以准确地分类和检测作物的成熟度阶段，即未成熟、中熟和未成熟。我们提出的方法集成了计算机视觉，图像处理，协作机器人技术和人工智能的一个子类-迁移学习。训练基于迁移学习的模型，以分类和识别处于成熟阶段的作物，并在实时检测期间定位作物。在真实世界的机器人辅助智能农业环境中的实验结果和分析成功地证明了作物成熟度识别的准确性，证明了迁移学习可以有效地提高农业行业收获过程的效率和生产力。最后对今后的研究工作进行了展望。
As robotics and artificial intelligence (AI) technologies have become increasingly relevant over the past couple of years, they will inevitably be key components for industries of all aspects which continue to expand to technological solutions. Particularly, the agricultural industry has progressed to using such means to minimize human involvement and reduce tasks that are time-consuming and costly. Motivated by this, we developed a robot-assisted crop maturity recognition and harvest system to accurately classify and detect the stages of ripeness the crops are in—ripe, medium ripe, and not ripe. Our proposed approach integrates computer vision, image processing, collaborative robotics, and a subcategory of artificial intelligence—transfer learning. The transfer learning-based model is trained to classify and recognize the crop in its maturity stages and locate the crop during real-time detection. Experimental results and analysis in real-world robot-assisted smart agriculture environments successfully demonstrated crop ripeness recognition accuracy, proving transfer learning could be utilized to effectively improve the efficiency and productivity of harvesting processes in the agricultural industry. The future work of this study is also discussed.