Analyses of Work Efficiency of a Strawberry-Harvesting Robot in an Automated Greenhouse

Abstract

Protected cultivation systems such as greenhouses are becoming increasingly popular globally and have been adopted because of unpredictable climatic conditions and their ability to easily control micro- and macroenvironments. However, limitations such as hazardous work environments and shortages in labor are major concerns for agricultural production using these structures. This has led to the development and adoption of robotic systems. For the efficient use of robots in protected cultivation systems, we formulate the work efficiency problem and model a three-dimensional standard strawberry greenhouse to analyze the effectiveness of a strawberry-harvesting robot compared to different levels of human workforce (experienced, average, and beginner). Simulations are conducted using Quest software to compare the efficiency of different scenarios of robotics to humans. Different methods of improvement from battery capacity and charge rate to harvesting speed are investigated and optimal conditions are recommended. The average hourly production of the robot is about five times lower than that of skilled workers. However, robots are more productive due to their ability to work around the clock. Comparative analyses show that a reduction in harvesting time per strawberry from 3 to 1 s would result in an increase in daily production from 347.93 to 1021.30 kg. This would lead to a five-fold increase in comparison to present daily production. A 10% improvement in battery charge time would result in the battery capacity gaining two extra hours from the current 10 h and would cut the current 2 h needed for charge to 1 h. This paper proposes an operation process and suggestions for changes needed for improving the work efficiency of robots in a greenhouse. This could be extended to other crops and greenhouses.

Produce Harvesting by Laser Stem-Cutting

“…The maintenance-free laser cutting has a long term positive impact on productivity, as compared to literature-dominating mechanical cutting tools that require frequent replacement. To this end, the authors believe that a 3D linear system replacing the robot arm, in addition to a 100 Watt laser module (following the conclusions in [37]) can reduce the average cycle time below the 4 seconds mark, cutting the gap with the average manual picking cycle standing at 1.5s [38]. That, in addition to further enhancing the fruit identification and localization algorithm, would greatly update the current development status [39] thanks to the minimal footprint of the developed tool.…”

Navigating the future: exploring technological advancements and emerging trends in the sustainable ornamental industry

“…In addition, the availability of a skilled workforce that accepts repetitive tasks in uncomfortable greenhouse conditions is decreasing rapidly, causing a reduced availability of workforce (Adegbola et al, 2019;Arad et al, 2020). Furthermore, the issue of labor shortage has become even more relevant during the current COVID-19 pandemic caused by the SARS-CoV-2 virus, which has limited international travel for migrant workers (Woo et al, 2020). These machines can work continuously and efficiently, reducing the need for manual labor and improving overall maintenance quality.…”

Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

“…However, robotic systems have not been used only for environmental monitoring, but also to perform greenhouse unsafe tasks like the UV-C treatment against powdery mildew (Mazar et al 2018). In fact, robotic systems have been designed to help labor tasks such as pesticide spraying with path-planning and traveling-control abilities (Nakao et al 2017;Mahmud et al 2019), transplanting (Han et al 2018), fertilizing (Yuan et al 2016), phenotyping (Atefi et al 2019) and harvesting (Wang et al 2017b;Arad et al 2020;Woo et al 2020). In addition, they have been tightly related to image-based detection algorithms and near-infrared spectroscopy, and therefore, to crop disease detection (Rizk and Habib 2018;Schor et al 2016Schor et al , 2017, classification and picking (Zhao et al 2016;Feng et al 2015).…”