A strawberry harvest‐aiding system with crop‐transport collaborative robots: Design, development, and field evaluation

Abstract

Mechanizing the manual harvesting of fresh market fruits constitutes one of the biggest challenges to the sustainability of the fruit industry. During manual harvesting of some fresh-market crops like strawberries and table grapes, pickers spend significant amounts of time walking to carry full trays to a collection station at the edge of the field. A step toward increasing harvest automation for such crops is to deploy harvest-aid collaborative robots (co-bots) that transport empty and full trays, thus increasing harvest efficiency by reducing pickers' non-productive walking times. This study presents the development of a co-robotic harvest-aid system and its evaluation during commercial strawberry harvesting. At the heart of the system lies a predictive stochastic scheduling algorithm that minimizes the expected non-picking time, thus maximizing the harvest efficiency. During the evaluation experiments, the co-robots improved the mean harvesting efficiency by around 10% and reduced the mean non-productive time by 60%, when the robot-to-picker ratio was 1:3. The concepts developed in this study can be applied to robotic harvest-aids for other manually harvested crops that involve walking for crop transportation.

Development of a Supervised Robotic Prototype for Citrus Harvesting

“…Basing on the conceptual models of different studies on robotics development by different researchers like Wan Ishak W.I (2010), Jiacheng Rong (2022), Amish Patel (2016), Wenkang Chen ( 2020), (Hamido & Morgan, 2018), (Chen et al, 2020), Yuki Onishi ( 2019), (Jia et al, 2020), (Didamony & Shal, 2020), (Peng et al, 2021), (Jawad et al, 2020), (Arad et al, 2020), ,(Xu Xiao, 2022), and (Jiacheng Rong, 2022), who have contributed much in agribots developments globally, a conceptual framework for this study was developed as shown below: For the developed robotic system to operate smoothly, the controller communicates with the driver hub through driver hub by wired means while the drive hub sends instructions from the operators to the microcontroller (Control Hub) through a wireless medium for instructions initiation. After that the, microcontroller checks whether the instructions require the motors connected on to it or the expansion hub.…”

“…With this method, it was identified to be time-consuming though at a low rate because the operator was the one to initiate the end effector to take action. (Peng et al, 2021), came up with a strawberry harvest-aiding system with crop-transport corobots such that manual harvesting of fresh market products like strawberries and grapes are mechanized which was the main challenge in the fruits industry. This robot was designed in dynamic predictive scheduling that was mathematically modeled so that it is able to handle uncertain requests.…”

Robotic Multi-Boll Cotton Harvester System Integration and Performance Evaluation

“…The control systems, navigation algorithm, and objection detection algorithms utilized in various agricultural and non-agricultural robots [5][6][7][8][9][10][11][12][13] can also be adapted for cotton harvesting robots. Several research articles have focused on distinct sub-components of a robotic cotton harvesting system, such as the development of a cotton boll detection model [14][15][16][17][18][19], navigation and path planning algorithms [20][21][22][23][24][25], and end-effector designs [26][27][28][29][30].…”

Power Consumption Analysis of a Prototype Lightweight Autonomous Electric Cargo Robot in Agricultural Field Operation Scenarios

“…This process also remains labor-demanding and harsh to be accomplished by humans, who could be better hired to collect the fruits [18]. Furthermore, recent studies indicate that the optimization of the tray transportation pattern between fruit collection points and trucks, using robot-aided methods, can drastically improve the logistics of the overall harvesting process [41,42].…”