Citation: Roshanianfard, A.; Noguchi, N.; Ardabili, S.; Mako, C.; Mosavi, A. Autonomous Robotic System for Pumpkin Harvesting. Agronomy 2022, 12, 1594. https:// doi.org/10.3390/agronomy12071594 Academic Editors: Baohua Zhang and Simon Pearson Received: 23 March 2022 Accepted: 6 May 2022 Published: 30 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). agronomy Article Autonomous Robotic System for Pumpkin Harvesting Ali Roshanianfard 1, * , Noboru Noguchi 2 , Sina Ardabili 3 , Csaba Mako 4 and Amir Mosavi 5,6, * 1 Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 13131-56199, Iran 2 Laboratory of Vehicle Robotics, Graduate School of Agriculture, Hokkaido University, Sapporo 060-8589, Japan; noguchi@cen.agr.hokudai.ac.jp 3 Department of Informatics, J. Selye University, 94505 Komarom, Slovakia; s.ardabili@ieee.org 4 Institute of Information Society, University of Public Service, 1083 Budapest, Hungary; mako.csaba@tk.hu 5 Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, 81107 Bratislava, Slovakia 6 John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary * Correspondence: alirf@uma.ac.ir (A.R.); mosavi.amirhosein@uni-nke.hu (A.M.) Abstract: The present study focused on the development, optimization, and performance evaluation of a harvesting robot for heavyweight agricultural products. The main objective of developing this system is to improve the harvesting process of the mentioned crops. The pumpkin was selected as a heavyweight target crop for this study. The main components of the robot consist of mobile platforms (the main robot tractor and a parallel robot tractor), a manipulation system and its end-effector, and an integrated control unit. The development procedure was divided into four stages: stage I (designed system using Solidworks), stage II (installation of the developed system on a temporary platform), stage III (developed system on an RT-1 (Yanmar EG453)), and stage IV (developed system on an RT-2 (Yanmar YT5113)). Various indicators related to the performance of the robot were evaluated. The accuracy of 5.8 and 4.78 mm in x and y directions and repeatability of 5.11 mm were observed. The harvesting success rate of 87~92%, and damage rate of 5% resulted in the evaluation of the final version. The average cycle time was 35.1 s, 42.6 s, and 43.2 s for stages II, III, and IV, respectively. The performance evaluations showed that the system’s indicators are good enough to harvest big-sized and heavy-weighted crops. Development of the unique and unified system, including a mobile platform, a manipulation system, an end-effector, and an integrated algorithm, completed the targeted harvesting process appropriately. The system can increase the speed and improve the harvesting process because it can work all day long, has a precise robotic manipulation and end-effector, and a programmable controlling system that can work autonomously. Keywords: harvesting machines; agricultural machines; artificial intelligence; smart farming; robotics; harvesting robots; IoT; agronomy; agriculture; sustainable development 1. Introduction Improvement of the mechanized food supply systems and self-sufficiency in the agriculture industry are critical challenges [1]. These concerns, along with many others such as limited agricultural farms, climate change, water crisis, labor shortage, farmer income reduction, and culture changes, threaten the output of farm works. These problems with their complexity push scientists to pursue a goal of “producing more food with limited resources”. Artificial intelligence (AI) and agricultural robots (AR) as robotic technology can be a benchmark technology to answer this question. Developing robots for agriculture farms which are unpredictable environments, needs specific consideration. The ARs can have uninterrupted activity. They have multiple programmability. And also they have programmed for various missions. The development of ARs as an intelligent system has many challenges, such as auto- navigation systems [2], sensor fusion [3], real-time motion detection [4], and multi-robot Agronomy 2022, 12, 1594. https://doi.org/10.3390/agronomy12071594 https://www.mdpi.com/journal/agronomy