Automation in Agriculture: Design and Evaluation of an Intelligent Robot for Maize Planting with Automatic Irrigation
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 10 |
| Year of Publication : 2024 |
| Authors : Jonat Jacob Franco Casilla, Meliza Fabiola Madueño Tica, Raúl Ricardo Sulla Torres |
10.14445/23488379/IJEEE-V11I10P122
How to Cite?
Jonat Jacob Franco Casilla, Meliza Fabiola Madueño Tica, Raúl Ricardo Sulla Torres, "Automation in Agriculture: Design and Evaluation of an Intelligent Robot for Maize Planting with Automatic Irrigation," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 10, pp. 216-222, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I10P122
Abstract:
This paper presents the design, development, and implementation of a system consisting of a robot and remote irrigation system based on Internet of Things (IoT) technology to optimize the corn planting process and improve agricultural productivity in Peru. The robot consists of sensors, as well as a central node for actuator activation and a web server interface. The robot determines irrigation actions through soil moisture sensors, as well as the state of the climate with humidity and temperature sensors, and data is obtained in real-time as the system is running. The pilot implementation shows the effectiveness of the robot against ideal and disturbed terrains, obtaining high performance in terms of accuracy and sowing of corn seeds, demonstrating the feasibility and effectiveness in a real working situation for agricultural use. This approach promotes more efficient and accurate work in the planting of corn, promoting sustainability for use in agriculture in Peru and offering a technological expansion in supply and improvement of current agricultural production.
Keywords:
Agricultural robot, Automatic irrigation, Image processing, Artificial vision, IoT.
References:
[1] Qixin Sun et al., “Citrus Pose Estimation from an RGB Image for Automated Harvesting,” Computers and Electronics in Agriculture, vol. 211, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Taehyeong Kim et al., “2D Pose Estimation of Multiple Tomato Fruit-Bearing Systems for Robotic Harvesting,” Computers and Electronics in Agriculture, vol. 211, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Binbin Xie et al., “Research Progress of Autonomous Navigation Technology for Multi-Agricultural Scenes,” Computers and Electronics in Agriculture, vol. 211, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Tao Li et al., “A Multi-Arm Robot System for Efficient Apple Harvesting: Perception, Task Plan and Control,” Computers and Electronics in Agriculture, vol. 211, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Xiang Feng et al., “Autonomous Localization and Navigation for Agricultural Robots in Greenhouse,” Wireless Personal Communications, vol. 131, pp. 2039-2053, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Giwan Lee et al., “Enhancing Detection Performance for Robotic Harvesting Systems through RandAugment,” Engineering Applications of Artificial Intelligence, vol. 123, no. C, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Yousef Asiri, “Unmanned Aerial Vehicles Assisted Rice Seedling Detection Using Shark Smell Optimization with Deep Learning Model,” Physical Communication, vol. 59, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Şahin Yıldırım, and Burak Ulu, “Deep Learning Based Apples Counting for Yield Forecast Using Proposed Flying Robotic System,” Sensors, vol. 23, no. 13, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Liang-Bi Chen, Xiang-Rui Huang, and Wei-Han Chen, “Design and Implementation of an Artificial Intelligence of Things-Based Autonomous Mobile Robot System for Pitaya Harvesting,” IEEE Sensors Journal, vol. 23, no. 12, pp. 13220-13235, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Rafiqul Islam, Habibullah Habibullah, and Tagor Hossain, “AGRI-SLAM: A Real-Time Stereo Visual SLAM for Agricultural Environment,” Autonomous Robots, vol. 47, pp. 649-668, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yaoguang Wei et al., “Review of Simultaneous Localization and Mapping Technology in the Agricultural Environment,” Journal of Beijing Institute of Technology, vol. 32, no. 3, pp. 257-274, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Zhen-wei Wu et al., “A Dataset of Tomato Fruits Images for Object Detection in the Complex Lighting Environment of Plant Factories,” Data in Brief, vol. 48, pp. 1-6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Divya Rathore et al., “A Two-Stage Deep-Learning Model for Detection and Occlusion-Based Classification of Kashmiri Orchard Apples for Robotic Harvesting,” Journal of Biosystems Engineering, vol. 48, pp. 242-256, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] A. Zahedi, A.M. Shafei, and M. Shamsi, “Application of Hybrid Robotic Systems in Crop Harvesting: Kinematic and Dynamic Analysis,” Computers and Electronics in Agriculture, vol. 209, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Satyam Raikwar, Hang Yu, and Thomas Herlitzius, “2D LIDAR SLAM Localization System for a Mobile Robotic Platform in GPS Denied Environment,” Journal of Biosystems Engineering, vol. 48, pp. 123-135, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ailian Jiang, and Tofael Ahamed, “Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection,” Sensors, vol. 23, no. 10, pp. 1-26, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Sorn Sooksatra, and Toshiaki Kondo, “CAMSHIFT-Based Algorithm for Multiple Object Tracking,” The 9th International Conference on Computing and InformationTechnology, Advances in Intelligent Systems and Computing, vol. 209, pp. 301-310, 2013.
[CrossRef] [Google Scholar] [Publisher Link]