Development and Control of Drones Applied to Monitoring in Fruit Growing during the Harvesting

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 12

Year of Publication : 2023

Authors : Gustavo Adolfo Romero Hito, Yeremie Abraham Pando Bravo, Roni Andre Villena Pampa, Jesús Talavera S, Joseph Guevara M

10.14445/23488379/IJEEE-V10I12P110

How to Cite?

Gustavo Adolfo Romero Hito, Yeremie Abraham Pando Bravo, Roni Andre Villena Pampa, Jesús Talavera S, Joseph Guevara M, "Development and Control of Drones Applied to Monitoring in Fruit Growing during the Harvesting," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 12, pp. 95-101, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I12P110

Abstract:

In the agricultural industry, accurate and efficient monitoring of the state of crops during the harvest stage is essential to ensure the quality of fresh produce, so this work proposes a system for monitoring fruit at the harvest stage through the use of drones and the development of image processing algorithms to estimate the measures of the fruit by comparing stereo vision and ArUco Marker; in addition also develops an algorithm to specify the stage of maturity in which they are. The tests were carried out on avocado crops in the Majes valley, Arequipa. In the tests, the effectiveness of the algorithms was obtained, evaluating the data obtained by the system and the actual data, demonstrating a reliable detection and an accurate calculation of the size of the avocados. The results are visualized by means of maps highlighting the different stages of maturity. This innovative approach presents significant potential for improving crop monitoring and management, especially in regions such as fruit exporting in Arequipa, where quality and precision are crucial to consolidate its position in international markets.

Keywords:

Image processing, Agricultural monitoring, Drone technology, Stereo vision, Measurement estimation.

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. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Giwan Lee et al., “Enhancing Detection Performance for Robotic Harvesting Systems through and Augment,” Engineering Applications of Artificial Intelligence, vol. 123, 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] A. Zlotnick, and E.A. Berger, “Voice Recognition Technology : A Review,” International Journal of Human-Computer Studies, vol. 70, no. 10, pp. 727-758, 2012.
[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 Fruit Images for Object Detection in the Complex Lighting Environment of the 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 (IC2IT2013), pp. 301-310, 2013.
[CrossRef] [Google Scholar] [Publisher Link]