Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning

Oleksandr Melnychenko; Lukasz Scislo; Oleg Savenko; Anatoliy Sachenko; Pavlo Radiuk

doi:10.3390/s24061913

Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning

Sensors (Basel). 2024 Mar 16;24(6):1913. doi: 10.3390/s24061913.

Authors

Oleksandr Melnychenko¹, Lukasz Scislo², Oleg Savenko¹, Anatoliy Sachenko^{3

4}, Pavlo Radiuk¹

Affiliations

¹ Faculty of Information Technologies, Khmelnytskyi National University, 11, Instytuts'ka Str., 29016 Khmelnytskyi, Ukraine.
² Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Craków, Poland.
³ Department of Informatics and Teleinformatics, Kazimierz Pulaski University of Technology and Humanities in Radom, ul. Malczewskiego 29, 26-600 Radom, Poland.
⁴ Research Institute for Intelligent Computer Systems, West Ukrainian National University, 11, Lvivska Str., 46009 Ternopil, Ukraine.

Abstract

In the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in sectors like agriculture by using intelligent sensors and advanced computing. Specifically, the task of fruit detection and counting in orchards represents a complex issue that is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide the timely and precise data necessary for these tasks. With the agricultural sector increasingly relying on technological advancements, the integration of innovative solutions is essential. This study presents a novel approach that combines artificial intelligence (AI), deep learning (DL), and unmanned aerial vehicles (UAVs). The proposed approach demonstrates superior real-time capabilities in fruit detection and counting, utilizing a combination of AI techniques and multi-UAV systems. The core innovation of this approach is its ability to simultaneously capture and synchronize video frames from multiple UAV cameras, converting them into a cohesive data structure and, ultimately, a continuous image. This integration is further enhanced by image quality optimization techniques, ensuring the high-resolution and accurate detection of targeted objects during UAV operations. Its effectiveness is proven by experiments, achieving a high mean average precision rate of 86.8% in fruit detection and counting, which surpasses existing technologies. Additionally, it maintains low average error rates, with a false positive rate at 14.7% and a false negative rate at 18.3%, even under challenging weather conditions like cloudiness. Overall, the practical implications of this multi-UAV imaging and DL-based approach are vast, particularly for real-time fruit recognition in orchards, marking a significant stride forward in the realm of digital agriculture that aligns with the objectives of Industry 4.0.

Keywords: YOLOv5; deep learning; fruit detection; fruit yield estimation; synchronization and autonomous movement; unmanned aerial vehicle; video stream transmission.

MeSH terms

Artificial Intelligence*
Deep Learning*
Diagnostic Imaging
Fruit
Intelligence

Grants and funding

This research was partially funded by the Ministry of Education and Science of Ukraine, state grant registration number 0121U112025, project title “Development of information technology for making human-controlled critical and safety decisions based on mental-formal models of machine learning”. This publication reflects the views of the authors only, and the Ministry of Education and Science of Ukraine cannot be held responsible for any use of the information contained therein.