Apple Detection with Occlusions Using Modified YOLOv5-v1

Melnychenko, Oleksandr; Savenko, Oleg; Radiuk, Pavlo

doi:10.1109/idaacs58523.2023.10348779

Cited by 1 publication

(2 citation statements)

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“…The presented research outlines a novel approach for the dynamic capture of images using multiple UAVs, ensuring synchronization between different UAVs and autonomous movement. This is a considerable enhancement over the findings in [ 51 ], which relied on a single camera perspective and did not address the challenges of synchronizing data from multiple sources. Moreover, this study focuses on the development of a system that excels across various fruit varieties and orchard configurations and offers computational efficiency for real-time applications, which is crucial for digital agriculture.…”

Section: Discussionmentioning

confidence: 95%

“…This paper contributes to the field of intelligent sensors and advanced computing in the era of Industry 4.0 by expanding upon Reference [ 51 ] in the domain of digital agriculture, specifically focusing on the integration of UAVs and new data processing methods. The presented research outlines a novel approach for the dynamic capture of images using multiple UAVs, ensuring synchronization between different UAVs and autonomous movement.…”

Section: Discussionmentioning

confidence: 99%

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

Melnychenko,

Scislo,

Savenko

et al. 2024

Sensors

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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.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%