Mobile robot vision navigation &amp;amp; localization using Gist and Saliency

Chang, Chin‐Feng; Siagian, Christian; Itti, Laurent

doi:10.1109/iros.2010.5649136

Cited by 36 publications

(1 citation statement)

References 30 publications

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“…Also, YOLO frame detection is seen as a regression problem because it finds targets from start to finish without the need for a complicated pipeline [24], making it very efficient. Moreover, YOLO outperforms other real-time systems regarding mean average precision (mAP) [25]. Goyal et al proposed a model based on the YOLOv5 object detection system to sort fruit for fruit detection and quality detection [26].…”

Section: Related Workmentioning

confidence: 99%

Utilizing UAV Data for Neural Network-based Classification of Melon Leaf Diseases in Smart Agriculture

Robi,

Ahmad,

Izhar

et al. 2024

IJACSA

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Integrating unmanned aerial vehicle (UAV) technology with plant disease detection is a significant advancement in agricultural surveillance, marking the beginning of a transformational era characterised by innovation. Traditionally, farmers have had to rely on manual visual inspections to identify melon leaf diseases, which proves to be a time-consuming and costly process in terms of labour. This paper aims to use UAV technology for plant disease detection to achieve notable progress in agricultural surveillance. Incorporating UAV technology, specifically utilising the You Only Look Once version 8 (YOLOv8) deeplearning model, is revolutionary in precision agriculture. This study uses UAV imagery in precision agriculture to explore the utility of YOLOv8, a powerful deep-learning model, for detecting diseases in melon leaves. The labelled dataset is created by annotating disease-affected areas using bounding boxes. The YOLOv8 model has been trained using a labelled dataset to detect and classify various diseases accurately. Following the training, the performance of YOLOv8 stands out significantly compared to other models, boasting an impressive accuracy of 83.2%. This high level of accuracy underscores its effectiveness in object detection tasks and positions it as a robust choice in computer vision applications. It has been shown that rigorous evaluation can help find diseases, which suggests that it could be used for early intervention in precision farming and to change how crop management systems work. This has the potential to assist farmers in promptly identifying and addressing plant issues, hence altering their crop management practices.

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Section: Related Workmentioning

confidence: 99%