Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector

Uğuz, Sinan

doi:10.35377/saucis.03.03.755269

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…• S. UĞUZ, in 2020 [23], Developed a deep learning Single Shot Detector (SSD) framework for disease detection. Olive peacock spot is a plant disease, and the dataset samples 1460 olive leaves.…”

Section: Object Detection With Deep Learningmentioning

confidence: 99%

Touchless Hand Sanitizer Mobile Robot Application: Survey and Dataset Preparation for Deep Learning Recognition

Khaleel,

Raheem

2022

J. Al-Qadisiyah Comp. Sci. Math.

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The COVID-19 pandemic spreads throughout the world, many sanitizer dispenser devices included robots designed to minimize human contact and as less as possible to minimize the speared of this pandemic or any germs among humans. In many positions, these sanitizers dispensers are important to sterilize and help people from infection such as patients and medical staff in hospitals, teachers, and students in schools or universities, etc. In this paper, many studies of sanitizer dispenser devices including mobile robot applications for touchless hand sanitizers especially in the coronavirus pandemic have been explained. The major aim of this survey is to compare and contrast many previous survey methodologies. This study covered the introduction to robot applications, hand sanitizers dispenser device including a mobile robot, and object detection-based deep learning techniques that recognizes people. Object detection with the deep learning method is discussed because it is the most important feature of the hand sanitizer device, which is based on a mobile robot application. Furthermore, in this work, the proposed block diagram of dataset processing has been instigated using the mini-computer Raspberry Pi version 4, Raspberry Pi camera system, and LabelImg graphical tool. These dataset outputs files of each image (label.xml and image.jpg) are prepared and classed into 7 classes to use as inputs to the next future work of deep learning. In future work, we want to create a mobile robot hand sanitizer that recognizes the people using a deep learning approach. The python programing language has been used. The contribution of this work is to create, annotate and prepare the dataset for Deep Learning while the previous related works depended on existing dataset that taken from website .

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Section: Object Detection With Deep Learningmentioning

confidence: 99%

Touchless Hand Sanitizer Mobile Robot Application: Survey and Dataset Preparation for Deep Learning Recognition

Khaleel,

Raheem

2022

J. Al-Qadisiyah Comp. Sci. Math.

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“…In 2020 the Uğuz [8] use a single shot detector, researchers developed a technique to identify olive peacock spot disease (SSD) the average accuracy (AP) value achieved was 96% percent. Ainiwaer et al [9] used a fixed-wing UAV for red-green-blue (RGB) imaging and (CNN)-based VGG16 and VGG19 models.…”

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confidence: 99%

Olive trees cases classification based on deep convolutional neural network from unmanned aerial vehicle imagery

Sehree

Khidhir²

2022

IJEECS

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Unmanned aerial vehicles (UAVs) are one of the various aerial remote sensing platforms with ease of use and cost-effectiveness it can deliver high-resolution imaging, obtained using a variety of sensors. Photogrammetric data is derived by the use of unmanned aerial systems (UAS, which consists of a UAV, sensor(s), and base station). As a result of these types, vegetation monitoring is conceivable. Deep neural networks have had a lot of success with image classification tasks, especially in the remote sensing field. In this paper, we demonstrate how deep neural networks can be used to classify olive trees status from aerial images. We have addressed a multi-class classification problem. In this work five different neural network architectures: VGG16, ResNet50, MobileNet, Xception, and VGG19 had been compared. Transfer learning had been accomplished using training of the fully connected layer(s) at the end of the deep learning layers. We used metrics such as accuracy, precision, recall, and confusion metric to evaluate the results. With accuracy, our model achieves the best results using ResNet50 with an accuracy is (97.2%).

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Peacock Spot Detection in Olive Leaves Using Self Supervised Learning in an Assembly Meta-Architecture

Huaquipaco,

Vera,

Yana-Mamani

et al. 2024

IEEE Access

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Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector

Cited by 7 publications

References 20 publications

Touchless Hand Sanitizer Mobile Robot Application: Survey and Dataset Preparation for Deep Learning Recognition

Touchless Hand Sanitizer Mobile Robot Application: Survey and Dataset Preparation for Deep Learning Recognition

Olive trees cases classification based on deep convolutional neural network from unmanned aerial vehicle imagery

Peacock Spot Detection in Olive Leaves Using Self Supervised Learning in an Assembly Meta-Architecture

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