scholarly journals pplication of deep learning model in recognition of growth stages of Cucumis melo L. in greenhouse

2021 ◽  
Vol 63 (11) ◽  
pp. 1-5
Author(s):  
Hoang Anh Tuan Dang ◽  
◽  
Minh Thang Nguyen ◽  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Cucumis Melo ◽  
Financial Analysis ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Growth Stages ◽  
Average Accuracy ◽  
Ip Camera ◽  
Cucumis Melo L

Despite the increasing application of deep learning (DL) models in various socioeconomics such as financial analysis and forecast, intelligent transport, self-driving, disease diagnosis, the effective use of this technology to support agricultural cultivation is still limited. This paper introduces the implementation of the lightest and state-of-the-art YOLOv5 architecture for automatic recognising of important growth stages of Cucumis meloL. from the camera images collected in the greenhouse. This image identification initiative achieved an average accuracy of 96% F1-score in the identification of the five growth stages of Cucumis melo L. using a limited set of training and testing data (total 2,818 images of Cucumis melo L.). These preliminary results lead to the conclusion that the YOLOv5 object detection and classification model is a truly lightweight and promising DL solution after the adoption of the transfer learning technique. Moreover, the YOLOv5 model can execute good performance on edge devices which may open up a new approach in different object detection and classification in real-time directly from a smartphone, Jetson Nano, IP camera...

scholarly journals Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

2021 ◽  
Vol 11 (2) ◽  
pp. 760
Author(s):  
Yun-ji Kim ◽  
Hyun Chin Cho ◽  
Hyun-chong Cho
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Computer Aided Diagnosis ◽  
Medical Data ◽  
Classification Model ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Original Training ◽  
Computer Aided ◽  
Aided Diagnosis

Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors classify gastroscopy images as normal or abnormal. To improve the performance of deep learning, a large amount of training data are required. However, the collection of medical data, owing to their nature, is highly expensive and time consuming. Therefore, data were generated through deep convolutional generative adversarial networks (DCGAN), and 25 augmentation policies optimized for the CIFAR-10 dataset were implemented through AutoAugment to augment the data. Accordingly, a gastroscopy image was augmented, only high-quality images were selected through an image quality-measurement method, and gastroscopy images were classified as normal or abnormal through the Xception network. We compared the performances of the original training dataset, which did not improve, the dataset generated through the DCGAN, the dataset augmented through the augmentation policies of CIFAR-10, and the dataset combining the two methods. The dataset combining the two methods delivered the best performance in terms of accuracy (0.851) and achieved an improvement of 0.06 over the original training dataset. We confirmed that augmenting data through the DCGAN and CIFAR-10 augmentation policies is most suitable for the classification model for normal and abnormal gastric endoscopy images. The proposed method not only solves the medical-data problem but also improves the accuracy of gastric disease diagnosis.

Design of robust deep learning-based object detection and classification model for autonomous driving applications

2022 ◽  
Author(s):  
Mesfer Al Duhayyim ◽  
Fahd N. Al-Wesabi ◽  
Anwer Mustafa Hilal ◽  
Manar Ahmed Hamza ◽  
Shalini Goel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Autonomous Driving ◽  
Classification Model

Comparison of Object Detection Methods for Corn Damage Assessment Using Deep Learning

2020 ◽  
Vol 63 (6) ◽  
pp. 1969-1980
Author(s):  
Ali Hamidisepehr ◽  
Seyed V. Mirnezami ◽  
Jason K. Ward
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Damage Detection ◽  
Damage Identification ◽  
Severe Weather ◽  
Crop Damage ◽  
Growth Stages ◽  
List Type ◽  
Data Set

HighlightsCorn damage detection was possible using advanced deep learning and computer vision techniques trained with images of simulated corn lodging.RetinaNet and YOLOv2 both worked well at identifying regions of lodged corn.Automating crop damage identification could provide useful information to producers and other stakeholders from visual-band UAS imagery.Abstract. Severe weather events can cause large financial losses to farmers. Detailed information on the location and severity of damage will assist farmers, insurance companies, and disaster response agencies in making wise post-damage decisions. The goal of this study was a proof-of-concept to detect areas of damaged corn from aerial imagery using computer vision and deep learning techniques. A specific objective was to compare existing object detection algorithms to determine which is best suited for corn damage detection. Simulated corn lodging was used to create a training and analysis data set. An unmanned aerial system equipped with an RGB camera was used for image acquisition. Three popular object detectors (Faster R-CNN, YOLOv2, and RetinaNet) were assessed for their ability to detect damaged areas. Average precision (AP) was used to compare object detectors. RetinaNet and YOLOv2 demonstrated robust capability for corn damage identification, with AP ranging from 98.43% to 73.24% and from 97.0% to 55.99%, respectively, across all conditions. Faster R-CNN did not perform as well as the other two models, with AP between 77.29% and 14.47% for all conditions. Detecting corn damage at later growth stages was more difficult for all three object detectors. Keywords: Computer vision, Faster R-CNN, RetinaNet, Severe weather, Smart farming, YOLO.

scholarly journals Metaheuristics with Deep Learning-Enabled Parkinson’s Disease Diagnosis and Classification Model

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Adel A. Bahaddad ◽  
Mahmoud Ragab ◽  
Ehab Bahaudien Ashary ◽  
Eied M. Khalil
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Fitness Function ◽  
Disease Diagnosis ◽  
Classification Performance ◽  
Classification Model ◽  
Optimal Subset ◽  
Diagnosis And Classification ◽  
Optimum Solution

Parkinson's disease (PD) affects the movement of people, including the differences in writing skill, speech, tremor, and stiffness in muscles. It is significant to detect the PD at the initial stages so that the person can live a peaceful life for a longer time period. The serious levels of PD are highly risky as the patients get progressive stiffness, which results in the inability of standing or walking. Earlier studies have focused on the detection of PD effectively using voice and speech exams and writing exams. In this aspect, this study presents an improved sailfish optimization algorithm with deep learning (ISFO-DL) model for PD diagnosis and classification. The presented ISFO-DL technique uses the ISFO algorithm and DL model to determine PD and thereby enhances the survival rate of the person. The presented ISFO is a metaheuristic algorithm, which is inspired by a group of hunting sailfish to determine the optimum solution to the problem. Primarily, the ISFO algorithm is applied to derive an optimal subset of features with a fitness function of maximum classification accuracy. At the same time, the rat swarm optimizer (RSO) with the bidirectional gated recurrent unit (BiGRU) is employed as a classifier to determine the existence of PD. The performance validation of the IFSO-DL model takes place using a benchmark Parkinson’s dataset, and the results are inspected under several dimensions. The experimental results highlighted the enhanced classification performance of the ISFO-DL technique, and therefore, the proposed model can be employed for the earlier identification of PD.

scholarly journals Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 298
Author(s):  
Jiwen Tang ◽  
Damien Arvor ◽  
Thomas Corpetti ◽  
Ping Tang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Image Classification ◽  
Water Consumption ◽  
Hough Transform ◽  
Classification Model ◽  
Irrigation Systems ◽  
Mato Grosso ◽  
Center Pivot ◽  
Sentinel 2

Irrigation systems play an important role in agriculture. Center pivot irrigation systems are popular in many countries as they are labor-saving and water consumption efficient. Monitoring the distribution of center pivot irrigation systems can provide important information for agricultural production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of center pivot irrigation systems is proposed. The proposed method combines a lightweight real-time object detection network (PVANET) based on deep learning, an image classification model (GoogLeNet) and accurate shape detection (Hough transform) to detect and accurately delineate center pivot irrigation systems and their associated circular shape. PVANET is lightweight and fast and GoogLeNet can reduce the false detections associated with PVANET, while Hough transform can accurately detect the shape of center pivot irrigation systems. Experiments with Sentinel-2 images in Mato Grosso achieved a precision of 95% and a recall of 95.5%, which demonstrated the effectiveness of the proposed method. Finally, with the accurate shape of center pivot irrigation systems detected, the area of irrigation in the region was estimated.

Deep Learning based Fruit Freshness Classification and Detection with CMOS Image sensors and Edge processors

2020 ◽  
Vol 2020 (12) ◽  
pp. 172-1-172-7 ◽  
Author(s):  
Tejaswini Ananthanarayana ◽  
Raymond Ptucha ◽  
Sean C. Kelly
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Image Classification ◽  
High Speed ◽  
Image Sensor ◽  
Vital Role ◽  
Image Sensors ◽  
Classification Model ◽  
Detection Model ◽  
Cmos Image Sensors

CMOS Image sensors play a vital role in the exponentially growing field of Artificial Intelligence (AI). Applications like image classification, object detection and tracking are just some of the many problems now solved with the help of AI, and specifically deep learning. In this work, we target image classification to discern between six categories of fruits — fresh/ rotten apples, fresh/ rotten oranges, fresh/ rotten bananas. Using images captured from high speed CMOS sensors along with lightweight CNN architectures, we show the results on various edge platforms. Specifically, we show results using ON Semiconductor’s global-shutter based, 12MP, 90 frame per second image sensor (XGS-12), and ON Semiconductor’s 13 MP AR1335 image sensor feeding into MobileNetV2, implemented on NVIDIA Jetson platforms. In addition to using the data captured with these sensors, we utilize an open-source fruits dataset to increase the number of training images. For image classification, we train our model on approximately 30,000 RGB images from the six categories of fruits. The model achieves an accuracy of 97% on edge platforms using ON Semiconductor’s 13 MP camera with AR1335 sensor. In addition to the image classification model, work is currently in progress to improve the accuracy of object detection using SSD and SSDLite with MobileNetV2 as the feature extractor. In this paper, we show preliminary results on the object detection model for the same six categories of fruits.

scholarly journals CmRAV1 shows differential expression in two melon (Cucumis melo L.) cultivars and enhances salt tolerance in transgenic Arabidopsis plants

2019 ◽  
Author(s):  
Lina Zhao ◽  
Furong Zhang ◽  
Bin Liu ◽  
Senlin Yang ◽  
Xue Xiong ◽  
...  
Keyword(s):  
Salt Tolerance ◽  
Cucumis Melo ◽  
Molecular Mechanisms ◽  
Transgenic Arabidopsis ◽  
Transcript Level ◽  
Soil Salinization ◽  
Saline Stress ◽  
Growth Stages ◽  
Plant Salt Tolerance ◽  
Cucumis Melo L

Abstract The growth and development of melon (Cucumis melo L.) are severely affected by soil salinization in many areas of the world, but the understanding of the molecular mechanisms underlying salt tolerance in melon remains limited. In this study, a new RAV (related to ABI3/VP1) gene, CmRAV1, was identified in melon. Protein structure homology analysis revealed that CmRAV1 contains an AP2 domain and a B3 domain, and subcellular localization assay revealed that CmRAV1 is localized in the nucleus. The transcript level of CmRAV1 was closely correlated with NaCl treatment, and the expression pattern of CmRAV1 differed between two cultivars (salt-tolerant and salt-sensitive cultivars) under NaCl treatment. In addition, yeasts transformed with CmRAV1 showed notably improved growth on medium containing 200 mM NaCl compared with wild-type ones. The overexpression of CmRAV1 in transgenic Arabidopsis thaliana resulted in enhanced salt tolerance at the seed germination and seedling growth stages. This study demonstrated that the expression of CmRAV1 was associated with saline stress and can potentially be utilized to improve plant salt tolerance.

scholarly journals Determination of the uptake and translocation of nitrogen applied at different growth stages of a melon crop (Cucumis melo L.) using 15N isotope

2011 ◽  
Vol 130 (3) ◽  
pp. 541-550 ◽  
Author(s):  
M.J. Cabello ◽  
M.T. Castellanos ◽  
A.M. Tarquis ◽  
M.C. Cartagena ◽  
A. Arce ◽  
...  
Keyword(s):  
Cucumis Melo ◽  
Growth Stages ◽  
15N Isotope ◽  
Cucumis Melo L ◽  
Different Growth Stages

scholarly journals Computer-Aided Diagnosis of Alzheimer’s Disease via Deep Learning Models and Radiomics Method

2021 ◽  
Vol 11 (17) ◽  
pp. 8104
Author(s):  
Yin Dai ◽  
Wenhe Bai ◽  
Zheng Tang ◽  
Zian Xu ◽  
Weibing Chen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Network Models ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Neural Network Models ◽  
Fusion Images ◽  
Alzheimer’S Disease Diagnosis ◽  
Single Modality

This paper focused on the problem of diagnosis of Alzheimer’s disease via the combination of deep learning and radiomics methods. We proposed a classification model for Alzheimer’s disease diagnosis based on improved convolution neural network models and image fusion method and compared it with existing network models. We collected 182 patients in the ADNI and PPMI database to classify Alzheimer’s disease, and reached 0.906 AUC in training with single modality images, and 0.941 AUC in training with fusion images. This proved the proposed method has better performance in the fusion images. The research may promote the application of multimodal images in the diagnosis of Alzheimer’s disease. Fusion images dataset based on multi-modality images has higher diagnosis accuracy than single modality images dataset. Deep learning methods and radiomics significantly improve the diagnosing accuracy of Alzheimer’s disease diagnosis.

scholarly journals Optimal Deep Learning based Classification Model for Mitral Valve Diagnosis System

2020 ◽  
Vol 9 (4) ◽  
pp. 315-321
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Mitral Valve ◽  
Edge Detection ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Validation Parameters ◽  
Effective Operation ◽  
Diagnosis Model

In present days, the domain of mitral valve (MV) diagnosis so common due to the changing lifestyle in day to day life. The increased number of MV disease necessitates the development of automated disease diagnosis model based on segmentation and classification. This paper makes use of deep learning (DL) model to develop a MV classification model to diagnose the severity level. For the accurate classification of ML, this paper applies the DL model called convolution neural network (CNN-MV) model. And, an edge detection based segmentation model is also applied which will helps to further enhance the performance of the classifier. Due to the non-availability of MV dataset, we have collected a MV dataset of our own from a total of 211 instances. A set of three validation parameters namely accuracy, sensitivity and specificity are applied to indicate the effective operation of the CNN-MV model. The obtained simulation outcome pointed out that the presented CNN-MV model functions as an appropriate tool for MV diagnosis

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