Automatic Detection of Water Stress in Corn Using Image Processing and Deep Learning

2021 ◽  
pp. 104-113
Author(s):  
Mor Soffer ◽  
Ofer Hadar ◽  
Naftali Lazarovitch
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Water Stress ◽  
Automatic Detection

Automatic Detection of Cowpea leaves Using Image Processing and Inception-V3 Model of Deep Learning

2021 ◽  
Author(s):  
Vijaya Choudhary ◽  
Paramita Guha ◽  
Kuldeep Tripathi ◽  
Sunita Mishra
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Automatic Detection

Real-Time Detection of Water Stress in Corn Using Image Processing and Deep Learning

2020 ◽  
Author(s):  
Mor Soffer ◽  
Naftali Lazarovitch ◽  
Ofer Hadar
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Water Stress ◽  
Crop Production ◽  
Irrigation Treatment ◽  
Agricultural Crop ◽  
Learning Techniques ◽  
Web Camera ◽  
Use Efficiency ◽  
Non Destructive

<p>Water limitation is one of the main environmental constraints that adversely affects agricultural crop production around the world. Precise and rapid detection of plant water stress is critical for increasing agricultural productivity and water use efficiency. Numerous studies conducted over the years have attempted to find effective ways to correctly recognize situations of water stress in order to determine irrigation regimes.</p><p>Water stress detection is currently done by various methods that are not ideal; these methods are often very expensive, destructive and cumbersome. Water stress in plants is also expressed at different visual levels. Image processing is alternative way to visually recognize water stress levels. Such analysis is non-destructive, inexpensive and allows to examine the spatial variability of stress level under field conditions.</p><p>In our study, we propose a new method for detecting water stress in corn using image processing and deep learning. For the purpose of collecting the images, we performed a three-months experiment, in which we took images of five different groups of corn. Each group had a different irrigation treatment, which led to five different levels of water stress. The images were collected using a web camera located approximately 2 m from the plants.</p><p>Stress classification was done by inserting processed images into a Convolutional Neural Network (CNN). Training the network was done using transfer-learning techniques in order to exploit the performance of an already trained CNN, for a fast and efficient training over the dataset. Testing the quality of classification was done using extra camera which took a different set of images.</p><p>Results were tested upon two sub-experiments - classification of three types of treatments and classification of five types of treatments; the results were 98% accuracy in classification into three types of treatments (well-watered, reduced-watered and draught stressed treatment), and 85% accuracy in classification into five different treatments. These initial results are definitely excellent and can certainly serve decision making for optimal irrigation. <strong> </strong></p>

Deep Learning-Based Image Processing for Whitecaps on the Ocean Surface

2020 ◽  
Vol 76 (2) ◽  
pp. I_163-I_168
Author(s):  
Yukun WANG ◽  
Yuji SUGIHARA ◽  
Xianting ZHAO ◽  
Haruki NAKASHIMA ◽  
Osama ELJAMAL
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Ocean Surface

scholarly journals Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Channel Model ◽  
Expert Knowledge ◽  
Automatic Detection ◽  
Data Representation ◽  
Learning Technology ◽  
Arrhythmia Detection ◽  
Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

Automatic detection of papilledema through fundus retinal images using deep learning

2021 ◽  
Author(s):  
Tanzila Saba ◽  
Shahzad Akbar ◽  
Hoshang Kolivand ◽  
Saeed Ali Bahaj
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Retinal Images

scholarly journals Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R

2021 ◽  
Vol 273 ◽  
pp. 116490
Author(s):  
Odei Garcia-Garin ◽  
Toni Monleón-Getino ◽  
Pere López-Brosa ◽  
Asunción Borrell ◽  
Alex Aguilar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Automatic Detection ◽  
Aerial Images ◽  
Learning Approach ◽  
Detection And Quantification

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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