Overview of Recent Deep Learning Methods Applied in Fruit Counting for Yield Estimation

This paper is an overview of the latest advancements of image recognition for fruit counting and yield estimation. Considering this domain is developing rapidly, we have considered the cutting-edge literature in the field, for the last 5 years, focused on the task of yield estimation by detecting and counting fruit in the tree canopy. This is a much more complex task than the classification of fruit post-harvesting, which has been more widely reviewed. Moreover, we identify the major challenges and propose the next steps for advancing this research field.

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Multiclass Classification of Modulation Formats in the presence of Rayleigh and Rician Channel Noise using Deep Learning Methods

2020 3rd International Conference on Information and Communications Technology (ICOIACT) ◽

10.1109/icoiact50329.2020.9332035 ◽

2020 ◽

Author(s):

Rahim Khan ◽

Yang Qiang ◽

Ahsan Bin Tufail ◽

Alam Noor

Keyword(s):

Deep Learning ◽

Multiclass Classification ◽

Channel Noise ◽

Learning Methods ◽

Modulation Formats ◽

Rician Channel

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Classification of electrocardiogram (ECG) data using deep learning methods

2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) ◽

10.1109/ismsit50672.2020.9255000 ◽

2020 ◽

Author(s):

Fatma Bozyigit ◽

Fatih Erdemir ◽

Murat Sahin ◽

Deniz Kilinc

Keyword(s):

Deep Learning ◽

Learning Methods ◽

Electrocardiogram Ecg ◽

Ecg Data

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A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽

10.3390/electronics10010081 ◽

2021 ◽

Vol 10 (1) ◽

pp. 81

Author(s):

Jianbin Xiong ◽

Dezheng Yu ◽

Shuangyin Liu ◽

Lei Shu ◽

Xiaochan Wang ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Plant Species ◽

Image Recognition ◽

Recurrent Neural Network ◽

Plant Diseases ◽

Learning Methods ◽

Smart Agriculture ◽

Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

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Classification of audio codecs with variable bit-rates using deep-learning methods

Digital Signal Processing ◽

10.1016/j.dsp.2020.102952 ◽

2020 ◽

pp. 102952

Author(s):

Atieh Khodadadi ◽

Soheila Molaei ◽

Mehdi Teimouri ◽

Hadi Zare

Keyword(s):

Deep Learning ◽

Learning Methods

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Classification of Ground Targets Based on Radar Micro-Doppler Signatures Using Deep Learning and Conventional Supervised Learning Methods

Radioengineering ◽

10.13164/re.2018.0835 ◽

2018 ◽

Vol 27 (3) ◽

pp. 835-845

Author(s):

Peibei Cao ◽

Weijie Xia ◽

Yi Li

Keyword(s):

Deep Learning ◽

Supervised Learning ◽

Learning Methods

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Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities

10.20944/preprints202102.0349.v3 ◽

2021 ◽

Author(s):

Pranjal Kumar

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Well Being ◽

Human Activity Recognition ◽

Research Field ◽

Daily Lives ◽

Video Cameras ◽

Advantages And Disadvantages

Human Activity Recognition (HAR) has become a vibrant research field over the last decade, especially because of the spread of electronic devices like mobile phones, smart cell phones, and video cameras in our daily lives. In addition, the progress of deep learning and other algorithms has made it possible for researchers to use HAR in many fields including sports, health, and well-being. HAR is, for example, one of the most promising resources for helping older people with the support of their cognitive and physical function through day-to-day activities. This study focuses on the key role machine learning plays in the development of HAR applications. While numerous HAR surveys and review articles have previously been carried out, the main/overall HAR issue was not taken into account, and these studies concentrate only on specific HAR topics. A detailed review paper covering major HAR topics is therefore essential. This study analyses the most up-to-date studies on HAR in recent years and provides a classification of HAR methodology and demonstrates advantages and disadvantages for each group of methods. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

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