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A Deep Learning-Driven Computer Vision and Web-Based Monitoring System for Automated Nutrient Management in Soilless Agriculture

Eda Nur Urtekin, Emre Dandıl • Journal of Agricultural Sciences (2026)

Sustainable food production increasingly relies on intelligent automation technologies that optimize resource usage while maintaining crop health. This study presents a deep learning-driven computer vision and web based monitoring system de...

DOAJ Open Access (DOAJ) DOI: 10.15832/ankutbd.1817471

Automated harvesting by a dual-arm fruit harvesting robot

Takeshi Yoshida, Yuki Onishi, Takuya Kawahara, Takanori Fukao • ROBOMECH Journal (2022)

Abstract In this study, we propose a method to automate fruit harvesting with a fruit harvesting robot equipped with robotic arms. Given the future growth of the world population, food shortages are expected to accelerate. Since much of Jap...

DOAJ Open Access (DOAJ) DOI: 10.1186/s40648-022-00233-9

Computer vision technology in agricultural automation —A review

Hongkun Tian, Tianhai Wang, Yadong Liu, Xi Qiao et al. • Information Processing in Agriculture (2019)

Computer vision is a field that involves making a machine “see”. This technology uses a camera and computer instead of the human eye to identify, track and measure targets for further image processing. With the development of computer visio...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.inpa.2019.09.006

Addressing practical challenge of using autopilot drone for asphalt surface monitoring: Road detection, segmentation, and following

Hormazd Ranjbar, Perry Forsythe, Alireza Ahmadian Fard Fini, Mojtaba Maghrebi et al. • Results in Engineering (2023)

According to the recent world bank report, around 80% of a life-cycle cost of a road is devoted to maintenance which includes monitoring and repair processes. To more effectively keep road serviceability, knowing the current status of road ...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.rineng.2023.101130

Survey on categorical data for neural networks

John Hancock, Taghi M. Khoshgoftaar • Journal Of Big Data (2020)

Abstract This survey investigates current techniques for representing qualitative data for use as input to neural networks. Techniques for using qualitative data in neural networks are well known. However, researchers continue to discover n...

OPENALEX cc-by DOI: https://doi.org/10.1186/s40537-020-00305-w

Incorporating structural plasticity into self-organization recurrent networks for sequence learning

Ye Yuan, Yongtong Zhu, Jiaqi Wang, Ruoshi Li et al. • Frontiers in Neuroscience (2023)

IntroductionSpiking neural networks (SNNs), inspired by biological neural networks, have received a surge of interest due to its temporal encoding. Biological neural networks are driven by multiple plasticities, including spike timing-depen...

DOAJ Open Access (DOAJ) DOI: 10.3389/fnins.2023.1224752

Face recognition in dense crowd using deep learning approaches with IP camera

Sobhana Mummaneni, Venkata Chaitanya Satya Ramaraju Mudunuri, Sri Veerabhadra Vikas Bommaganti, Bhavya Vani Kalle et al. • Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska (2025)

A facial recognition system is a biometric security and surveillance system that can identify and monitor individuals in a crowded area. Manually monitoring a crowded environment is a difficult and error-prone task. Therefore, in such conte...

DOAJ Open Access (DOAJ) DOI: 10.35784/iapgos.6818

Deep Learning-Based Prediction of Wind Power for Multi-turbines in a Wind Farm

Xiaojiao Chen, Xiuqing Zhang, Mi Dong, Liansheng Huang et al. • Frontiers in Energy Research (2021)

The prediction of wind power plays an indispensable role in maintaining the stability of the entire power grid. In this paper, a deep learning approach is proposed for the power prediction of multiple wind turbines. Starting from the time s...

DOAJ Open Access (DOAJ) DOI: 10.3389/fenrg.2021.723775

Deep learning as a tool for ecology and evolution

Marek L. Borowiec, Rebecca B. Dikow, Paul B. Frandsen, Alexander J. McKeeken et al. • Methods in Ecology and Evolution (2022)

Abstract Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been use...

OPENALEX cc-by-nc DOI: https://doi.org/10.1111/2041-210x.13901

On Definition of Deep Learning

Wenjun Zhang, Guosheng Yang, Yingzi Lin, Chunli Ji et al. • World Automation Congress (WAC) (2018)

Deep learning has become a buzz word recently. However, there is a lack of unified definition to deep learning in literature. The goal of this paper is to overview various definitions to deep learning and to show their limitations. Finally,...

OPENALEX DOI: https://doi.org/10.23919/wac.2018.8430387

A Review of Machine Learning and Deep Learning Applications

Pramila P. Shinde, Seema Shah • International Conference on Computing Communication Control and automation (2018)

Machine learning is one of the fields in the modern computing world. A plenty of research has been undertaken to make machines intelligent. Learning is a natural human behavior which has been made an essential aspect of the machines as well...

OPENALEX DOI: https://doi.org/10.1109/iccubea.2018.8697857

Overview of deep learning

Xuedan Du, Yinghao Cai, Shuo Wang, Leijie Zhang • Youth Academic Annual Conference of Chinese Association of Automation (YAC) (2016)

In recent years, deep learning has achieved great success in many fields, such as computer vision and natural language processing. Compared to traditional machine learning methods, deep learning has a strong learning ability and can make be...

OPENALEX DOI: https://doi.org/10.1109/yac.2016.7804882

Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding

Shaorong Zhang, Shaorong Zhang, Qihui Wang, Benxin Zhang et al. • Frontiers in Neuroscience (2023)

IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses signi...

DOAJ Open Access (DOAJ) DOI: 10.3389/fnins.2023.1292724