Certain investigation of precision agriculture robot using lab view

Mohan, Santhakumar; Kumar, E. Praveen; Paulchamy, B.

doi:10.1109/icctet.2013.6675975

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Former president APJ Abdul Kalam believed that empowering women was essential to nation-building. Empowering women is vital because their values and viewpoints help build great families, wealthy communities, and flourishing nations (Mohan, 2013). The empowerment of women in the economy drives sustained economic development.…”

Section: Review Of Literaturementioning

confidence: 99%

Empowering Women Entrepreneurs: Catalysts for Economic Growth

Bhuvaneswari

2024

management

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The involvement of women in business is essential for empowering women and driving economic advancement. This abstract provides a concise overview of a seminar paper that explores the concept of women entrepreneurship, its impact on women’s empowerment, and its role in promoting economic growth. This article examines the challenges faced by female entrepreneurs, the benefits of promoting women’s entrepreneurship, and the strategies for creating a supporting environment for women-owned businesses. Furthermore, this resource presents successful case studies and example techniques from other nations to demonstrate the positive outcomes of supporting women entrepreneurs. To summarise, the abstract emphasises the importance of women’s entrepreneurship in empowering women and contributing to sustainable economic development.

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Section: Review Of Literaturementioning

confidence: 99%

Empowering Women Entrepreneurs: Catalysts for Economic Growth

Bhuvaneswari

2024

management

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“…Nowadays, we are using sensors, smart devices, and satellite imagery to collect data related to agricultural practices. This data is mostly generated from IoT devices, "smart" machines, and intelligent robots or drones [40][41][42]. This DGM mainly comes in streaming, micro-batches, and small-batches of data that can be collected and processed in real-time.…”

Section: Data Generated By Machine (Dgm)mentioning

confidence: 99%

Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge

2022

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Smart agriculture today uses a wide range of wireless communication technologies. Low Power Consumption Embedded Devices (LPCED), such as the Internet of Things (IoT) and Wireless Sensor Networks, make it possible to work over great distances at a reduced cost but with limited transferable data volumes. However, data management (DM) in intelligent agriculture is still not well understood due to the fact that there are not enough scientific publications available on this. Though data management (DM) benefits are factual and substantial, many challenges must be addressed in order to fully realize the DM’s potential. The main difficulties are data integration complexities, the lack of skilled personnel and sufficient resources, inadequate infrastructure, and insignificant data warehouse architecture. This work proposes a comprehensive architecture that includes big data technologies, IoT components, and knowledge-based systems. We proposed an AI-based architecture for smart farming. This architecture called, Smart Farming Oriented Big-Data Architecture (SFOBA), is designed to guarantee the system’s durability and the data modeling in order to transform the business needs for smart farming into analytics. Furthermore, the proposed solution is built on a pre-defined big data architecture that includes an abstraction layer of the data lake that handles data quality, following a data migration strategy in order to ensure the data’s insights.

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“…Facing the demand for efficient production in modern agriculture, intelligent robots have also attracted many attentions of agricultural researchers. As a new concept of agricultural machinery [7][8], agricultural robots have huge economic benefits in the field of agricultural production, and have broad market prospects. The timely development and development of a new generation for agricultural machinery represented by agricultural robots is of great significance for my country's transition to modern agriculture.…”

Section: Introductionmentioning

confidence: 99%

A Robot Vision Navigation Method using deep Learning in Edge Computing Environment

Li¹,

Yin²,

Lin³

2021

Preprint

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In the development of modern agriculture, the intelligent use of mechanical equipment is one of the main signs for agricultural modernization. Navigation technology is the key technology for agricultural machinery to control autonomously in operating environment, and it is a hotspot in the field of intelligent research on agricultural machinery. Facing the accuracy requirements of autonomous navigation for intelligent agricultural robots, this paper proposes a visual navigation algorithm for agricultural robots based on deep learning image understanding. The method first uses cascaded deep convolutional network and hybrid dilated convolution fusion method to process images collected by vision system. Then it extracts the route of processed images based on improved Hough transform algorithm. At the same time, the posture of agricultural robots is adjusted to realize autonomous navigation. Finally, our proposed method is verified by using non-interference experimental scenes and noisy experimental scenes. Experimental results show that the method can perform autonomous navigation in complex and noisy environments, and has good practicability and applicability.

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Certain investigation of precision agriculture robot using lab view

Cited by 6 publications

References 8 publications

Empowering Women Entrepreneurs: Catalysts for Economic Growth

Empowering Women Entrepreneurs: Catalysts for Economic Growth

Data Management and Integration of Low Power Consumption Embedded Devices IoT for Transforming Smart Agriculture into Actionable Knowledge

A Robot Vision Navigation Method using deep Learning in Edge Computing Environment

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