AI-Based Yield Prediction and Smart Irrigation

Ramdinthara, Immanuel Zion; Bala, P. Shanthi; Gowri, A. S.

doi:10.1007/978-981-16-6210-2_6

Cited by 3 publications

(5 citation statements)

References 43 publications

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“…In the grape preproduction stage, farmers utilize ML algorithms to predict crop yield, assess soil conditions, and optimize irrigation strategies [55]. This predictive capability aids in disease identification and weather anticipation, which is crucial for enhancing agricultural productivity [56].…”

Section: Ai In the Wine Supply Chainmentioning

confidence: 99%

“…The quality of the wine is monitored and managed at every step of production using AI-enabled technologies by analyzing data from IoT devices [55,126,127].…”

Section: Aimentioning

confidence: 99%

“…AI is used to predict the quality of a wine before it is bottled, allowing winemakers to make adjustments and reduce waste [55,124].…”

mentioning

confidence: 99%

“…To increase production and profitability, AI is used to analyze data from IoT devices in the wine supply chain in order to anticipate future yields and identify the most efficient use of existing resources [55,124].…”

mentioning

confidence: 99%

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The Integration of the Internet of Things, Artificial Intelligence, and Blockchain Technology for Advancing the Wine Supply Chain

Adamashvili,

Zhizhilashvili,

Tricase

2024

Computers

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The study presents a comprehensive examination of the recent advancements in the field of wine production using the Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain Technology (BCT). The paper aims to provide insights into the implementation of these technologies in the wine supply chain and to identify the potential benefits associated with their use. The study highlights the various applications of IoT, AI, and BCT in wine production, including vineyard management, wine quality control, and supply chain management. It also discusses the potential benefits of these technologies, such as improved efficiency, increased transparency, and reduced costs. The study concludes by presenting the framework proposed by the authors in order to overcome the challenges associated with the implementation of these technologies in the wine supply chain and suggests areas for future research. The proposed framework meets the challenges of lack of transparency, lack of ecosystem management in the wine industry and irresponsible spending associated with the lack of monitoring and prediction tools. Overall, the study provides valuable insights into the potential of IoT, AI, and BCT in optimizing the wine supply chain and offers a comprehensive review of the existing literature on the study subject.

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Section: Ai In the Wine Supply Chainmentioning

confidence: 99%

“…The quality of the wine is monitored and managed at every step of production using AI-enabled technologies by analyzing data from IoT devices [55,126,127].…”

Section: Aimentioning

confidence: 99%

“…AI is used to predict the quality of a wine before it is bottled, allowing winemakers to make adjustments and reduce waste [55,124].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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The Integration of the Internet of Things, Artificial Intelligence, and Blockchain Technology for Advancing the Wine Supply Chain

Adamashvili,

Zhizhilashvili,

Tricase

2024

Computers

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“…This technology allows for the collection and analysis of agricultural information through the Internet of Things, enabling farmers to make more precise decisions and improve crop health [6]. By utilizing technologies such as big data, machine learning, robotics, and IoT, the quality and quantity of agricultural production can be improved [7]. The implementation of IoT-based agriculture, also known as Smart Farming, has the potential to increase productivity and meet the everincreasing demands of the growing population [8].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Distributed Database Management System for IoT in the Context of Smart Agriculture in Indonesia

Judijanto

2023

WSNT

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The integration of Distributed Database Management Systems (DDBMS) for Internet of Things (IoT) applications in smart agriculture presents a transformative paradigm in the dynamic landscape of Indonesian agriculture. This qualitative research investigates the challenges and opportunities associated with this integration, drawing insights from interviews with diverse stakeholders, including farmers, technology developers, and policymakers. The technical challenges encompass data synchronization, network latency, and security considerations, emphasizing the need for standardized protocols and robust security measures. Infrastructure challenges, such as connectivity issues and power supply dependency, underscore the importance of rural infrastructure development. User acceptance challenges highlight resistance to change and the perceived complexity of DDBMS, emphasizing the significance of targeted awareness and training programs. Despite these challenges, stakeholders recognize the advantages of scalability, enhanced data availability, and improved decision-making capabilities offered by DDBMS. The study proposes recommendations, including technological solutions, policy interventions, and capacity-building initiatives, to address identified challenges and promote the effective utilization of DDBMS in Indonesian smart agriculture. This research contributes context-specific insights to the global discourse on IoT and DDBMS in agriculture.

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Artificial Intelligence, Internet of Things, and Machine-Learning

Akensous,

Sbbar,

Ech-chatir

et al. 2023

Advances in Environmental Engineering and Green Technologies

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Water scarcity has been escalating both in terms of frequency and severity, owing to climate change and global warming. Furthermore, water is a vital source that is at the core of crucial sectors like agriculture. Yet, this source is labeled scarce, and its distribution is uneven globally. For the aforementioned reasons, achieving a rational use of water is of utmost importance. In this framework, computational intelligence like artificial intelligence (AI), the internet of things (IoT), and machine-learning, has been gaining momentum for implementing smart irrigation and precision agriculture. Thus, the chapter surveys a selection of recent studies, corroborating AI, IoT, and machine-learning as promising approaches to advance agriculture. The chapter also sheds light on the notion of virtual water, proposes strategies to deal with water scarcity, and highlights the essential components to achieve effective smart irrigation, thereby switching toward an innovative model of sustainable digital agriculture.

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AI-Based Yield Prediction and Smart Irrigation

Cited by 3 publications

References 43 publications

The Integration of the Internet of Things, Artificial Intelligence, and Blockchain Technology for Advancing the Wine Supply Chain

The Integration of the Internet of Things, Artificial Intelligence, and Blockchain Technology for Advancing the Wine Supply Chain

Performance Analysis of Distributed Database Management System for IoT in the Context of Smart Agriculture in Indonesia

Artificial Intelligence, Internet of Things, and Machine-Learning

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