Implementation of Artificial Intelligence, Machine Learning, and Internet of Things (IoT) in revolutionizing Agriculture: A review on recent trends and challenges

Dawn, Nabarun; Ghosh, Tania; Ghosh, Souptik; Saha, Aloke; Mukherjee, Pronoy; Sarkar, Subhajit; Guha, Sagnik; Sanyal, Tanmay

doi:10.52756/ijerr.2023.v30.018

Cited by 18 publications

(5 citation statements)

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“…The study also highlights the need for technical expertise for the repair and maintenance of these systems. Dawn et al (2023) delve into the implementation of AI, Machine Learning, and IoT in revolutionizing agriculture. The paper discusses how AI can fulfill the needs of a growing world population through automation.…”

Section: Evolution and Future Trends In Ai-based Predictive Analytics...mentioning

confidence: 99%

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Oluwafunmi Adijat Elufioye,

Chinedu Ugochukwu Ike,

Olubusola Odeyemi

et al. 2024

Comput. sci. IT res. j.

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This study provides a comprehensive review of the integration and impact of Artificial Intelligence (AI) in agricultural supply chains, focusing on its role in enhancing demand forecasting and optimizing supply. The primary objective was to assess how AI-driven predictive analytics transforms agricultural practices, addressing challenges, and shaping future trends. A systematic literature review and content analysis methodology were employed, utilizing academic databases and digital libraries to source peer-reviewed articles and conference papers published between 2014 and 2024. The inclusion criteria focused on studies related to AI applications in agricultural supply chains, while exclusion criteria filtered out non-peer-reviewed and irrelevant literature. Key findings reveal that AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture. AI technologies, including machine learning and big data analytics, have led to advancements in real-time data analysis, predictive maintenance, and resource optimization. However, challenges such as data quality, infrastructure development, and skill gaps among agricultural professionals persist. The future landscape of AI in agriculture is marked by growth opportunities and challenges, including the need for equitable AI technology access and ethical considerations. The study recommends that industry leaders and policymakers invest in infrastructure, promote AI research and development, and provide training to facilitate AI adoption. Future research should focus on developing robust AI models tailored to agriculture, exploring AI's integration with emerging technologies, and assessing AI's long-term socio-economic impacts. This study contributes to understanding AI's current applications and future potential in transforming agricultural supply chains, offering valuable insights for stakeholders in the agricultural sector. Keywords: Artificial Intelligence, Agricultural Supply Chains, Predictive Analytics, Demand Forecasting.

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Section: Evolution and Future Trends In Ai-based Predictive Analytics...mentioning

confidence: 99%

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Oluwafunmi Adijat Elufioye,

Chinedu Ugochukwu Ike,

Olubusola Odeyemi

et al. 2024

Comput. sci. IT res. j.

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“…Ongoing research initiatives aim to leverage emerging technologies such as the Internet of Things (IoT) to collect and manage data from agricultural facilities [5,[8][9][10][11][12][13]. The integration of IoT with Artificial Intelligence (AI) for decision-making [14], remote sensing for observation [15], and blockchain technology for data security [16] further enhances agricultural processes, including disease identification, pest control, soil monitoring [17,18], and yield prediction [19,20].…”

Section: Introductionmentioning

confidence: 99%

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

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The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

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“…These aerial vehicles, equipped with state-ofthe-art sensors, cameras, and data analytics capabilities, enable farmers to manage their fields and crops in entirely new ways. By employing UAV technology to deliver precise, real-time insights into crop health, soil conditions, and overall farm management, smart farming has advanced beyond conventional methods (Akkem et al, 2023;Dawn et al, 2023;Lachgar et al, 2023). Farmers now have access to valuable data that they can use to increase crop yields, make educated decisions, and promote sustainable agricultural practises thanks to the ability of these unmanned devices to collect highresolution data across vast agricultural landscapes .…”

Section: Introductionmentioning

confidence: 99%

Smart Farming with Sooty Tern Optimization based LS-HGNet Classification Model

Krishnan,

Vikranth,

Sumithra

et al. 2024

IJERR

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Smart farming technologies enable farmers to use resources like water, fertilizer and pesticides as efficiently as possible. This paper discusses how Unmanned Aerial Vehicle (UAV) pictures can be used to automatically detect and count tassels, thereby advancing the advancement of strategic maize planting. The real state of affairs in cornfields is complicated, though, and the current algorithms struggle to provide the speed and accuracy required for real-time detection. This research employed a sizable, excellent dataset of maize tassels to solve this problem. This paper suggests using the bottom-hat-top-hat preprocessing technique to address the lighting irregularities and noise in maize photos taken by drones. The Lightweight weight-stacked hourglass Network (LS-HGNet) model is suggested for classification. The hourglass network structure of LS-HGNet, which is mostly utilised as a backbone network, has allowed significant advancements in the discovery of maize tassels. In light of this, the current work suggests a lighter variant of the hourglass network that also enhances the accuracy of tassel detection in maize plants. The additional skip connections used in the new hourglass network architecture allow minimal changes to the number of network parameters while improving performance. Consequently, the suggested LS-HGNet classifier lowers the computational burden and increases the convolutional receptive field. The hyperparameter tuning process is then carried out using the Sooty Tern Optimisation Algorithm (STOA), which helps increase tassel detection accuracy. Numerous tests were conducted to verify that the suggested approach is more accurate at 98.7% and more efficient than the most advanced techniques currently in use.

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Implementation of Artificial Intelligence, Machine Learning, and Internet of Things (IoT) in revolutionizing Agriculture: A review on recent trends and challenges

Cited by 18 publications

References 0 publications

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Smart Farming with Sooty Tern Optimization based LS-HGNet Classification Model

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