Root disease classification with hybrid optimization models in IoT

Jayapalan, Daniel Francis Selvaraj; Ananth, John Patrick

doi:10.1016/j.eswa.2023.120150

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…Furthermore, Ref. [226] employed hybrid optimization models to classify root diseases in IoT-based systems, analyzing correlation-related measures to understand the relationship between disease incidence and environmental factors [133].…”

Section: Correlation-related Measuresmentioning

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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Section: Correlation-related Measuresmentioning

confidence: 99%

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

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

View full text Add to dashboard Cite

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Lightweight Deep Learning

Kondaveeti,

Vatsavayi,

Mangapathi

et al. 2023

Advancement in Business Analytics Tools for Higher Financial Performance

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Lightweight deep learning is a subfield of artificial intelligence and machine learning that prioritises efficiency and compactness while developing deep learning models. It is ideal for low-powered mobile phones, embedded systems, and internet-of-things devices due to their speed and low latency. To make lightweight deep learning models, pruning and quantization are used to remove unnecessary parameters and reduce model weight accuracy. Transfer learning is used to fine-tune a pre-trained deep learning model on a smaller dataset. This chapter introduces the fundamentals of lightweight deep learning, including various lightweight models and their applications across different industries.

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Root disease classification with hybrid optimization models in IoT

Cited by 2 publications

References 35 publications

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

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

Lightweight Deep Learning

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