An Internet of Things-based Efficient Solution for Smart Farming

Mathi, Senthilkumar; Akshaya, R; Sreejith, K

doi:10.1016/j.procs.2023.01.252

Cited by 16 publications

(13 citation statements)

References 15 publications

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“…In terms of crop production, Ref. [103] used random forest to classify rice growth stages, while [121] utilized classification models, namely SVM and KNN, to predict plant diseases. Both models show good results for each task.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…This suggests a move towards comprehensive, data-driven decision-making in agriculture. IoT-enabled smart irrigation systems [120][121][122] are another significant trend in crop production. These systems utilize sensors to monitor soil moisture levels and climate conditions, enabling precise and automated irrigation.…”

Section: Crop Productionmentioning

confidence: 99%

“…The limited number of studies categorized under general agriculture [121,140] suggests a need for more research that transcends specific forms of agriculture. Further research could explore cross-disciplinary approaches that integrate multiple forms of agriculture, addressing overarching challenges in the agriculture sector.…”

Section: Other Forms Of Agriculturementioning

confidence: 99%

“…Effective disease detection and management strategies are crucial for mitigating crop losses and ensuring food security [160]. Advances in AI-powered IoT devices and predictive analytics have shown promise in early disease detection and prevention [121,147,161]. However, many smallholder farmers lack access to affordable and reliable disease monitoring tools and extension services, hindering timely disease detection and response [154].…”

Section: Crop Disease Detection and Managementmentioning

confidence: 99%

“…The integration of IoT technologies in agriculture presents transformative opportunities [213,223,237]. Authors [121,126,216] advocate for the use of low-power sensors, aerial images from drones, and advanced clustering algorithms to enrich data collection, improve system scalability, and enhance decision-making accuracy. The versatility of IoT applications is demonstrated by its deployment in diverse agricultural stages [76] and various forms of agriculture, such as crop production [104,109,147,182], fish farming [108], aquaculture [89,159,239], and beekeeping [139,149].…”

Section: Iot Opportunitiesmentioning

confidence: 99%

See 4 more Smart Citations

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: Machine Learning Methodsmentioning

confidence: 99%

Section: Crop Productionmentioning

confidence: 99%

Section: Other Forms Of Agriculturementioning

confidence: 99%