Banana Irrigation System and Scheduling based on Reinforcement Learning

Blessy, Angelin; Kumar, Amit; Johri, Prashant

doi:10.14445/22315381/ijett-v70i8p240

Cited by 1 publication

(1 citation statement)

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“…Deficient AI/ML models may inadvertently extend computation times, as expounded in [78,219]. Prolonged computations persist until yielding satisfactory outcomes.…”

Section: Impact Of Ai/ml Weaknesses On Iotmentioning

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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“…Deficient AI/ML models may inadvertently extend computation times, as expounded in [78,219]. Prolonged computations persist until yielding satisfactory outcomes.…”

Section: Impact Of Ai/ml Weaknesses On Iotmentioning

confidence: 99%