Control and Estimation of Renewable Energy

Ožana, Štěpán; Kengar, Nayan Mahadev; Prakash, Praveen

doi:10.1007/978-981-19-8963-6_32

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…The research in smart microgrids [24][25][26] and sustainable energy generation [27][28][29][30] emphasizes the significance of such filters in bettering the system reliability and efficiency. Their use also spills over to condition monitoring in hybrid electric vehicles [31][32][33][34], robotic system control [35][36][37][38][39][40][41][42][43][44], and renewable energy optimization [45][46][47][48][49][50][51][52][53][54][55][56], thus, an indication of their versatility and real-time control and decision-making capability.…”

Section: Introductionmentioning

confidence: 99%

Advanced crop monitoring: incorporating the Kalman filter into modern agriculture

Obaideen,

Faroukh,

Bonny

et al. 2024

Sensing for Agriculture and Food Quality and Safety XVI

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This paper investigates the integration of Kalman Filter technologies into modern agricultural practices, with a focus on advancing crop monitoring techniques. Through a comprehensive bibliometric and text mining analysis, we explore the evolution of research in this area from 1983 to 2023, highlighting the steady growth in scholarly publications and the interdisciplinary nature of the field. Our findings reveal a significant interest in leveraging Kalman Filter algorithms to enhance the precision and efficiency of agricultural operations, addressing the challenges of sustainability and food security. The study underscores the importance of data assimilation, real-time monitoring, and predictive analysis in agriculture, facilitated by the adoption of Kalman Filter and its variants. We also examine the integration of emerging technologies, such as unmanned aerial vehicles (UAVs) and remote sensing, with Kalman Filter techniques to develop sophisticated agricultural monitoring systems. The paper concludes with insights into future research directions, emphasizing the need for overcoming barriers to technology adoption and fostering interdisciplinary collaborations. This work contributes to the understanding of how computational intelligence can transform agricultural practices, offering solutions for more sustainable and efficient farming.

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