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• W4385298908 abstract "Smart livestock farming strives to make farming more lucrative, efficient, and ecologically beneficial by using digital technologies. Precision livestock fencing, in which each animal is followed and studied independently, is the most promising kind of smart livestock farming. The Internet of Things (IoT) allows farmers to save money and effort by keeping tabs on crops, mapping out their land, and giving them data to develop sensible management strategies for their farms. Surveillance, disaster management, firefighting, border patrol, and courier services employ Unmanned Aerial Vehicles (UAVs) that are originally created for the military. The segment focuses on UAVs in livestock and agricultural production. This is achieved via employing robots, drones, remote sensors, and computer imagery in unison with ever-improving in-Depth Learning for farming. Deep learning (DL) algorithms find many uses in the agricultural sector, from identifying plant diseases to estimating yields to detecting weeds to forecasting the weather and determining how much water is in the soil. The challenging characteristics of smart livestock farming are climate change, biodiversity loss, and continuous monitoring. Hence, in this research, the Unmanned Aerial Vehicles enabled Integrated Farm Management (UAV-IFM) has been designed to improve smart livestock farming. Safe and reliable tracking of livestock from farm to fork is made possible by this sensor, which has far-reaching implications for detecting and containing disease outbreaks and preventing the resulting financial losses and food-related health pandemics. UAV-IFM aims to improve the assessment process so that smart livestock farming may be more widely adopted and offers growth-supportive help to farmers. Conclusions gathered from this study's examination of the UAV-IFM reveal that these instruments correctly forecast and verify smart livestock farming management within the framework of the assessment procedure. The experimental analysis of UAV-IFM outperforms smart livestock farming in terms of efficiency ratio, performance, accuracy, and prediction." @default.
• W4385298908 created "2023-07-28" @default.
• W4385298908 creator A5027723382 @default.
• W4385298908 date "2023-08-01" @default.
• W4385298908 modified "2023-10-14" @default.
• W4385298908 title "Internet of things enabled deep learning methods using unmanned aerial vehicles enabled integrated farm management" @default.
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• W4385298908 doi "https://doi.org/10.1016/j.heliyon.2023.e18659" @default.
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