Mishra Shailendra
Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
Heliyon. 2023 Jul 26;9(8):e18659. doi: 10.1016/j.heliyon.2023.e18659. eCollection 2023 Aug.
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.
智能畜牧业致力于通过使用数字技术，使养殖更具盈利性、高效性和生态效益。精准牲畜围栏技术能对每只动物进行独立跟踪和研究，是最具前景的智能畜牧业类型。物联网使农民能够通过监测作物、绘制土地地图并提供数据，制定合理的农场管理策略，从而节省金钱和精力。监视、灾害管理、消防、边境巡逻和快递服务使用的无人机最初是为军事用途而研发的。该部分聚焦于无人机在畜牧业和农业生产中的应用。这是通过将机器人、无人机、远程传感器和计算机图像与不断改进的深度学习技术结合用于农业来实现的。深度学习算法在农业领域有诸多用途，从识别植物病害、估算产量、检测杂草到预测天气以及测定土壤含水量等。智能畜牧业面临的挑战包括气候变化、生物多样性丧失和持续监测。因此，在本研究中，设计了无人机赋能的综合农场管理系统（UAV - IFM）以改善智能畜牧业。这种传感器能够实现从农场到餐桌对牲畜的安全可靠跟踪，这对于检测和控制疾病爆发、防止由此导致的经济损失以及与食品相关的健康大流行具有深远意义。UAV - IFM旨在改进评估过程，以便智能畜牧业能够得到更广泛的采用，并为农民提供促进发展的帮助。从本研究对UAV - IFM的考察中得出的结论表明，这些工具在评估程序框架内能够正确预测和验证智能畜牧业管理。UAV - IFM的实验分析在效率比、性能、准确性和预测方面均优于智能畜牧业。
