Environmental Modelling & Software, Год журнала: 2023, Номер 162, С. 105634 - 105634
Опубликована: Янв. 26, 2023
Язык: Английский
Sensors, Год журнала: 2021, Номер 21(11), С. 3758 - 3758
Опубликована: Май 28, 2021
The digital transformation of agriculture has evolved various aspects management into artificial intelligent systems for the sake making value from ever-increasing data originated numerous sources. A subset intelligence, namely machine learning, a considerable potential to handle challenges in establishment knowledge-based farming systems. present study aims at shedding light on learning by thoroughly reviewing recent scholarly literature based keywords’ combinations “machine learning” along with “crop management”, “water “soil and “livestock accordance PRISMA guidelines. Only journal papers were considered eligible that published within 2018–2020. results indicated this topic pertains different disciplines favour convergence research international level. Furthermore, crop was observed be centre attention. plethora algorithms used, those belonging Artificial Neural Networks being more efficient. In addition, maize wheat as well cattle sheep most investigated crops animals, respectively. Finally, variety sensors, attached satellites unmanned ground aerial vehicles, have been utilized means getting reliable input analyses. It is anticipated will constitute beneficial guide all stakeholders towards enhancing awareness advantages using contributing systematic topic.
Язык: Английский
Процитировано
526
Agriculture, Год журнала: 2023, Номер 13(8), С. 1593 - 1593
Опубликована: Авг. 11, 2023
Precision agriculture employs cutting-edge technologies to increase agricultural productivity while reducing adverse impacts on the environment. is a farming approach that uses advanced technology and data analysis maximize crop yields, cut waste, productivity. It potential strategy for tackling some of major issues confronting contemporary agriculture, such as feeding growing world population environmental effects. This review article examines latest recent advances in precision including Internet Things (IoT) how make use big data. aims provide an overview innovations, challenges, future prospects smart farming. presents current state most innovations technology, drones, sensors, machine learning. The also discusses main challenges faced by management, adoption, cost-effectiveness.
Язык: Английский
Процитировано
301
Computers and Electronics in Agriculture, Год журнала: 2022, Номер 201, С. 107217 - 107217
Опубликована: Авг. 13, 2022
Язык: Английский
Процитировано
158
Agronomy, Год журнала: 2022, Номер 12(3), С. 748 - 748
Опубликована: Март 21, 2022
Agricultural Big Data is a set of technologies that allows responding to the challenges new data era. In conjunction with machine learning, farmers can use address problems such as farmers’ decision making, water management, soil crop and livestock management. Crop management includes yield prediction, disease detection, weed quality, species recognition. On other hand, considers animal welfare production. The purpose this paper synthesize evidence regarding involved in implementing learning agricultural Data. We conducted systematic literature review applying PRISMA protocol. This 30 papers published from 2015 2020. develop framework summarizes main encountered, techniques, leading used. A significant challenge design architectures due need modify adapting techniques volume increases.
Язык: Английский
Процитировано
84
Current Opinion in Environmental Sustainability, Год журнала: 2023, Номер 61, С. 101252 - 101252
Опубликована: Янв. 25, 2023
Food security, land degradation, climate change, and a growing population are interconnected challenges key issues for sustainable agriculture. In this context, the Digital Twin (DT) is uniquely positioned to overcome these support goals of sustainability. Through use state-of-the-art technologies, increased information availability can empower stakeholders pursue objectives production methods. However, if benefits be fully leveraged, potential negative technical social–ecological effects technology must assessed mitigated. Therefore, an exploratory review conducted, outlining progress current examples toward aims Additionally, technological dangers concept investigated, culminating in high-level roadmap that highlights necessary milestones required open development DTs
Язык: Английский
Процитировано
71
Smart Agricultural Technology, Год журнала: 2023, Номер 5, С. 100255 - 100255
Опубликована: Май 17, 2023
Future agricultural systems should increase productivity and sustainability of food production supply. For this, integrated efficient capture, management, sharing, use environmental data from multiple sources is essential. However, there are challenges to understand efficiently different types sources, which differ in format time interval. In this regard, the role emerging technologies considered be significant for gathering, analyses use. study, a concept was developed facilitate full integration digital enhance future smart sustainable systems. The has been based on results literature review diverse experiences expertise enabled identification stat-of-the-art technologies, knowledge gaps. features proposed solution include: collection methodologies using tools; platforms handling sharing; application Artificial Intelligent analysis; edge cloud computing; Blockchain, decision support system; governance security system. study identified potential positive implications i.e. implementation could value, farm productivity, effectiveness monitoring operations making, provide innovative business models. contribute an overall competitiveness, sustainability, resilience sector as well transformation agriculture rural areas. This also provided research direction relation concept. will benefit researchers, practitioners, developers tools, policy makers supporting transition smarter more
Язык: Английский
Процитировано
52
Multimedia Tools and Applications, Год журнала: 2023, Номер 83(5), С. 12875 - 12915
Опубликована: Июль 1, 2023
Язык: Английский
Процитировано
50
World Journal of Advanced Research and Reviews, Год журнала: 2024, Номер 1, С. 2276 - 2285
Опубликована: Янв. 27, 2024
Precision agriculture, facilitated by advancements in Artificial Intelligence (AI), has emerged as a transformative paradigm modern farming. This review comprehensively examines the integration of AI technologies precision agriculture to enhance sustainability and optimize farming practices. The paper synthesizes recent research developments applications, covering key areas such crop monitoring, resource management, decision support systems, automation. adoption AI-driven techniques, including machine learning, computer vision, sensor technologies, is reshaping traditional methods providing farmers with real-time data actionable insights. Crop monitoring applications utilize satellite imagery, drones, ground-based sensors assess plant health, detect diseases, irrigation strategies. systems empower make informed choices based on data-driven predictions, weather forecasts, historical patterns, contributing resource-efficient practices minimizing environmental impact. Resource management critical aspect sustainable farming, plays pivotal role optimizing use water, fertilizers, pesticides. Smart enabled algorithms, ensure precise efficient water distribution, reducing wastage promoting conservation. analysis soil conditions helps tailor fertilization practices, enhancing nutrient utilization runoff. also explores automating operations through robotics autonomous vehicles. These not only alleviate labor shortages but improve efficiency planting, harvesting, maintenance. Additionally, fosters connectivity enabling seamless communication between devices, sensors, equipment. As continues evolve, highlights challenges future prospects. Ethical considerations, security, digital divide rural are among that need attention. Moreover, discusses potential avenues for further research, emphasizing interdisciplinary collaboration address complex issues associated implementation agriculture. provides comprehensive overview impact offering insights into current challenges, directions. enhances productivity contributes long-term ensuring food security face growing global population.
Язык: Английский
Процитировано
40
Computers and Electronics in Agriculture, Год журнала: 2024, Номер 221, С. 108982 - 108982
Опубликована: Май 8, 2024
In recent years, climate change patterns and extreme weather events have adversely affected agricultural production raised concerns about its effect on crop yields. These changes can affect the yield in many ways including length of growing season, planting harvest time windows, precipitation amount frequency, degree days, etc. So, it is important to analyze variability for a better understanding crops. This study aims historical monthly county-level data state Ohio quantify maize (Zea mays) soybean (Glycine max) temporal impact proposed scenarios yield. Machine learning algorithms were used model temperature levels variability, addition, prediction models integrated with higher lower emissions predict year 2100. To parameters Random Forest outperformed RMSE 0.61 Mt/ha R2 0.73 0.21 Mt/ha, 0.64 crop. Maximum July which has negative correlation was found be most dominant parameter followed by August, July, June positive Precipitation August maximum July. Based projected average increase Ohio's alone, predicted drop 13.2% or 18.5% respectively due scenario Ohio. Similarly, 6.64% 9.63% expected 2100 both scenarios. economic terms based current commodity values, this an astounding loss 254.7 369.7 million USD 535.9 – 751.1
Язык: Английский
Процитировано
18
Tourism Management, Год журнала: 2025, Номер 110, С. 105180 - 105180
Опубликована: Март 10, 2025
Язык: Английский
Процитировано
3