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Titlebook: Artificial Intelligence Techniques in Smart Agriculture; Siddharth Singh Chouhan,Akash Saxena,Sanjeev Jain Book 2024 The Editor(s) (if app

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發(fā)表于 2025-3-21 18:47:13 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Artificial Intelligence Techniques in Smart Agriculture
影響因子2023Siddharth Singh Chouhan,Akash Saxena,Sanjeev Jain
視頻videohttp://file.papertrans.cn/168/167570/167570.mp4
發(fā)行地址Discusses mechanisms and understanding for adaptation of artificial intelligence in different sectors of agriculture.Summarizes different models, algorithms, tools and findings related to artificial i
圖書封面Titlebook: Artificial Intelligence Techniques in Smart Agriculture;  Siddharth Singh Chouhan,Akash Saxena,Sanjeev Jain Book 2024 The Editor(s) (if app
影響因子.This edited volume explores the integration of artificial intelligence to improve crop production. It addresses the critical need for intelligent crop management in light of the world‘s escalating population. Encompassing a spectrum of technologies, including computer vision, image processing, soft computing, machine learning, and deep learning, the book explores advancements in decision-making systems. It integrates data science methodologies, Internet of Things, wireless communications, and a range of sensors and actuators to provide precise, timely, and cost-effective solutions to agricultural challenges, ultimately enhancing both the quality and quantity of crop yields. The book empowers its audience to direct their efforts towards designing models and prototypes that benefit society and the environment, making it an indispensable resource for those eager to shape the future of intelligent agriculture...It serves as a comprehensive guide for students, scholars, and academicians keen on delving into the transformative field of artificial intelligence in agriculture. Researchers, scientists, and field experts will find invaluable insights to guide their exploration and contribut
Pindex Book 2024
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發(fā)表于 2025-3-21 21:14:04 | 只看該作者
Thea Heijenbrok-van Herpen,Wobbe HospesCollaboratively addressing the numerous sophisticated technical and social issues is necessary for fully utilizing the potential of AI for small-scale farmers who are at risk. The primary issue that has been highlighted is the lack of high-quality data that represent different farm types, marginaliz
板凳
發(fā)表于 2025-3-22 01:11:00 | 只看該作者
Thea Heijenbrok-van Herpen,Wobbe Hospeszed agriculture, increasing efficiency, productivity, and sustainability. One of the main drivers of this change is the integration of artificial intelligence (AI) technologies encompassing machine learning (ML), computer vision and data analytics into agriculture. AI has become a revolutionary tool
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發(fā)表于 2025-3-22 07:13:29 | 只看該作者
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發(fā)表于 2025-3-22 12:08:44 | 只看該作者
Criteria voor goede richtlijnenrming practices. The necessity of smart farming in addressing global challenges such as increasing food demand, resource scarcity, and the impacts of climate change has been highlighted. Smart farming, driven by technologies like precision agriculture, digital farming, artificial intelligence (AI),
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發(fā)表于 2025-3-22 16:12:54 | 只看該作者
https://doi.org/10.1007/978-90-368-0267-3hapter delves into the technical underpinnings of these innovations, exploring diverse devices, their functionalities, and the platforms that orchestrate their seamless integration. We embark on a journey, dissecting the hardware and software components of intelligent devices, including sensors, act
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發(fā)表于 2025-3-23 00:11:58 | 只看該作者
J.J.E. Everdingen,D.H.H. Dreesens,T. Weijdenicultural business has a number of barriers, notably changing weather conditions that induce a variety of serious diseases in plants, depressing crop yields, and revenues. An optimized Convolution Neural Network model for identifying and reliably detecting plant stress phenotyping. By using the capa
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發(fā)表于 2025-3-23 03:30:21 | 只看該作者
Integriteit en kwaliteit van clinical trials computer vision. This chapter explores the profound impact of AI-driven computer vision, highlighting its crucial role in changing how we manage crops, detect diseases, and optimize resources in agriculture. This integration goes beyond just improving precision farming; it empowers effective crop m
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