Artificial Intelligence in Agriculture

Scope & Guideline

Empowering Agricultural Progress with Cutting-Edge AI

Introduction

Welcome to your portal for understanding Artificial Intelligence in Agriculture, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN-
PublisherKEAI PUBLISHING LTD
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationARTIF INTELL AGR / Artif. Intell. Agric.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address16 DONGHUANGCHENGGEN NORTH ST, Building 5, Room 411, BEIJING, DONGCHENG DISTRICT 100009, PEOPLES R CHINA

Aims and Scopes

The journal 'Artificial Intelligence in Agriculture' aims to advance the integration of artificial intelligence technologies within agricultural practices, focusing on enhancing productivity, sustainability, and precision in farming. The methodologies employed range from machine learning and deep learning to computer vision and remote sensing, with a strong emphasis on practical applications in various agricultural sectors.
  1. Precision Agriculture and Crop Management:
    Explores the use of AI to optimize crop yields and resource use, including precision irrigation, nutrient management, and pest control.
  2. Computer Vision and Image Processing:
    Utilizes advanced imaging techniques and algorithms for tasks such as crop monitoring, disease detection, and quality assessment.
  3. Machine Learning Applications:
    Focuses on developing and applying machine learning algorithms for predictive analytics, classification tasks, and automation in agriculture.
  4. Remote Sensing and UAV Technologies:
    Investigates the role of drones and satellite imagery in monitoring agricultural landscapes and improving decision-making processes.
  5. Sustainable Farming Practices:
    Promotes the integration of AI technologies to support environmentally friendly practices and enhance food security.
Recent publications in 'Artificial Intelligence in Agriculture' reflect a dynamic shift towards advanced technological applications and interdisciplinary approaches. Emerging themes highlight the growing relevance of AI in addressing contemporary agricultural challenges.
  1. Deep Learning Innovations:
    There is a significant increase in research utilizing deep learning frameworks for various applications, including disease detection, crop classification, and yield prediction.
  2. Integration of IoT and AI:
    The convergence of Internet of Things (IoT) technologies with AI is trending, focusing on real-time monitoring and data-driven decision-making in agricultural contexts.
  3. Automated and Smart Agriculture Solutions:
    Emerging research is heavily focused on automation technologies, including robotic systems and AI-driven machinery for tasks ranging from planting to harvesting.
  4. Data-Driven Decision-Making:
    A growing emphasis on utilizing big data analytics and machine learning to inform strategic decisions in crop management and agricultural sustainability.
  5. Environmental and Climate Resilience:
    Research is increasingly focused on using AI to develop strategies for climate-resilient agriculture, addressing issues such as drought prediction and resource management.

Declining or Waning

While the journal continues to publish a diverse range of topics, certain themes have shown a decline in prominence over recent years. This may reflect shifts in research focus or advancements in technology that render some previously popular topics less relevant.
  1. Traditional Agricultural Techniques:
    Research focusing on conventional farming methods has decreased, as the emphasis shifts towards technology-driven solutions and smart agriculture.
  2. Basic Data Collection Methods:
    Studies that primarily discuss manual or rudimentary data collection methods are becoming less common as more sophisticated, automated approaches gain traction.
  3. General Agricultural Reviews:
    Broad reviews of agricultural practices without a specific technological focus have seen a decline, as more niche and technology-specific studies are favored.
  4. Non-AI-Based Solutions:
    Research that does not incorporate AI or machine learning methodologies is becoming less frequent, as the journal emphasizes innovative technological applications.

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