Predictive Analytics: Case Studies for AI in Agriculture

Predictive Analytics    |    Intermediate
  • 10 videos | 58m 43s
  • Includes Assessment
  • Earns a Badge
Population growth, climate change, and volatile commodity prices risk factors are putting a strain on the agricultural system nowadays. Using artificial intelligence (AI) in agriculture can potentially help mitigate this strain in areas such as yield prediction and disease detection in agriculture. In this course, explore a study that uses machine learning (ML) models for agricultural use cases. Next, explore a specific case study that attempts to predict the yield of maize and soybean crops on various American farms. Finally, examine a study that uses machine learning for pest detection. Upon completion, you'll be able to gather and analyze academic papers on machine learning in agriculture, identify problem categories and solution constructs, and recall common recurring themes in research.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Recognize the role of artificial intelligence (ai) in agriculture
    Recall how artificial intelligence can help with agriculture
    Outline the studies performed for ai for agriculture
    Recall studies of crop management and disease detection
  • Identify different studies on managing livestock with ai
    Recognize a study on the process for yield prediction
    Recall the relation between many ai features and crop yield
    Outline a study that detects insects in crops
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 51s
  • 9m 55s
  • Locked
    3.  Adoption of AI in Agriculture Case Study
    5m 34s
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    4.  AI for Agriculture Studies
    4m 57s
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    5.  Adoption of AI in Agriculture Case Study Findings
    8m 21s
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    6.  Livestock Management Studies
    6m
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    7.  Yield Prediction Case Study
    6m 21s
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    8.  Yield Prediction Case Study Findings
    7m 12s
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    9.  Detecting Pests in Crops Case Study
    5m 30s
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    10.  Course Summary
    3m 2s

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