Predictive Analytics: Case Studies for AI in Agriculture
Predictive Analytics 2022
| 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
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discover the key concepts covered in this courserecognize the role of artificial intelligence (AI) in agriculturerecall how artificial intelligence can help with agricultureoutline the studies performed for AI for agriculturerecall studies of crop management and disease detection
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identify different studies on managing livestock with AIrecognize a study on the process for yield predictionrecall the relation between many AI features and crop yieldoutline a study that detects insects in cropssummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview1m 51sUP NEXT
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2.The Role of AI in Agriculture9m 55s
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3.Adoption of AI in Agriculture Case Study5m 34s
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4.AI for Agriculture Studies4m 57s
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5.Adoption of AI in Agriculture Case Study Findings8m 21s
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6.Livestock Management Studies6m
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7.Yield Prediction Case Study6m 21s
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8.Yield Prediction Case Study Findings7m 12s
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9.Detecting Pests in Crops Case Study5m 30s
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10.Course Summary3m 2s
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