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Using data mining techniques for decision support in agriculture: support vector machines

Code: 9781835451434
Wu Caicong, China Agricultural University, China

Chapter synopsis:

This chapter introduces data mining in agriculture, focusing on support vector machines (SVM). SVM employs optimal hyperplanes in high-dimensional space for linear classification, aiming to find the maximum-margin hyperplane, crucial for accurate classification. The SVM model, a non-probabilistic binary linear classifier, can be adapted for probabilistic classification using methods like Platt scaling. It efficiently handles nonlinear classification through the kernel trick, mapping inputs into high-dimensional spaces. In agriculture, SVM can be used for applications such as crop pest and disease identification, trajectory segmentation, and yield prediction. The chapter underscores SVM's pivotal role in transforming agricultural practices and discusses future research trends.



DOI: 10.19103/AS.2023.0132.08
£25.00
Table of contents
  • 1 Introduction
  • 2 The support vector machine model
  • 3 Developments in support vector machines algorithms
  • 4 Challenges in using support vector machines
  • 5 Solutions to support vector machine shortcomings: other models
  • 6 Case study: identifying damage from cotton spider mites
  • 7 Case study: identifying turning trajectories of a wheat harvester
  • 8 Case study: tractor emission prediction
  • 9 Case study: spring wheat yield prediction
  • 10 Conclusion and future trends
  • 11 Where to look for further information
  • 12 References

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