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Precision agriculture relies upon inputs from several different types of sensors in order to produce the desired maps and recommendations. Usually, combining those different types of data into meaningful information is not trivial, so the development of techniques capable of fully exploring the complementarities of different types of sensors has received considerable attention in recent years. This field of research has been evolving fast, making it difficult to keep track of all the developments being reported in the literature. This chapter provides a comprehensive overview on the data fusion and sensor fusion subjects, focusing on the main advances achieved so far and on the main challenges that still require suitable solutions. The objective is to offer a good reference for both readers wanting to learn more about the subject, and also for those wanting to tackle similar problems themselves.
- 1 Introduction
- 2 Data fusion challenges and solutions in precision water management
- 3 Data fusion challenges and solutions in precision soil analysis and management
- 4 Data fusion challenges and solutions in precision nutrient management
- 5 Data fusion challenges and solutions in precision disease, pest and weed management
- 6 Data fusion challenges and solutions in crop monitoring, yield prediction and phenotyping
- 7 Data fusion challenges and solutions in automation and robotics
- 8 Conclusion
- 9 Future research needs
- 10 Where to look for further information
- 11 References