Abstract

Keywords

۱٫ Introduction

۲٫ Overview of advanced image recognition technologies

۳٫ Advanced image recognition technologies of agricultural diseases

۳٫۱٫ Methods based on deep learning

۳٫۱٫۱٫ Deep learning models

۳٫۱٫۲٫ Works based on deep learning

۳٫۱٫۳٫ Discussion

۳٫۲٫ Methods based on transfer learning

۳٫۲٫۱٫ Transfer learning models

۳٫۲٫۲٫ Works based on transfer learning

۳٫۲٫۳٫ Discussion

۳٫۳٫ Summary

۴٫ Conclusion

Declaration of Competing Interest

Acknowledgment

References

Abstract

Agricultural disease image recognition has an important role to play in the field of intelligent agriculture. Some advanced machine learning methods associated with the development of artificial intelligence technology in recent years, such as deep learning and transfer learning, have begun to be used for the recognition of agricultural diseases. However, the adoption of these methods continues to face a number of important challenges. This paper looks specifically at deep learning and transfer learning and discusses the recent progress in the use of these advanced technologies for agricultural disease image recognition. Analysis and comparison of these two methods reveals that current agricultural disease data resources make transfer learning the better option. The paper then examines the core issues that require further study for research in this domain to continue to progress, such as the construction of image datasets, the selection of big data auxiliary domains and the optimization of the transfer learning method. Creating image datasets obtained under actual cultivation conditions is found to be especially important for the development of practically viable agricultural disease image recognition systems.

۱٫ Introduction

A recent report by the Food and Agriculture Organization of the United Nations suggests that more than one third of the natural loss of agricultural production every year is caused by agricultural diseases and pests [1], making these the most important factors currently affecting agricultural production and food security [2]. Agricultural production is complex and there are numerous agricultural diseases and pests that need to be taken into account. Traditional approaches that rely on laboratory-based observations and experiments can easily lead to incorrect diagnoses.