Application of Machine Learning in Smart Agriculture

Chao Huang ； Mingqiang Gao ； Haoyuan Ma

《International Core Journal of Engineering》 10卷9期 (2024/09) Pp. 10-13

https://doi.org/10.6919/ICJE.202409_10(9).0002

摘要

As an important component of the artificial intelligence, machine learning has not only attracted wide attention in the academic world, but also has great application potential in the agricultural industry. Machine learning is an advanced technology for smart agriculture that can develop advanced methods of disease detection and classification. In view of the application potential of machine learning in the field of agriculture, the concept and classification of machine learning are described through the research of machine learning related literature. This paper summarizes the application status of machine learning in smart agriculture by combining machine learning with the management of crop growth period, planting conditions and yield requirements.Major applications of machine learning, including crop, water, soil, and animal management, are investigated, revealing its important role in revolutionizing traditional agricultural practices. This paper focuses on the application of machine learning in pest identification and control, crop yield, water resource analysis and soil analysis. Combined with the problems existing in the application of machine learning in smart agriculture, the future research direction is proposed.

關鍵字

Machine Learning ； Agricultural Applications ； Smart Agriculture ； Disease Detection

參考文獻

White E L, Thomasson J A, Auvermann B et al. Report from the conference, ‘identifying obstacles to applying big data in agriculture’[J]. Precision Agriculture, 2020, 22(1):306-315.

Bhat S A, Huang N-F. Big Data and AI Revolution in Precision Agriculture: Survey and Challenges[J]. IEEE Access, 2021, 9(110209-110222).

Araújo S O, Peres R S, Barata J et al. Characterising the Agriculture 4.0 Landscape-Emerging Trends, Challenges and Opportunities[J]. Agronomy, 2021, 11(4):667.

Mueller B, Kinoshita T, Peebles A et al. Artificial intelligence and machine learning in emergency medicine: a narrative review[J]. Acute Med Surg, 2022, 9(1):e740.

Zhang J, Nazir S, Huang A et al. Multicriteria Decision and Machine Learning Algorithms for Component Security Evaluation: Library-Based Overview[J]. Security and Communication Networks 2020(1):1-14.