Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability
Chittagong University of Engineering & Technology · Universiti Putra Malaysia
Abstract
The agriculture sector is confronted with numerous challenges in the quest for accurate crop yield estimation, which is essential for efficient resource management and mitigating food scarcity in a rapidly growing global population. This research paper delves into the application of advanced Artificial Intelligence (AI) techniques to enhance crop yield estimation in the context of diverse agricultural challenges. Through a systematic literature review and analysis of relevant studies, this paper explores the role of AI methods, such as Machine Learning (ML) and Deep Learning (DL), in addressing the complexities posed by geographical variations, crop diversity, and cultivation areas. The review identifies a…
Citation impact
- FWCI
- 77.11
- Percentile
- 100%
- References
- 139
Authors
2Topics & keywords
- Sustainability
- Yield (engineering)
- Agriculture
- Agricultural engineering
- Crop yield
- Deep learning
- Artificial intelligence
- Agroforestry
- Zero hunger