Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2024 Nov 29;10(24):e40836.
doi: 10.1016/j.heliyon.2024.e40836. eCollection 2024 Dec 30.

Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability

Md Abu Jabed  1   2 , Masrah Azrifah Azmi Murad  1
Affiliations
Review

Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability

Md Abu Jabed et al. Heliyon. .

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 wealth of AI-powered solutions employed in crop yield prediction, emphasizing the importance of precise environmental and agricultural data. Key factors contributing to accurate estimation include temperature, rainfall, soil type, humidity, and various vegetation indices, such as NDVI, EVI, LAI, and NDWI. The research paper also examines the algorithms frequently utilized in the machine learning domain, including Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). In the realm of deep learning, Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM), and Deep Neural Networks (DNN) emerge as promising candidates. The findings of this study shed light on the transformative potential of advanced AI techniques in improving crop yield estimation accuracy, ultimately enhancing agricultural planning and resource management. By addressing the challenges posed by geographical diversity, crop heterogeneity, and changing environmental conditions, AI-driven models offer new avenues for sustainable agriculture in an ever-evolving world. This research paper provides valuable insights and directions for future studies, highlighting the critical role of AI in ensuring food security and sustainability in agriculture.

Keywords: Crop yield production; Decision support system; Deep learning; Machine learning; Systematic literature review.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Flowchart displaying search results.
Fig. 2
Fig. 2
Distribution of articles.
Fig. 3
Fig. 3
Most Used Machine Learning and Deep Learning approaches.
Fig. 4
Fig. 4
Metrics for assessing performance.
See this image and copyright information in PMC

References

    1. Modi A., Sharma P., Saraswat D., Mehta R. Review of crop yield estimation using machine learning and deep learning techniques. Scalable Computing. 2022;23(2):59–79. doi: 10.12694/scpe.v23i2.2025. - DOI
    1. Goel N., Kaur S., Kumar Y. AI, Edge and IoT-Based Smart Agriculture. Elsevier Inc; 2022. Chapter 23 - machine learning-based remote monitoring and predictive analytics system for crop and livestock. - DOI
    1. van Klompenburg T., Kassahun A., Catal C. Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 2020;177(July) doi: 10.1016/j.compag.2020.105709. - DOI
    1. Xu X., Gao P., Zhu X., Guo W., Ding J., Li C., Zhu M. Design of an integrated climatic assessment indicator (ICAI) for wheat production : a case study in Jiangsu Province , China. Ecol. Indicat. 2019;101(July 2018):943–953. doi: 10.1016/j.ecolind.2019.01.059. - DOI
    1. Filippi P. 0123456789; 2019. An Approach to Forecast Grain Crop Yield Using Multi - Layered , Multi - Farm Data Sets and Machine Learning. - DOI

LinkOut - more resources