VijayLaxmi,
GauravSingla,
- Professor, Faculty of computing &GuruKashiUniversity,TalwandiSabo, Punjab, India
- Research Scholar, Faculty of computing &GuruKashiUniversity,TalwandiSabo, Punjab, India
Abstract
Agricultural productivity plays a critical role in global food security, and accurate crop yield prediction is essential for optimizing resource allocation and decision-making in farming. The rapid advancements in Machine Learning (ML) and Deep Learning(DL)have transformed agricultural forecasting, enabling data-driven approaches for crop prediction. This review paper provides a comprehensive analysis of various ML and DL techniques applied in crop yield forecast, highlighting the ineffectiveness, challenges, and future directions. The study explores different models, including Random Forest, Support Vector Technologies, Artificial Neural Networks, Long Short-Term Memory Networks, and other ensemble methods, comparing the performance based on accuracy metrics such as R² score, RMSE,and MAE. Additionally, the paper discusses the incorporation of IoT and remote sensing technologies in modern precision agriculture.statistical methods, while deep learning models excel in handling complex, nonlinear relationships in agricultural data. However, challenges such as data availability,environmental variability,and computationale fficiency remain key barriers.This review aims to provide insights into the potential of AI-driven approaches inenhancing agricultural sustainability and precision farming,paving the way for future research and innovations in smart agriculture
Keywords: Crop Yield Forecasting, Machine Learning in Agriculture, Deep Learning for Crop Prediction, Precision Agriculture, Smart Farming, Artificial Intelligence in Agriculture, RemoteSensingandIoTinAgriculture,Data-Driven Agriculture
[This article belongs to Research & Reviews : Journal of Agricultural Science and Technology ]
References
- Tiwari A, Darbari M. Emerging trends in computer science and its application. In: Proceedings of the International Conference on Advances in Emerging Trends in Computer Applications (ICAETC2023); 2023 Dec 21–22; Lucknow, India.
- Jhajharia K, Mathur P, Jain S, Nijhawan S. Crop yield prediction using machine learning and deep learning techniques. Procedia Comput Sci. 2023;218:406–17.
- Kolipaka VRR, Namburu A. Crop yield prediction using machine learning and deep learning techniques. Int J Recent Innov Trends Comput Commun. 2023;11(10):582–4.
- Maheswari MU, Ramasamy R. A comparative study of agricultural crop yield prediction using machine learning techniques. In: Proceedings of the 4th IEEE International Conference on Advanced Computing and Communication Systems; 2023 Mar 16–18; Shillong, Meghalaya, India.
- Elbasietal E. Crop prediction model using machine learning algorithms. Appl Sci. 2023;13:9288.
- Patil P, Athavale P, Bothara M, Tambolkar S, More A. Crop selection and yield prediction using machine learning approach. Curr Agric Res J. 2023;11(3):968–80.
- Nikhil UV, Pandiyan AM, Raja SP, Stamenkovic Z. Machine learning-based crop yield prediction in South India: performance analysis of various models. Computers. 2024;13:137.
- Petrillo A, Rehman M, Baffo I. Digital and sustainable transition in textile industry through Internet of Things technologies: a Pakistani case study. Appl Sci. 2024;14(13):5380.
- Bukhari SNH. Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture. USA: CRC Press; 2024.
- Usang RO, Olu-Owolabi BI, Adebowale KO. Integrating principal component analysis, fuzzy inference systems, and advanced neural networks for enhanced estuarine water quality assessment. J Hydrol Reg Stud. 2025;57:102182.
| Volume | 14 | |
| Issue | 03 | |
| Received | 10/08/2025 | |
| Accepted | 10/09/2025 | |
| Published | 25/09/2025 | |
| Publication Time | 46 Days |