A Review on Crop Yield Prediction and Disease Detection

Abstract

Crop yield prediction and plant disease detection are critical challenges in modern agriculture, directly impacting productivity and food security. As part of a project-oriented study, this paper presents a comprehensive review of machine learning and deep learning techniques applied to these agricultural problems. The review analyzes existing approaches such as convolutional neural networks, ensemble learning models, time-series forecasting techniques, and IoT-enabled systems reported in recent literature. Performance metrics, input features, and application contexts of these methods are compared to identify strengths, limitations, and research gaps. The insights obtained from this review serve as a foundation for the design and development of an intelligent agricultural decision-support system in future project implementation.

Introduction

Agriculture is vital for human survival but faces challenges like crop diseases, pests, climate variability, and soil degradation. Traditional disease detection and yield prediction methods are often slow, subjective, and inaccurate.

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) enable precision agriculture by automating disease detection, yield forecasting, and decision-making. Convolutional Neural Networks (CNNs) detect crop diseases using leaf images, while YOLO allows real-time localization of multiple diseases. Yield prediction uses models like XGBoost and LSTM to capture non-linear and temporal patterns from environmental and crop-specific data.

Hybrid and ensemble models combining ML and DL (e.g., Random Forest, XGBoost, CNN–LSTM) achieve high accuracy (92–98%) for disease detection and yield prediction. Integration with IoT devices, multisensor data, and remote sensing enhances real-time monitoring, pest prediction, and decision support. Mobile applications bridge lab research and field deployment, providing farmers with actionable insights on crop health and interventions.

The literature emphasizes that multi-source data fusion, hybrid learning frameworks, and explainable AI are key to sustainable, scalable, and accurate precision agriculture systems.

Conclusion

The reviewed literature collectively illustrates the transformative potential of artificial intelligence and machine learning in modern agriculture. Across multiple studies, models incorporating deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent architectures (LSTM), and ensemble learning techniques have consistently demonstrated high predictive accuracy in crop yield estimation and pest detection. Integrating multisource data, such as climatic parameters, soil characteristics, NDVI indices, and remote sensing imagery, significantly enhances model robustness and adaptability to varying environmental conditions. The works analyzed, including those employing IoT-enabled deep learning systems and multisensor fusion techniques, reveal that real-time environmental monitoring can effectively predict pest infestations and crop productivity with accuracies often exceeding 94–97%. The hybrid CNN–LSTM architecture particularly excels in capturing spatial–temporal dependencies, while ensemble-based frameworks demonstrate strong generalization across different crops and regions. Moreover, the adoption of explainable AI (XAI) methods ensures interpretability and transparency, making these systems more acceptable for real-world deployment among agronomists and farmers. Despite these advancements, several challenges persist. Model scalability, data heterogeneity, limited labeled datasets, and the dynamic nature of agricultural ecosystems remain major research bottlenecks. Additionally, achieving global generalization requires integrating data from diverse agro-climatic zones and improving model interpretability for non-technical users. The findings of this review will guide the selection of algorithms and system architecture for the subsequent project implementation. Future work will focus on developing and evaluating a prototype system based on the insights identified in this study.

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Copyright

Copyright © 2025 Akshara Vinod, Fahad N N, Megha K P, Saniga Sudheer, Binu Balakrishnan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.