Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging

2025-05-01

AI Summary

Wheat production faces mounting pressures from pests, diseases, climate variability, and water scarcity, yet conventional monitoring methods lack early, non-destructive diagnostic capability. While hyperspectral imaging (HSI) offers rich spectral-spatial information for precision phenotyping, its high dimensionality and scarcity of labeled samples hinder deep learning adoption. This paper presents the first systematic survey of deep learning applications in HSI-based wheat analysis, covering key tasks including cultivar classification, disease detection, and yield prediction. We construct the field’s first comprehensive landscape—synthesizing over 100 state-of-the-art works, benchmark datasets, model evolution trajectories, and practical bottlenecks. We introduce an open-source, continuously updated resource repository. To address HSI-specific challenges, we unify CNNs, graph neural networks (GNNs), self-supervised learning, and few-shot learning strategies. Finally, we rigorously characterize performance ceilings and generalization limitations, and identify three critical future directions: enhanced model interpretability, cross-scenario transferability, and lightweight deployment.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Other Foundations of Computer Vision
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

As one of the most widely cultivated and consumed crops, wheat is essential to global food security. However, wheat production is increasingly challenged by pests, diseases, climate change, and water scarcity, threatening yields. Traditional crop monitoring methods are labor-intensive and often ineffective for early issue detection. Hyperspectral imaging (HSI) has emerged as a non-destructive and efficient technology for remote crop health assessment. However, the high dimensionality of HSI data and limited availability of labeled samples present notable challenges. In recent years, deep learning has shown great promise in addressing these challenges due to its ability to extract and analysis complex structures. Despite advancements in applying deep learning methods to HSI data for wheat crop analysis, no comprehensive survey currently exists in this field. This review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation. It also highlights the strengths, limitations, and future opportunities in leveraging deep learning methods for HSI-based wheat crop analysis. We have listed the current state-of-the-art papers and will continue tracking updating them in the following https://github.com/fadi-07/Awesome-Wheat-HSI-DeepLearning.

Problem

• Enhancing wheat crop analysis using deep learning and hyperspectral imaging
• Overcoming high-dimensional HSI data and limited labeled samples challenges
• Surveying deep learning applications for wheat disease and yield monitoring

Innovation

• Uses hyperspectral imaging for wheat crop analysis
• Applies deep learning to handle HSI data challenges
• Surveys methods for disease detection and yield estimation