Vision Transformers in Precision Agriculture: A Comprehensive Survey

2025-04-30

AI Summary

Plant disease detection in precision agriculture faces bottlenecks in scalability and accuracy, particularly under resource-constrained field conditions. Method: This study systematically reviews Vision Transformer (ViT) applications across disease classification, detection, and segmentation tasks. It comparatively analyzes architectural characteristics of ViTs versus CNNs, maps their transfer learning pathways in agricultural vision, characterizes differences in inductive biases, and surveys recent hybrid model advances. A unified analytical framework is established, integrating major agricultural datasets, standardized evaluation metrics, and cross-model performance benchmarks. Contribution/Results: The study reveals ViTs’ superior cross-domain generalization and long-range dependency modeling capabilities. It further identifies lightweight optimization strategies tailored to few-shot learning and low-compute agricultural scenarios—providing both theoretical foundations and practical implementation guidelines for deploying ViTs in real-world, resource-limited farming environments.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Detecting plant diseases is a crucial aspect of modern agriculture - it plays a key role in maintaining crop health and increasing overall yield. Traditional approaches, though still valuable, often rely on manual inspection or conventional machine learning techniques, both of which face limitations in scalability and accuracy. Recently, Vision Transformers (ViTs) have emerged as a promising alternative, offering benefits such as improved handling of long-range dependencies and better scalability for visual tasks. This survey explores the application of ViTs in precision agriculture, covering tasks from classification to detection and segmentation. We begin by introducing the foundational architecture of ViTs and discuss their transition from Natural Language Processing (NLP) to computer vision. The discussion includes the concept of inductive bias in traditional models like Convolutional Neural Networks (CNNs), and how ViTs mitigate these biases. We provide a comprehensive review of recent literature, focusing on key methodologies, datasets, and performance metrics. The survey also includes a comparative analysis of CNNs and ViTs, with a look at hybrid models and performance enhancements. Technical challenges - such as data requirements, computational demands, and model interpretability - are addressed alongside potential solutions. Finally, we outline potential research directions and technological advancements that could further support the integration of ViTs in real-world agricultural settings. Our goal with this study is to offer practitioners and researchers a deeper understanding of how ViTs are poised to transform smart and precision agriculture.

Problem

• Detecting plant diseases using Vision Transformers for crop health
• Comparing ViTs and CNNs in agricultural image analysis tasks
• Addressing technical challenges of ViTs in precision agriculture applications

Innovation

• Vision Transformers improve long-range dependency handling
• ViTs transition from NLP to computer vision
• Hybrid models enhance performance in agriculture