Vision Transformers in Precision Agriculture: A Comprehensive Survey

TL;DR

This survey analyzes Vision Transformers (ViTs) as an alternative to CNNs for precision agriculture tasks such as plant disease detection and crop monitoring. It covers ViT architecture, inductive biases, and strategies to mitigate biases, and compares pure ViT models with hybrid CNN–ViT approaches across numerous datasets and performance metrics. The review highlights that ViTs often outperform CNNs in global-context tasks and that hybrid models plus transfer learning improve robustness and practicality, while noting challenges in data requirements, computation, and interpretability. The authors outline future directions for efficient architectures, diverse field datasets, and edge-deployable solutions to advance real-world agricultural decision support.

Abstract

Detecting plant diseases is a crucial aspect of modern agriculture, as 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 advantages such as improved handling of long-range dependencies and better scalability for visual tasks. This review explores the application of ViTs in precision agriculture, covering a range of tasks. We begin by introducing the foundational architecture of ViTs and discussing 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. This study also includes a comparative analysis of CNNs and ViTs, along with a review of hybrid models and performance enhancements. Technical challenges such as data requirements, computational demands, and model interpretability are addressed, along with potential solutions. Finally, we outline future 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.

Figures (10)

• Figure 1: The Encoder-Decoder architecture of the Transformer model attention2017. The encoder processes the input sequence to generate contextualized representations, which are then passed to the decoder to produce the output sequence. This structure enables efficient parallel processing and captures long-range dependencies through self-attention mechanisms, allowing for better modeling of relationships between distant elements in the sequence.
• Figure 2: The Attention mechanism attention2017. The attention computation uses queries (Q), keys (K), and values (V) to generate weighted representations, allowing the model to focus on relevant parts of the input sequence based on learned attention scores.
• Figure 3: multi-head attention attention2017. It runs multiple Self-Attention operations in parallel, each with different learned projections of Q, K, and V, allowing the model to capture diverse contextual relationships from different representation subspaces.
• Figure 4: Vision Transformer architecture vit2020. The image is split into fixed-size patches, which are linearly embedded, combined with positional embeddings, and fed into a Transformer encoder. The model captures global image context through Self-Attention across all patches.
• Figure 5: The Shifted window mechanism in Swin Transformer based on MSA p44-2021. The red box indicates a window where self-attention is applied, while black boxes represent individual image patches. The shaded region B illustrates the area that is masked out during shifted window self-attention to preserve the locality of each window.