A Comprehensive Review of Deep Learning in Agricultural Visual Perception: Progress, Bottlenecks, and Emerging Trends (深度学习在农业视觉感知中的综合综述:进展、瓶颈与新兴趋势)

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Open Access Review

by
Chen Chen

1,*,
Runlin Liu

2 and
Leijun Xu

1

1

School of Low-Altitude Technology, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China

2

School of Electrical and Information Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang 212013, China

Author to whom correspondence should be addressed.

Submission received: 12 July 2026 / Revised: 7 August 2026 / Accepted: 24 August 2026 / Published: 26 August 2026

Abstract

This paper provides a comprehensive review of deep learning in agricultural visual perception. Climate change and land degradation demand a shift from experience-driven to data-driven intelligent agriculture. Traditional manual inspections remain subjective and unscalable. Computer vision offers a non-invasive solution for precision crop and livestock management, while challenges also exist. Its application in actual agricultural scenarios faces specific challenges such as severe occlusion, drastic changes in lighting, and non-rigid deformation of biological targets. To systematically summarize how these perception bottlenecks are being resolved, this review explores deep learning architectures featuring spatial extraction and spatio-temporal modeling. This comprehensive review first constructs a progressive analytical framework from low-level data augmentation and static spatial cognition to high-level dynamic spatio-temporal reasoning, and then systematically classifies existing literature. In-depth analysis reveals that these three core challenges can be computationally resolved using three unified technologies. Domain drift and label scarcity, rather than baseline accuracy, are the main obstacles to practical deployment. We further identified four priority research directions, i.e., architectural efficiency, data-level annotation efficiency, deployment-level privacy and simulation, and trust-level security, to guide future research.

1. Introduction

Driven by escalating climate change, land degradation, and resource scarcity, precision agriculture has emerged as a strategic imperative for ensuring global food security [1]. Within the realms of broad-acre farming and advanced animal husbandry, achieving precise, instantaneous observation of plant developmental traits and specific animal actions remains essential for refining operational strategies. Nevertheless, automated monitoring in such environments encounters a fundamental obstacle in computer vision: the intensely shifting spatial arrangements and continuous physical transformations of the observed subjects. Accurate and real-time perception of crop phenotypes and livestock behavior is fundamental to optimizing production decisions and improving yield in modern agriculture [2]. However, intelligent perception in these scenarios faces a core bottleneck: the highly dynamic spatial and morphological evolution of targets [3]. For crop phenotyping, plant canopies undergo drastic spatiotemporal changes throughout their growth cycle, such as wheat transitioning from creeping to upright posture and from green to yellow [4]. For livestock analysis, individual animals exhibit severe non-rigid deformations during posture transitions, e.g., pigs standing, lying down, or walking [5]. These behavioral state variations produce substantially different visual representations, posing significant challenges for robust visual recognition [6]. The non-rigid deformation of pigs when standing, lying down, walking, and interacting with environmental enrichment objects leads to huge differences in their visual representations under different behavioral states [7]. Traditional operators with fixed geometric topology fail under such conditions [8]. Deep learning has emerged as the dominant paradigm to overcome this limitation, with architectures such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Neural Networks (GNNs) learning adaptive hierarchical representations from massive data.

Besides dynamic morphological evolution, high density and severe occlusion from spatial overlap pose another major challenge for visual perception. Intertwined targets severely degrade object boundaries in agricultural images. For example, dense cotton bolls create substantial visual clutter that obscures individual plant structures [9,10]. Similarly, highly aggregated fish during feeding cause severe inter-individual overlap, making it difficult for standard tracking pipelines to maintain consistent target identities and ultimately leading to irreversible loss of structural information [11,12]. To overcome this limitation, modern visual perception systems in precision agriculture are transforming discrete individual identification toward continuous spatial density estimation [13]. This density map-based modeling method mitigates occlusion-induced interference by bypassing the need for explicit segmentation of fragmented boundaries, enabling high-precision macroscopic perception even in extremely crowded environments [14]. Finally, image enhancement and domain adaptation technologies have been increasingly adopted to address visual perception failures caused by degradation in harsh agricultural environments [15].

For example, Yu et al. (2024) proposed a lightweight image enhancement network and a nighttime dairy cow detection method, eliminating light degradation interference [16]. Zhao et al. (2025) introduced an enhancement architecture with dynamic residual integration and ultra-lightweight upsampling modules to address rain and fog degradation. This approach overcomes harsh weather interference for strawberry maturity assessment [17]. Furthermore, Kaplan et al. (2026) developed a color correction-based image enhancement scheme for underwater aquaculture. This scheme corrects underwater imaging distortion and greatly improves target recognition systems [18]. In summary, traditional computer vision methods show significant limitations in complex and dynamic agricultural production environments. Current visual perception is undergoing a paradigm transition from single-task precision toward integrated optimization of physics-driven low-level visual restoration and high-level semantic perception [19]. This architecture integrates task-specific deep networks (e.g., low-light enhancement, image dehazing, and underwater restoration) with downstream perception tasks, featuring a clear division of labor and high targeted efficacy. Breaking these environmental bottlenecks is essential for achieving robust intelligent perception.

This review systematically covers four major categories of deep learning architectures that have driven recent developments in agricultural vision. Convolutional Neural Networks, including classic backbone networks and detection-specific variants, remain the workhorse for local pattern encoding and real-time edge deployment. Recurrent Neural Networks and Sequence Models are used to address temporal dependencies in growth monitoring and behavior analysis. Transformer-based architectures and their efficient successor, Vision Mamba, have emerged in recent years, providing global context modeling and linear time-scale scaling capabilities for high-resolution images. Generative models, including Generative Adversarial Networks and diffusion models, address data scarcity through synthetic augmentation and achieve zero-shot transfer. Furthermore, graph-based methods can dynamically model non-Euclidean relationships between discrete entities (e.g., livestock skeletons), while deep reinforcement learning shifts the paradigm from passive perception to active decision-making under uncertainty. This review selects these architectures based on two principles: (1) their prevalence in agricultural vision literature over the past five years; and (2) their effectiveness in addressing the three core challenges defining the field: non-rigid target deformation, severe occlusion, and environmental degradation.

Unlike previous reviews that categorized methods by crop type or application area, this review makes three contributions: (1) it constructs a progressive four-layer framework covering data foundation, static spatial cognition, dynamic spatiotemporal reasoning, and embodied deployment; (2) it critically compares different architectures within each layer, clarifying their advantages, limitations, and application scenarios; and (3) it systematically explores emerging visual foundation models and embodied intelligence. This review aims to reveal the mathematical unity of agricultural perception problems and provide practical guidance for future research.

2. Review Framework and Article Structure

This review aims to systematically survey and critically evaluate deep learning techniques for agricultural visual perception, focusing on how modern architectures address three persistent bottlenecks: non-rigid target deformation, severe occlusion, and environmental image degradation. Unlike previous reviews that categorize methods by crop species or application scenarios, this review offers three distinctive contributions: a progressive four-layer analytical framework spanning data foundation, static spatial cognition, dynamic spatiotemporal reasoning, and future outlook; critical comparisons of competing architectures within each layer to clarify their respective advantages, limitations, and application scenarios; and a systematic discussion of emerging vision foundation models and embodied intelligence, which have been largely overlooked in prior agricultural surveys. The remainder of this review is organized as follows. Section 3 covers the foundational data pipeline. It includes multi-modal imaging acquisition (Section 3.1), low-level enhancement under extreme conditions (Section 3.2), and cross-domain transfer learning for data-scarce scenarios (Section 3.3). Together, these three components form a coherent “acquisition-restoration-generalization” workflow, as depicted in Figure 1. Section 4 moves from data foundation to static spatial cognition. It examines three core perceptual tasks: non-rigid deformation detection, dense crowd counting, and fine-grained classification. Section 5 extends static perception to high-level spatiotemporal reasoning. It addresses short-term behavioral dynamics, long-term lifecycle analysis, heterogeneous fusion, and embodied decision-making. Section 6 synthesizes persistent challenges and future directions. Section 7 concludes the review.

Figure 1. Deep learning-driven closed-loop visual perception in precision agriculture. (a–c) Multi-modal imaging classification; (d) Dehazing; (e) Low-light enhancement; (f) Underwater restoration; (g,h) Deformation/Non-rigid; (i,j) Crowding/Severe Occlusion; (k) Low inter-class variance; (l) Short-term behaviors; (m) Long-term evolution; (n) Vision foundation; (o) Agricultural robots/Automated machinery.

Figure 1 presents the schematic framework for deep learning-driven closed-loop visual perception in precision agriculture, highlighting the integration of deep learning models within the perception cycle. This system demonstrates a four-layer progressive technological evolution, from bottom-level multimodal data acquisition, static spatial analysis, and dynamic spatio-temporal inference to top-level embodied execution and data feedback. (1) Multidimensional data acquisition sources: macroscopic satellite remote sensing (a) [20], microscopic multispectral imaging (b) [21], heterogeneous data of laboratory microscopic images (c) [22]. (2) Image enhancement under extreme environments: image dehazing (d) [23], low-light enhancement (e) [16], underwater reconstruction algorithm (f) [24]. (3) Solving non-rigid deformation problems: using key point skeleton estimation technology to reconstruct the topological structure of pigs (g) [25], deep feature extraction and coordinate regression architecture (h) [26]. (4) Solving extreme dense occlusion problems: late stage of wheat planting (i) [27], density problem in intensive fish farming (j) [28]. (5) Solving the problem of low interclass variance: introducing fine-grained visual classification (FGVC), focusing on microscopic local features to accurately distinguish crop varieties or disease categories with extremely similar appearances (k) [29]. (6) Cross-scale spatio-temporal dynamic cognition: covering high-frequency dynamic behavior localization at the millisecond/second level (e.g., tracking the high dynamic operation of greenhouse personnel/agricultural machinery) (l) [30], as well as long-term evolution and three-dimensional morphological simulation prediction of crops throughout the growing season (m) [31]. (7) Embodied Intelligent Closed-Loop Deployment: A large-scale vision-language foundation model (e.g., Vision-Language Model, Large Language Model) integrating prior agricultural knowledge is responsible for global semantic reasoning and command issuance (n) [32], as well as edge-side driving of agricultural automated machinery/robots to perform precise physical interactions (o) [33]. (8) Right-side Feedback Loop: The physical trial-and-error and interactive feedback of the robot in a real unstructured environment are transformed into high-value self-supervised data and continuously fed back to the underlying visual model, forming a closed-loop evolutionary ecosystem. As the foundational layer of the proposed framework (Figure 1), the following sections will first systematically discuss multi-modal data acquisition and low-level image enhancement techniques.

3. Multi-Modal Data and Low-Level Enhancement

This section establishes the foundational visual workflow for agricultural perception, comprising three stages that transform raw sensory input into actionable semantic information. First, we outline multi-scale, multimodal imaging methods (Section 3.1), ranging from satellite remote sensing to microscopic imaging. Second, we explore enhancement techniques (Section 3.2) for recovering degraded images under extreme conditions (rain, fog, low light, and underwater scattering). Third, we address data scarcity through cross-domain transfer learning (Section 3.3). These three parts constitute a coherent “acquisition–restoration–generalization” workflow, laying the foundation for subsequent perception tasks.

Agricultural visual perception relies heavily on specific imaging modalities designed for different spatial scales and specific perception tasks—from satellite-based regional crop monitoring to Unmanned Aerial Vehicle (UAV)-based plot-level perception and ground-based proximal inspection of crops and livestock. Satellite remote sensing and Unmanned Aerial Vehicle (UAV) remote sensing respectively construct complementary observation systems for macro-strategic monitoring and local fine-grained observation. Awais et al. (2022) confirmed through regression analysis that satellite imagery excels in regional farmland assessment, while UAVs capture local heterogeneous details lost by satellites [34]. Building a robust visual foundation for precision agriculture requires a systematic approach that combines high-fidelity multimodal data acquisition with targeted environmental degradation mitigation measures. Figure 2 illustrates the overall architecture and workflow of this process, depicting the step-by-step construction from raw multi-source input data to a high-quality visual foundation.

Figure 2. Workflow of m