A Taxonomy of Machine Learning for UAV-Enabled Precision Agriculture: A Structured Survey (面向无人机赋能精准农业的机器学习方法分类:结构化综述)

Logical Operator Operator

Search Text

Search Type

add_circle_outline

remove_circle_outline

Article Menu

Font Type:

Arial Georgia Verdana

Font Size:

Aa Aa Aa

Line Spacing:

  

Column Width:

  

Background:

Open Access Review

by
Wan D. Bae

1,*,
Shayma Alkobaisi

2,
Muhammad Farhan Safdar

3 and
Prachitee Chouhan

1

1

Department of Computer Science, Seattle University, Seattle, WA 98122, USA

2

College of Information Technology, United Arab Emirates University, Sheik Khalifa Bin Zayed Street, Al Ain P.O. Box 15551, Abu Dhabi, United Arab Emirates

3

Faculty of Electronics and Information Technology, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland

Author to whom correspondence should be addressed.

Submission received: 21 February 2026 / Revised: 4 June 2026 / Accepted: 5 June 2026 / Published: 18 June 2026

Abstract

Precision agriculture increasingly relies on machine learning applied to high-resolution data acquired by unmanned aerial vehicles (UAVs) to support crop monitoring, stress detection, and yield forecasting. This survey presents a structured review of machine learning methods for UAV-enabled precision agriculture and organizes over 100 peer-reviewed studies within a unified four-dimensional taxonomy defined by sensing modality, data type, model family, and analytical task. The taxonomy enables systematic comparison across RGB, multispectral, hyperspectral, LiDAR, and IoT data sources and across classical machine learning, deep learning, hybrid sequential models, and emerging transformer-based architectures. We analyze how modeling choices interact with data characteristics to influence robustness, cross-environment generalization, computational efficiency, and deployment feasibility on UAV and edge platforms. Recurring challenges include limited labeled data, domain shift across seasons and fields, multimodal heterogeneity, occlusion, and real-time processing constraints. We identify emerging research directions, including data-efficient learning, representation-level multimodal fusion, domain adaptation, lightweight architectures for embedded deployment, and uncertainty aware decision support. By formalizing the landscape through a unified taxonomy, this survey provides a foundation for designing scalable, robust, and deployable machine learning systems for next-generation precision agriculture.

Graphical Abstract

1. Introduction

Precision agriculture (PA) increasingly depends on unmanned aerial vehicles (UAVs) and data-driven modeling systems to enable high-resolution crop monitoring, stress detection, and yield forecasting. Advances in sensing platforms, including RGB, multispectral, hyperspectral, thermal, and LiDAR (Light Detection and Ranging) sensors, combined with machine learning and deep learning techniques, have transformed how agricultural data are collected, processed, and interpreted. These technologies support scalable data-driven decision making leading to improved productivity, resource efficiency, and environmental sustainability.

Beyond serving as an application domain, PA presents a set of characteristics that actively shape the development of artificial intelligence (AI) methods. Agricultural environments expose AI systems to strong non-stationarity across seasons, cultivars, and management practices; extreme intra class variability caused by growth stages and phenology; and severe data imbalance between healthy and stressed conditions. These properties challenge assumptions commonly made in benchmark-driven computer vision and time-series modeling, where data distributions are often static and labels are abundant.

Existing AI approaches in PA are often limited in scope, focusing on specific sensing modalities, individual crops, or isolated analytical tasks such as segmentation, pest and disease detection, fruit and bloom count, and yield prediction. Many surveys emphasize either model architectures or sensing technologies but do not systematically connect data characteristics, model design, and downstream agricultural applications. As a result, it remains difficult to compare approaches across studies or to understand how methodological choices interact with real-world deployment constraints in UAV-enabled precision agriculture. In view of these limitations, the following research questions were formulated:

• How do different AI model families (e.g., convolutional neural networks (CNNs), transformers, recurrent neural networks (RNNs), and classical models) perform across UAV-based sensing modalities and agricultural tasks?

• What factors limit the reliability of AI-based predictions under varying real-world agricultural conditions?

• How can UAV-based AI systems be translated into actionable insights for crop monitoring and interventional management?

Unlike previous surveys that primarily organize the literature around individual sensing platforms, crop types, or model architectures, this review introduces a unified four-dimensional taxonomy that jointly connects sensing modality, data type, model family, and analytical task within a single analytical framework. This structure enables systematic comparison across studies that would otherwise remain disconnected, including segmentation, detection, counting, and yield prediction pipelines operating under different sensing and deployment conditions. The proposed taxonomy also provides practical guidance for designing scalable and operationally feasible UAV-enabled agricultural analytics systems.

The key contributions of this survey are as follows:

• We propose a unified taxonomy for AI-driven precision agriculture that integrates four complementary dimensions: sensing modality, data type, model family, and analytical task, enabling systematic cross-study comparison.

• We identify structural challenges including data scarcity, annotation cost, domain shift, limited cross-modal integration, and deployment constraints that limit robustness and scalability.

• We outline emerging research directions, including data-efficient learning, domain adaptation, representation-level multimodal fusion, synthetic data generation, and lightweight architectures for real-time deployment.

The literature reviewed in this survey was collected primarily from IEEE Xplore, ACM Digital Library, Scopus, Web of Science, ScienceDirect, and MDPI databases using combinations of keywords related to UAVs, precision agriculture, machine learning, deep learning, crop monitoring, disease detection, segmentation, fruit counting, and yield prediction. The survey focuses primarily on peer-reviewed journal and conference papers published between 2015 and 2025, with emphasis on studies involving UAV-enabled sensing and AI-driven analytical pipelines. Papers were selected based on their relevance to at least one of the four taxonomy dimensions introduced in this survey: sensing modality, data type, model family, or analytical task. More than 100 studies were analyzed across segmentation, pest and disease detection, bloom and fruit counting, and yield prediction tasks, spanning RGB, multispectral, hyperspectral, thermal, LiDAR, satellite, and IoT-based sensing modalities.

The remainder of this paper is organized as follows. Section 2 provides background on precision agriculture, discusses key challenges, and introduces our taxonomy. Section 3 reviews sensing modalities, data-acquisition strategies, and data-preparation techniques. Section 4 summarizes segmentation methods. Section 5 reviews pest and disease detection. Section 6 covers bloom detection, fruit counting, and yield prediction. Section 8 synthesizes cross-cutting challenges and emerging research directions, and Section 9 concludes the survey.

2. Background, and Taxonomy

2.1. Background and Research Gap

The evolution of information and communication technologies, remote sensing, and internet of things (IoT) has dramatically expanded the range of data available for precision agriculture [1,2]. UAVs and ground-based sensors provide high-resolution measurements of crop and soil conditions, while satellite platforms offer broad spatial and temporal coverage. Although these technologies enable data-driven decision making, they also introduce several challenges that directly impact the design and deployment of deep learning models.

Data acquisition is central to both deep learning (DL) models and IoT systems in agriculture. Sensors, UAVs, and other devices collect diverse data on pests, soil properties, canopy structure, crop yields, and environmental variables such as temperature, light, and humidity [2,3]. Studies on smart agriculture systems [4,5,6] emphasize the importance of reliable sensing infrastructures and communication networks for real-time monitoring and control.

High-quality annotation is equally critical. Accurate labels enable supervised DL models to learn robust representations for tasks such as pest detection, disease diagnosis, and crop health monitoring [7,8]. For UAV imagery, the annotation process is often labor intensive and requires domain expertise, particularly when dealing with subtle symptoms or occluded plant parts.

Several works highlight the difficulties of vegetation stress detection using UAVs, where models must capture both intra field variability and temporal dynamics [9,10]. Large-scale, well-annotated datasets are rarely available, and models trained on small datasets tend to overfit or fail to generalize to new fields. To mitigate these issues, researchers employ data augmentation and regularization strategies such as geometric transformations and dropout layers [11]. The use of high-resolution imagery and appropriate preprocessing steps is particularly important for building reliable prediction models [12].

Data quality issues remain a recurring challenge. Agricultural data vary widely across crop types, regions, and seasons, leading to covariate shifts that degrade model performance. Incremental or phased adoption of advanced sensing and annotation technologies has been proposed as a practical strategy to improve data quality over time and support the sustainable deployment of PA systems [13].

In summary, the following research gap continues to shape the development of AI-based solutions in UAV-enabled precision agriculture:

• Data variability and generalization: Although UAV sensing enables high-resolution monitoring, significant variability across environments, crop varieties, and management practices limits the generalizability of machine learning (ML) models.

• Data quality and limited annotated datasets: Many deep learning approaches rely on large, consistently annotated datasets, which remain scarce and costly to obtain, particularly for fine-grained agricultural tasks.

• Operational deployment and scalability: Practical deployment requires the robust integration of sensing platforms, data infrastructures, and analytical models, which is still evolving in many precision agriculture systems.

This preliminary information about the research gap motivates the methodological choices discussed in subsequent sections and underscores the need for a structured taxonomy that connects sensing modalities, data types, model families, and analytical tasks.

2.2. Taxonomy of UAV-Based Sensing and Machine Learning in Precision Agriculture

The diversity of sensing platforms, data types, model architectures, and agricultural applications makes it challenging to compare existing approaches in a systematic manner. To provide a unifying perspective, we introduce a taxonomy that organizes UAV-based sensing and machine learning methods along four complementary dimensions: (1) sensing modality, (2) data type, (3) model family, and (4) analytical task. This taxonomy links upstream sensing and data preparation steps in Section 3 with downstream segmentation, detection, counting, and prediction methods discussed in later sections. It also provides the structural foundation for the survey, with each subsequent section mapped explicitly to one or more of these four dimensions.

Several recent surveys review deep learning applications in agriculture or remote sensing independently; however, they typically organize the literature along a single dimension, such as model architecture, crop type, or sensing platform. In contrast, the taxonomy proposed in this survey integrates four complementary dimensions including sensing modality, data type, model family, and analytical task into a unified framework. This multidimensional structure enables systematic comparison across studies that would otherwise appear disconnected, such as UAV-based object detection and satellite-driven yield forecasting.

Unlike model centric surveys that emphasize architectural trends, or remote sensing reviews that focus primarily on sensor characteristics, our taxonomy explicitly links upstream sensing infrastructure and data preparation choices to downstream learning models and decision support objectives. This integrated perspective reveals underexplored combinations of sensing modalities and model families and provides a structured foundation for analyzing and designing scalable, end-to-end data-driven systems in precision agriculture.

Compared to previous reviews that focus primarily on algorithms or crop-specific case studies, this survey integrates sensing infrastructure, data structure, modeling approaches, and analytical objectives within a unified engineering framework. By bridging sensing technologies and machine learning methodologies, the proposed taxonomy supports the design and evaluation of deployable, robust, and scalable agricultural systems.

Sensing modality: AI-enabled precision agriculture relies on heterogeneous sensing platforms that vary widely in spatial, spectral, and temporal resolution. UAV-mounted RGB, multispectral, hyperspectral, thermal, and LiDAR sensors provide high spatial detail for canopy structure, plant health assessment, and fine-scale monitoring [3,14]. Satellite platforms extend temporal and regional coverage, offering multi-temporal vegetation indices and spectral diagnostics used extensively for crop health and yield modeling [2]. Ground-based IoT sensors, including soil moisture probes, weather stations, and in-field cameras provide micro-environmental and crop-level observations that complement airborne and satellite sensing [15]. Together, these modalities form the multi-resolution sensing backbone of modern agricultural analytics.

Data type: The raw data produced by these sensing modalities differ in structure, dimensionality, and temporal properties. We categorize data types into five groups: (i) single frame 2D imagery, (ii) multi-temporal image sequences used for phenological monitoring and yield prediction [16], (iii) 3D point clouds derived from LiDAR or structure from motion photogrammetry [14], (iv) tabular sensor and environmental streams from IoT systems, and (v) fused multimodal datasets combining imagery, spectral indices, and ground measurements [17]. Each data type imposes different requirements on preprocessing pipelines, feature extraction, and model selection.

Model family: A wide range of machine learning and deep learning models have been applied to agricultural imagery and sensor data. Classical methods such as support vector machines (SVMs), random forests, and fuzzy rule-based systems remain competitive when datasets are small or interpretability is required. Deep learning models dominate imagery-driven tasks, including convolutional neural networks (CNNs) [18], residual networks (ResNet) [19], encoder–decoder architectures such as U-Net [20], region-based detectors including Faster region-CNN (Faster R-CNN) and Mask Region-CNN (Mask R-CNN) [21,22], and one-stage detectors such as you only look once (YOLO) variants [23,24]. Sequential models (long short-term memory (LSTM) [25], Convolutional LSTM (ConvLSTM) [26]) and hybrid architectures (CNN–SVM, CNN–LSTM, and stacking ensembles) further extend these capabilities. Recent advances in attention-based and transformer architectures further expand this model family dimension, offering alternatives to convolutional and recurrent networks for modeling long-range spatial and temporal dependencies in agricultural data.

Analytical task: The fi