UAV-Based Classification of Crop Phenological Stages Using Deep Learning(MDPI Drones 2026-08-17)

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Open Access Article

by
Ravil I. Mukhamediev

1,2,
Valentin Smurygin

1,
Liudmila Gorodetskaya

1,
Yan Kuchin

1,2,
Adilet Dauletuly

1,
Nursultan Kuldeyev

1,
Adilkhan Symagulov

1,2,* and
Irina Fedorovich

3

1

Institute of Automation and Information Technology, Satbayev University (KazNRTU), Almaty 050013, Kazakhstan

2

Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan

3

AgriSoft LLP, Almaty 050060, Kazakhstan

Author to whom correspondence should be addressed.

Submission received: 4 June 2026 / Revised: 5 August 2026 / Accepted: 9 August 2026 / Published: 17 August 2026

Highlights

What are the main findings?

• A ResNet18 model based on transfer learning was developed to classify BBCH-defined phenological stages of five crops from low-altitude RGB imagery acquired by UAVs.

• Using 11,489 images of sunflower, rapeseed, soybean, wheat, and barley, the crop-specific models achieved high accuracy on internal test sets, with a mean accuracy of 99.73% under the studied low-altitude UAV imaging conditions.

What are the implications of the main findings?

• The results support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture.

• BBCH-based labeling with both single stages and stage ranges can represent heterogeneous or transitional field conditions. However, broader deployment requires validation across independent fields, seasons, regions, and survey conditions.

Abstract

This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.

1. Introduction

The need to improve the sustainability and productivity of agricultural production in the face of climate change, resource scarcity, and increasing environmental demands is driving the transition from traditional technologies to precision agriculture (PA) [1,2] and smart agriculture (SA) [3]. A key component of these technologies is monitoring, which provides regular spatial and temporal information on the condition of crops, soil, and environmental factors to support decision-making (from work planning to differentiated resource application and outcome forecasting). In the modern literature, the most common monitoring tasks include the following: (1) determining the phenological stages of crop development [4], (2) assessing the water status of plants and water stress to support irrigation decisions [5], (3) monitoring the nutrient (primarily nitrogen) status of crops to optimize fertilizers [6], (4) identifying diseases and pests [7], (5) monitoring weeds for targeted protection [8], (6) crop mapping [9] and forecasting yields based on observation data during the season [10], (7) monitoring soil condition, where along with the water regime, quality/fertility indicators (for example, organic carbon content as an integral indicator) are becoming increasingly important [11], and for arid regions—(8) soil salinization as a factor of degradation and reduced productivity [12,13]. To address these challenges, modern agricultural monitoring utilizes remote, proximal, and ground-based sensing data, which differ primarily in the distance of the sensor from the observed object, spatial coverage, and level of detail. Remote sensing enables regular monitoring of large areas using satellite, aerial, and Unmanned Aerial Vehicle (UAV) platforms, while proximal and ground-based sensing focus on obtaining more detailed measurements near plants and from the field surface, making these approaches complementary in precision and intelligent farming systems [14,15] (Figure 1).

Figure 1. Key monitoring tasks in precision and smart agriculture using remote and proximal sensing.

This paper addresses the automatic classification of crop phenological stages using low-altitude UAV imagery, a task that is important for optimizing the timing of agronomic interventions and improving yield forecasting [16]. The proposed approach is intended for detailed phenological assessment of selected field zones identified during preliminary large-area monitoring using satellite imagery or higher-altitude UAV.

Traditional methods for determining phenological phases, based on visual observations by experts, are labor-intensive, limiting their use for monitoring large areas. The development of remote sensing technologies, including the use of satellite data and images from unmanned aerial vehicles (UAVs), has significantly expanded the possibilities for collecting information on crop conditions [4,17]. These data provide high spatial and temporal resolution and are widely used in agricultural monitoring.

In recent years, deep learning methods have played a key role in image analysis in agriculture. Reviews [18,19] show that deep learning models demonstrate higher accuracy compared to traditional image processing methods. Convolutional neural networks (CNNs) are widely used for plant image classification and remote crop monitoring [20,21,22]. Pre-trained architectures such as ResNet [23] and detection models of the YOLO family, which have been successfully applied in agricultural tasks [24], can achieve high accuracy even with limited data.

Despite the progress made, automatic classification of phenological stages remains a challenging task in practice. This is due to the high variability in plant appearance, the gradual nature of the transition between stages, and the limited availability of detailed labeled data. Crop heterogeneity further complicates the task: within a single field, plants at different stages of development may be present simultaneously.

In real-world settings, image labeling is often performed at the field level and reflects a mixture of phenological states. Therefore, this study uses an approach in which the label describes the dominant state of the plot and can correspond to either a single stage or a range of stages on the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale. This allows transitional field states to be represented explicitly during annotation.

Accordingly, this study investigates the use of deep learning for automatic classification of the phenological stages of five crops—sunflower, rapeseed, soybean, wheat, and barley—from UAV images labeled according to the BBCH scale, including both single stages and stage ranges. To achieve this goal, the following tasks were completed: dataset formation and labeling, image preprocessing, model development and training based on the ResNet18 architecture, classification quality assessment, and analysis of the obtained results.

The paper is organized into six sections. Section 2, Related Work, provides a focused overview of previous studies relevant to phenological classification of agricultural crops. Section 3 briefly describes the study area, initial data, and methods. Section 4 presents the experimental results, Section 5 discusses the results and limitations, and Section 6 summarizes the work and outlines directions for further research.

2. Related Work

Previous studies have addressed crop phenological classification using manual field observations, semi-automated proximal sensing, satellite time series, and image-based approaches, including UAV and ground-based imagery [14,16,25,26,27]. These studies demonstrate that phenological information can be derived from different sensing platforms and modeling strategies, but performance and transferability depend strongly on data type, spatial and temporal resolution, labeling strategy, and validation design.

2.1. Methods of Phenological Classification

For clarity, phenological classification methods can be grouped by the degree of automation and the type of data used (Figure 2). Manual methods include visual observations by experts in the field, the use of phenological diaries and scales, particularly the BBCH scale, as well as protocols for selective crop sampling [25].

Figure 2. Classification of methods for determining crop phenological stages: manual, semi-automated, and automated approaches.

Manual observations are biologically interpretable and can capture transitional states, but they are labor-intensive, subjective, and difficult to scale. Semi-automated methods combine visual observations with proximal or handheld measurements and determine the stage using selected features or rules [14].

Automatic approaches can be divided into methods based on remote sensing time series and methods based on near- or mid-range imagery. Time-series methods use spectral-index dynamics, threshold rules, seasonal-curve approximations, and machine learning models such as Support Vector Machines (SVMs) and k-Nearest Neighbors (kNNs) [4,28], as well as deep learning and transformer-based architectures [29,30].

Phenological normalization of the time axis can improve model portability [31]. However, satellite-based approaches remain limited by spatial resolution, cloudiness, and mixed pixels, especially in heterogeneous fields and small plots.

Image-based approaches determine phenology from plant morphology and canopy structure using ground cameras, PhenoCam systems, mobile imagery, or UAV data. PhenoCam studies combine CNN classification with temporal post-processing such as Hidden Markov Models to obtain robust daily field-state sequences [32,33]. UAV imagery provides lower temporal continuity but much higher spatial resolution, enabling plant- or row-level analysis and detection of within-field heterogeneity [16,34]. Image-based phenological tasks include scene classification [35], organ detection [24], and segmentation-based analysis [20].

2.2. Machine Learning Methods for Phenological Classification

Early automated phenological classification relied on manually designed spectral, textural, and canopy features followed by classical machine learning methods, including SVM and kNN [28]. Such methods can perform well under stable conditions, for example, in sugarcane stage identification from Sentinel-2 vegetation-index time series [36], but their transferability between regions and seasons is limited by differences in cultivars, management, weather, and phenological timing.

Recent studies increasingly use deep learning, attention mechanisms, and temporal models. For UAV orthomosaics, MaizeHT combines CNNs and self-attention and reaches 97.7–98.7% accuracy in maize growth-stage recognition [35]. ResNet-50 has been applied to rice growth-stage classification from ground images [37], while CNN-LSTM and CNN-GRU models are used when image sequences are available [38]. For PhenoCam imagery, DL classification combined with HMM post-processing improves temporal consistency under management events such as plowing, harvesting, and crop rotation [32].

2.3. Phenological Classification Using UAV Data and Deep Learning

For the present study, the most relevant works are those that classify phenological stages directly from UAV RGB or multispectral imagery using deep learning. Reviews confirm that UAVs are valuable for such tasks because they combine high spatial resolution, flexible survey timing, and operation below cloud cover [17,18]. However, adjacent phenological stages often differ only in subtle morphological details, so classification errors commonly occur between neighboring classes [39,40].

A further limitation concerns data and labeling. Many studies rely on local datasets collected in one region with a fixed sensor and survey protocol, which may overestimate performance because of hidden correlations in background, illumination, and management conditions. Consequently, practical deployment requires attention to domain shifts and stronger evaluation protocols such as leave-one-field or leave-one-season-out validation [41].

In Central Asia, including Kazakhstan, UAV-based phenological classification remains underrepresented despite the region’s agro-climatic diversity and the importance of grain and oilseed production. A relevant dataset for wheat, barley, and soybean in East Kazakhstan has been published using multispectral UAV imagery [42]. Related studies from Kazakhstan also show the potential of UAVs and machine learning methods for soybean plant and weed detection [24] and agricultural salinity mapping [13], but available datasets remain limited in crops, years, and survey conditions. Standardized BBCH labeling is therefore important for comparability and for representing mixed field states [25].

Overall, recent research highlights several persistent limitations:

• Weak separability of adjacent stages and continuous transitions between phases [36,38,40];

• Heterogeneous crop states within a field, which complicate the assumption of one stage per image [28];

• Limited availability of labeled BBCH datasets covering diffe