[1,2]Ribana Roscher

[1]Data Science for Crop Systems, Forschungszentrum Jülich GmbH, Wilhelm-
Johnen-Straße, Jülich, 52428, Germany

2]Remote Sensing Group, University of Bonn, Niebuhrstr. 1a, Bonn, 53113,
Germany

3]Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2,
Zürich, 8092, Switzerland

4]Photogrammetry & Robotics Lab, University of Bonn, Nussallee 15, Bonn,
53115, NRW, Germany

# Data-Centric Digital Agriculture: A Perspective

<EMAIL_ADDRESS>Lukas Roth<EMAIL_ADDRESS>Cyrill Stachniss
<EMAIL_ADDRESS>Achim Walter<EMAIL_ADDRESS>*  [
[  [

###### Abstract

In response to the increasing global demand for food, feed, fiber, and fuel,
digital agriculture is rapidly evolving to meet these demands while reducing
environmental impact. This evolution involves incorporating data science,
machine learning, sensor technologies, robotics, and new management strategies
to establish a more sustainable agricultural framework. So far, machine
learning research in digital agriculture has predominantly focused on model-
centric approaches, focusing on model design and evaluation. These efforts aim
to optimize model accuracy and efficiency, often treating data as a static
benchmark. Despite the availability of agricultural data and methodological
advancements, a saturation point has been reached, with many established
machine learning methods achieving comparable levels of accuracy and facing
similar limitations. To fully realize the potential of digital agriculture, it
is crucial to have a comprehensive understanding of the role of data in the
field and to adopt data-centric machine learning. This involves developing
strategies to acquire and curate valuable data and implementing effective
learning and evaluation strategies that utilize the intrinsic value of data.
This approach has the potential to create accurate, generalizable, and
adaptable machine learning methods that effectively and sustainably address
agricultural tasks such as yield prediction, weed detection, and early disease
identification.

###### keywords:

Digital agriculture, data-centric learning, data utilization

##  1 Introduction

Agriculture is at the forefront of the world’s most critical challenges,
including securing food, feed, fiber, and fuel, as well as addressing climate
change. Therefore, it plays a crucial role in achieving the United Nations’
Sustainable Development Goals, specifically SDG 2 (no hunger) and SDG 13
(climate action). It is responsible for 15%-25% of global greenhouse gas
emissions [1] and is susceptible to the impacts of droughts and floods [2].
The decline in biodiversity and ecosystem services further highlights the need
for improved field management practices, fertilizers, and pesticides [3].
Traditional agriculture, which relies on farmers’ intuitions and experiential
decision-making, is increasingly challenged by consumers’ and citizens’
expectations of sustainable agriculture [4]. Modern agriculture, particularly
digital agriculture, has emerged as a crucial approach to address these
challenges. Digital agriculture utilizes various technologies such as sensors
and robotics and primarily relies on data-driven methods to optimize
agricultural practices [5]. The main aim is to inform decision-making
regarding optimal and sustainable resource allocation, appropriate amounts,
places, and timings of measures. Farmers, researchers, and policymakers can
increasingly access diverse sources of observational and simulation-based
data, such as soil nitrogen availability, rainfall forecasts, crop development
and yield potential, disease risks, and weed or pest densities. Insights
derived from this data can be translated into actionable responses, leading to
enhanced sustainability in agricultural management practices [6]. The
transformative potential of digital agriculture is driven by the synergistic
integration of digital technologies, along with access to data through the use
of new data sources, efficient and targeted data collection, and the ability
to optimize data quality and usage. This perspective emphasizes the often-
overlooked importance of data in digital agriculture and introduces the
concept of _data-centric digital agriculture_ with a specific focus on crop
production. It explores promising developments in data science, sensor
technologies, and robotics that are crucial to realizing the full potential of
data.

##  2 From Early Precision Agriculture via Farm Management Systems to
Agricultural Robotics

Over several decades, agriculture has transformed significantly, with data and
technology becoming central elements [7, 8, 9, 10]. These innovations have
reshaped farming management and the field from traditional to modern
agriculture. In this section, we will elaborate on this evolution, from the
beginnings of precision agriculture through the rise of digital agriculture to
agricultural robotics. We will discuss how the combination of data,
technological advancements, and innovative farming practices have collectively
changed the agricultural landscape, leading to more informed decision-making
and raising exciting questions about farming’s future.

The nature and variety of data sources in agriculture have evolved and
expanded significantly (as detailed in Box 1). Early agricultural data was
confined to manual records and observational notes, often documented in books
and logbooks. While valuable, these records lacked the depth and scope of
today’s data sources. Modern agriculture has seen a proliferation of data
types, including experimental data from controlled trials and research
adhering to strict scientific protocols. Moreover, non-experimental data
collected from real-world farming scenarios have become invaluable due to
their practical applicability. Simultaneously, the integration of various
sensors and technologies into agriculture has increased. These technologies
range from soil and weather sensors to drone-based remote sensing and
satellite imagery, enhancing the granularity and real-time aspect of data
acquisition. The trend towards a richer and more diverse range of data sources
is expected to continue. In combination with digital technologies, it promises
to deepen our understanding of agricultural systems. This evolution not only
reshapes the data landscape in agriculture but also highlights the potential
for more precise, sustainable, and efficient farming practices.

Figure 1: In this Venn diagram, we visually represent the evolution of
different agricultural paradigms over time as technology usage and available
data increase. In the past, traditional agriculture was dominant, but
currently, the focus has shifted to digital agriculture. In the future, we
expect data-centric digital agriculture to become increasingly important. It
is worth noting that different paradigms can coexist, and a blending of
different practices takes place as they evolve. This underlines the dynamic
and interconnected nature of modern agriculture.

Alongside this data evolution, the increasing availability of advanced digital
technologies and data sources, coupled with the potential for improved data
usage, has led to new paradigms, as outlined in Fig. 1. Traditionally, farmers
based their field management decisions on qualitative observations at the
field-level. In the mid-80s, precision agriculture applications emerged and
gained traction later in the 90s [11, 12]. Two developments were fundamental
prerequisites for their establishment: (1) The ability to resolve in-field
heterogeneity in growth conditions, such as plant-available nutrients in the
soil, and growth status, such as plant biomass, and (2) the ability to apply
spatially resolved treatments to these heterogeneities, for example, variable
rate application of nutrients. The broader field of digital agriculture has
emerged more recently due to advances in digital technologies such as data
science, machine learning, and robotics [5, 6]. While precision agriculture
can be considered a subset or component of digital agriculture, precision
agriculture was the original approach that laid the foundation for digital
agriculture. It paved the way for integrating digital technologies and data-
driven practices into agriculture and enabled the development of more
comprehensive and advanced digital agriculture solutions (see Fig 2).

In the field of digital agriculture, phenotyping has become a significant
focus area [13, 14, 15]. Phenotyping in agriculture involves the detailed
measurement and analysis of observable plant traits. These traits encompass a
wide range of characteristics, including growth patterns, stress responses,
and crop yield predictions. In the past, phenotyping relied on qualitative
assessments of these traits. However, advancements in sensor technology and
data science strategies, particularly in imaging and remote sensing, have
transformed phenotyping into a data-rich and quantitative field. Through the
use of advanced sensor technologies, high-throughput phenotyping allows for
precise and detailed measurements of plant traits. This brings a new dimension
to digital agriculture by providing real-time, data-driven insights into plant
health and performance.

Figure 2: Unmanned aerial vehicles (UAVs), robots, and smart agricultural
machines integrate monitoring, phenotyping, spot-spraying, and chemical-free
weeding. Image courtesy: PhenoInspect GmbH, EarthSense, Flourish Project,
Naio, Verdant, Carbon Robotics.

The agricultural transformation extends beyond just data and technology.
Advancements in digital technologies significantly impact farm management and
crop cultivation methods. Practical solutions such as crop diversification,
along with new field arrangements and smaller individual field sizes, can be
important steps to address the multiple challenges of agriculture. Smaller,
homogeneous crop patches can benefit biodiversity, pest control, and soil
fertility [16]. Experiments with 3-m-wide strip cropping systems [17],
flowering strips [18], hedgerows, contour plowing, and other biodiversity-
friendly land-use practices [19] have shown increased ecosystem services.
However, diversified field arrangements require an appropriately high data
resolution, which could be achieved with high-quality field sensor data and
remote and proximal sensing-based devices. Moreover, mechanistic and machine
learning-based agroecosystem models simulating crop growth, development, and
soil processes in response to environmental conditions and management
practices will become more crucial [20, 21, 22, 23].

,

Figure 3: Innovative cropping systems such as patch cropping focus on a high
plant diversity in small areas, fostering that crops can grow where they find
optimal conditions for growth in the soil. In this way, chemical synthetic
pesticides and fertilizers can be reduced. The image shows the patchCROP
experiment at Leibniz Centre for Agricultural Landscape Research (ZALF)
Müncheberg, with permanent tracks and a GPS-controlled control system that
enable targeted and precise trials. Image courtesy: Hendrik Schneider, Marten
Schmidt/ZALF

If fields are managed in patches according to their overall natural boundary
conditions (soil properties, sun exposure, and other microclimatic factors),
choosing the ideal cultivar from the available spectrum becomes even more
critical (see Fig. 3). Similarly, segmenting an entire field or farm into
appropriate patches for crop rotation [24] would be a significant challenge.
These challenges can be effectively addressed with the capabilities of various
sensing technologies and data processing and analysis. For example, multi-year
yield assessments can be conducted to determine the appropriate patches for
the field. These assessments can be done using combined harvester data or
retrospectively using satellite-based yield estimations [25]. Managing fields
with irregular sizes is only feasible if tractors and agricultural machinery
are GPS-controlled or even autonomous to reduce labor. Coupling these
management procedures with controlled-traffic farming, where tractors
repeatedly use the same lanes in the field, can minimize the effects of soil
compaction as much as possible [24].

Despite four decades of development in digital agriculture, it remains
confined to a few use cases, raising questions about its broader adoption [7].
One observation we make is that although there are numerous machine learning
methods and statistical tools available, the lack of widely applicable models
is hindering the progress of digital agriculture. Currently, data-driven
autonomous solutions are primarily employed in tasks related to established
crops and varieties that are typically managed using large machinery [26].
However, to reduce environmental impact and foster stronger connections
between producers and consumers, there’s growing interest in envisioning a
future with a small fleet of lightweight robots and intelligent assistants
supporting farmers. Such solutions have the potential to efficiently manage
diverse crops on small plots, potentially revolutionizing sustainable
cultivation practices.

Another critical observation we make is that the development of field
management techniques and machinery in agricultural practices has not kept
pace with the advancements in sensing technologies. A prime example of this is
mineral nitrogen (N) fertilizer, one of the most crucial farming inputs, as
elaborated in Box 2. Commercially available sensing products now possess
precision that surpasses the capabilities of most conventional fertilizer
spreaders, leading to discrepancies between sensing and application, which may
be exacerbated by the shift from proxy measurements like vegetation indices to
single plant and organ detection techniques [27, 28, 29]. These advancements
hold promise for tasks such as weed control and pest management. However,
despite research demonstrating the feasibility of site-specific treatments,
commercial systems are limited for large-scale operations. Consequently, the
recent emphasis in digital agriculture has been on increasing the efficiency
of large-scale farming machinery [30, 31]. This focus on ’economies of scale’
has resulted in larger farms being managed by the same workforce as before
[26]. Yet, it is essential to recognize that ecological improvements cannot be
solely achieved by reducing human inputs; conserving other natural resources
is equally critical.

Based on these observations, we state that the future of digital agriculture
depends on several critical factors, but above all, the trade-off between
costs and benefits must improve. One challenging yet readily attainable goal
we can strive for is to develop agricultural models that are accurate and
provide a high generalization power and adaptability to be applicable to
various agricultural scenarios and tasks. We argue that achieving this goal
involves improving data creation, selection, curation, and utilization,
potentially through different machine learning paradigms. Further, it is
essential to understand better which data holds high value and quality for
building such models. By improving the models, the trade-off between the cost
and benefit of digital agricultural systems can be improved, and incentives
can be created to drive adoption.

##  3 The role of data in digital agriculture

Figure 4: The scientific process in traditional and digital agriculture. The
various types of modeling are highlighted in dark purple (data-centric), blue
(model-centric), and red (hypothesis-centric). The scientific method cycle
illustrates the feedback resulting from actions and implications to novel
scientific questions.

In digital agriculture, we observe that the significance of data quality is
often underestimated, and the cascading effects of data quality throughout the
entire digital agricultural ecosystem are frequently disregarded [32]. To
define the criteria for high-quality data in this context, we elaborate on
several key dimensions. However, it is important to note that these dimensions
must still fully evolve and be proven in practice to have a clear
understanding. We characterize high-quality data in digital agriculture by
diversity and completeness. It encompasses a wide range of samples and
captures the nuances of different agricultural scenarios. This completeness
ensures that all necessary information is available and strengthens the
resilience of machine learning models by avoiding critical gaps. Moreover,
data accuracy is crucial as it measures how well the recorded values align
with the true or expected values. Connected to this, we identify unbiasedness
as necessary, which considers the presence or absence of systematic errors and
consistent deviations. In the diverse landscape of agricultural data, for
example, sampling bias happens when certain classes are overrepresented in the
collection of data samples, such as data on diseases, which can result in
distorted findings [33]. Further, we consider data consistency critical in
digital agriculture to maintain the integrity of properties or attributes
across different data sets. This is especially important when merging
agricultural data sources to ensure seamless matching for different use cases.
Finally, we characterize high-quality data by its relevance to the intended
agricultural task. It closely aligns with the requirements and context of the
agricultural landscape, ensuring that models trained on such data are accurate
and adaptable to different agricultural conditions.

In this section, we describe the use of data in traditional and digital
agriculture and discuss three different paradigms in agriculture that differ
in their modeling process and the integration and usage of data: hypothesis-
centric traditional agriculture, model-centric digital agriculture, and data-
centric digital agriculture (see Fig. 4).

Hypothesis-centric traditional agriculture. Traditional agriculture primarily
adheres to a hypothesis-centric approach (red cycle in Fig. 4), where farming
practices are predominantly guided by qualitative assessments and
observational data collected on-field. Farmers base their decision-making on
long-established practices rooted in historical knowledge, non-experimental
data, and qualitative assessments. This process is influenced by policies that
are derived from experimental data. The data collected in experiments follow
specific requirements for testing and evaluating a hypothesis. The objective
in this context is often to test and validate existing agricultural
hypotheses, seeking a deeper understanding of causal relationships within a
particular environment.

Actions in a policy-driven system can occur for two reasons. Firstly,
practitioners may optimize their farming system within the existing system
boundaries, such as reallocating limited resources. Secondly, a controlling
instance may adjust ineffective policies and recommendations, redefining
system boundaries. Consequently, implications in policy-driven systems are
reactive, and proactive actions by farmers are not encouraged (Figure 5,
example ’System boundaries’).

Model-centric digital agriculture. In digital agriculture, farmers’
qualitative observations are complemented by non-experimental and experimental
data collected by remote and proximal sensors. Depending on the application
and the objectives, mechanistic, machine learning-based, or hybrid models are
preferable [34, 35, 36]. Moreover, either non-experimental data only or both
data types are used. So far, digital agriculture, particularly machine
learning for agricultural applications, has predominantly been model-centric
and focused on the design and evaluation of models [37, 29] (blue cycle in
Fig. 4). These approaches primarily aim to enhance the accuracy and efficiency
of the models themselves while treating the data as a static benchmark instead
of a dynamic representation of the application. Despite the growing
availability of agricultural data [38, 39, 40, 41] and advancements in
methodologies, there is currently a point where many established machine
learning methods achieve comparable levels of accuracy and face similar
limitations in their applicability [42].

While the common belief is that more data will lead to better models, building
such models requires immense computational resources and leads to significant
environmental impacts [43]. However, limiting the size of datasets can lead to
reduced generalization ability and incomplete information and insights. In the
agricultural domain, plant development is influenced by complex interactions
between genetic information and the environment. Given the vast number of
potential environments and genotypes, and the lack of a well-established
relationship between observable traits, genetic information, and the
environment, it is crucial to have a comprehensive dataset that encompasses
sufficient information. However, storing such a large amount of data is
impractical due to the sheer volume of information generated in the digital
agriculture domain. Storing every observed data point becomes increasingly
unwieldy and may not align with the practical goals of data analysis.
Essentially, trying to archive and analyze every piece of data will inevitably
lead to an inefficient allocation of resources and computational efforts [44].

A farmer’s actions using model-centric approaches impact the fields to which
they are applied. However, the models have no feedback loop regarding their
impact at the field or farm levels. Consequently, the impacts in model-centric
systems are confined to the field boundary, and any changes are expected to
occur only within that boundary. Transformation at the landscape level is not
promoted (Figure 5, example ’Field’).

Data-centric digital agriculture. Data-centric digital agriculture strongly
emphasizes data, utilizing systematic and algorithmic approaches to identify
and utilize rich and high-quality datasets for building models. This involves
a comprehensive evaluation process to ensure that the model built on the
dataset performs optimally for the intended task. Here, the data is optimized
in a cycle, that means with several feedback loops and not treated as a static
benchmark, but evolves jointly with the model development. Data-centric
approaches address the mentioned challenges of model-centric digital
agriculture and identify valuable and high-quality data to build accurate,
generalizable, and adaptable models [32, 45]. Therefore, data-centric digital
agriculture is a logical next step to improve the widespread applicability and
adoption of agricultural models.

Data-centric approaches have the potential to revolutionize the agricultural
landscape by fostering the creation of accurate, generalizable, and adaptable
models. By focusing on the data and its quality and recognizing and exploiting
the dynamic nature of data in representing the agricultural environment, we
can overcome the current limitations of machine learning models, which are
restricted to small use cases and narrow application domains. Furthermore, the
targeted use of high-quality data and data-centric approaches allows for a
comprehensive analysis of the models and the detection of shortcomings, which
can be corrected by constant feedback loops. If policies permit, farmers can
adjust their farming systems in real-time using predictions generated by a
model built on high-quality data. Both policymakers and farmers can contribute
newly collected data to the system, which enables insights to be gained across
different systems and boundaries (see Figure 5, example ’Landscape’).

Figure 5: The transformative potential of data in agriculture. A shift from
field and system boundaries to the landscape (green) can be achieved by moving
from hypothesis-centric traditional agriculture to data-centric digital
agriculture.

##  4 Looking into the Future: A Data-centric Digital Agriculture

In this section, we explore the integration and use of data at every stage of
the agricultural scientific process (see Fig. 4) and highlight its central
role in shaping agricultural research and practice. The section begins with
data collection and curation, guided by the scientific question. It continues
to model-centric agriculture, which includes important aspects such as
designing models, optimizing parameters, and establishing evaluation metrics
to assess model performance. Learning accurate models that generalize well and
can adapt to new environments requires the process to be iterative, driven by
insights and feedback gained from data and its interaction with the model.
While these advancements can significantly improve the modeling process, they
also present challenges, including the accessibility of data and
sustainability aspects. Our elaborations will show the transformative
potential of data-centric digital agriculture, providing insight into its
crucial role in overcoming obstacles and shaping the future of agricultural
science.

###  4.1 Data collection and curation

####  4.1.1 Novel sensor and robotic technologies

Sensing in agriculture often involves combining position sensors such as
GNSS/GPS with other modalities, including remote sensing technologies like
satellite or UAV imagery, environmental sensors that measure factors like
temperature and humidity, and sophisticated instruments for assessing soil
properties [46, 47]. The effectiveness of these modalities strongly depends on
the specific quantity being estimated. While the shoot of a plant can be
easily sensed using cameras, measuring solid properties, fluxes, genetic
information, or root growth poses significant challenges. In many cases, only
proxies can be measured, and the correlation to the desired quantity may be
questioned. However, all of this information is relevant for describing the
field’s current state and making future predictions.

Especially in real field environments, current below-ground sensing systems
have severe limitations. Often, it needs to be combined with domain knowledge
of underlying processes and structures in the plant system, side-specific
calibration, and complex soil models [48, 14]. Similar limitations can be
observed in some above-ground sensing techniques. Cameras with more spectral
bands provide relevant information about plant traits, but hyperspectral
sensing, particularly, still faces serious calibration issues when applied in
the field under real-world conditions. Nevertheless, this technique is crucial
for health monitoring, especially related to diseases [49, 15].

Integrating new, affordable, and powerful sensors, especially those with high
spatial resolution and spectral capabilities, along with integrating different
modalities, offers an opportunity for advanced applications in agriculture
[50, 23]. However, this potential still needs to be fully realized. One
example is the combination of data from high-resolution above-ground sensors
with low-resolution below-ground sensors. This combination allows for
extracting more relevant traits, enabling more accurate modeling of the
current situation. Additionally, this approach enables the semi-automatic
generation of weakly labeled data across different modalities. One strategy to
explore for this is transfer learning (see also 4.2.1). Transfer learning
involves leveraging knowledge gained from high-resolution sensors to enhance
the performance of models using simpler, low-resolution sensors. For instance,
high-resolution data can generate semi-automatic, weakly labeled data for low-
resolution sensors. This concept is also connected to surrogate modeling, as
the distilled knowledge from high-resolution sensors aids in training models
for low-resolution counterparts.

Regarding robotics, several developments in agricultural operations will
impact data acquisition and its utilization. Firstly, robots for data
acquisition are often aerial platforms that collect high-resolution data at
frequent intervals [51, 50, 14]. The collected phenotypical data provide
valuable information for breeding applications and monitoring the current
status quo [14, 13]. Secondly, it is clear today that we will witness in the
near future an increase in robots performing specific management tasks, such
as weeding, often motivated by the desire to reduce agrochemical usage. By
combining intervention actions like weeding with sensing and repeated
operations, more detailed data can be obtained, allowing for the monitoring of
treatment effectiveness on a larger scale [52, 53, 54]. Thirdly, robots have
the potential to monitor and maintain field arrangements that are currently
not considered at relevant scales, such as multi-cropping (patch-, strip-,
inter-, pixel-cropping) [24, 17, 16]. The usage of robots in these
arrangements is expected to provide new data and insights. Furthermore, the
question of whether we will continue to see an increase in the size of
agricultural machines or a shift towards fleets of smaller robots capable of
performing multiple tasks is still under debate. However, using numerous small
systems will impact data acquisition, as they will provide more distributed
sensing [9]. Similar considerations apply to the development of robots that
assist humans in specific operations, such as handling delicate fruits in
orchards. In these cases, more detailed and process-related data can be
captured.

####  4.1.2 Active sensing

Agricultural environments can be vast, cluttered, and filled with occlusions,
making it difficult to sense relevant quantities of interest. Passive data
recording has limitations as important information may not be directly
observable or a large number of observations may be needed to cover fields or
orchards densely. Therefore, robots need to change viewpoints, move to
specific locations, or move obstacles to optimize data acquisition. Active
information gathering involves controlling a mobile system to obtain higher-
quality observations that are more relevant to agricultural tasks or have
higher accuracy. Existing approaches typically focus on optimizing sensor
coverage or use sample-based viewpoint selection. Information-driven
approaches are a fundamental solution but can be computationally challenging.
For real-world applications, approximations are needed to make information-
driven approaches applicable, e.g., for exploration [55].

One promising approach to enhancing data acquisition in agriculture is through
active sensing [56, 57, 58, 59]. Active sensing combines recording data with
active learning and selective labeling paradigms [60, 61]. Selective labeling
becomes particularly valuable in the dynamic and diverse agricultural
landscape, where field and environmental conditions can vary significantly. It
involves carefully choosing specific instances or features in the data for
detailed annotation, which can significantly enhance the learning systems used
for scene analysis in agriculture. Further, the active sensing framework can
employ techniques like uncertainty quantification methods [62] and explainable
machine learning [63]. These techniques provide valuable insights into the
quality of the collected data and shed light on the decision-making processes
of the models. By adopting approximative information-driven approaches that
integrate data acquisition strategies and labeling needs, we can create models
that are accurate and built on selectively collected, high-quality data. In
the agricultural context, this means that the models can better adapt to the
nuances of real-world conditions, leading to more effective and reliable
outcomes in decision-making processes for farmers.

####  4.1.3 Data generalization and specification

In the field of data collection and curation for agriculture, the traditional
’one-size-fits-all’ approach is already being replaced by a more nuanced
strategy [26, 9, 12, 50, 10]. The traditional approach aimed to collect broad
data that could be applied to various agricultural scenarios to achieve
generality. However, this approach often lacked the specificity to address
unique challenges in different regions or crops [64, 65]. In contrast, the
modern agricultural landscape requires more specific data, which is made
possible by precision agriculture tools and sensors. Moreover, these
technologies have become more accessible and affordable [10]. Moving forward,
it is necessary to balance the seemingly contrasting principles of
generalization and specification.

To address this challenge, adaptable data strategies and, with them, adaptable
models appear to be a promising approach. This transition requires a shift in
how we view data - from a static, homogeneous resource to one that can be
customized and refined to solve specific agricultural tasks. These strategies
involve continuously gathering data and adjusting data collection methods and
parameters to align with changing needs and tasks. They also rely on
approaches that can utilize data and its quality information to adapt existing
models for specific applications. However, several prerequisites must be met
to effectively utilize adaptable data strategies.

Firstly, the data should be highly granular and have a clear structure that
accurately reflects agricultural conditions. This allows for the extraction of
detailed insights specific to different agricultural domains. Additionally,
the data should be high-quality, meaning it should be complete, diverse,
accurate, unbiased, consistent, and relevant, among other potential criteria
[32, 66]. Several methods show promise for creating (i.e., generating or
collecting) and curating high-quality data. If a model is already available,
it can be utilized to identify new data that improves and adapts the model
(see Sec. 4.2.1), as well as to detect less accurate or irrelevant data,
thereby enhancing the quality of the dataset. If no model is available,
incorporating data-only techniques such as data quality assessments [10], data
sampling strategies [67, 68], data augmentation with real and synthetic data
[69], and data cleaning is crucial to ensure that the data remains a valuable
resource for agricultural applications. The timeliness of the data and the
ability to continuously update and refresh it are also essential requirements.
Agricultural systems are dynamic, and the data should reflect these ongoing
changes. Regular data updates keep models and decision support systems
relevant and accurate. Here, edge computing, with its ability to process data
near the source of data generation, can significantly reduce latency and
enhance real-time decision support [70].

Secondly, the data should be well-documented and tagged, providing metadata
that describes its source, context, and quality [71]. Rich metadata is crucial
for proper data curation and interpretation, as it helps ensuring data
reliability and integrity. A significant development in this area is the
emergence of foundation models, which are built on a vast amount of data from
multiple domains and modalities, with the aim of adapting their knowledge to
specific applications [72]. The transition towards adaptable data strategies,
combined with foundation models, can potentially drive the next phase of
digital agriculture, offering tailored solutions for various agricultural
challenges. However, there are still open questions regarding their
generalization capabilities, superiority over specialized models, ability to
provide explanations and uncertainty measures, and inherent biases.

Furthermore, adaptable data strategies depend on open data standards and
interoperability, which facilitate the integration of diverse datasets from
various sources and formats [71]. This interoperability allows for the
harmonious fusion and utilization of data from different regions, sensors, and
crops to address unique challenges (see also Sec. 4.4).

###  4.2 Modeling strategies and machine learning

####  4.2.1 Model transfer

In the dynamic agricultural environment, model transfer strategies provide
exciting opportunities to foster adaptability [73]. These techniques primarily
focus on transferring knowledge and insights, often in the form of data, from
one task or domain to another [74]. By doing so, they maximize the use of
existing models and minimize the need for extensive data collection in the
target domain.

Several approaches to transfer learning strategies can be employed in digital
agriculture [75, 65, 76, 77]. Instance-based transfer learning involves
identifying relevant data samples from both domains that can be transferred to
each other. This process allows models to adapt to and generalize better in
new agricultural environments. Feature-based transfer learning focuses on
migrating relevant features from one agricultural setting to another. For
example, it can adapt features learned from one crop variety to enhance
predictions for a different variety. Model-based transfer learning, often
combined with a foundational model, involves using the knowledge of an
existing model that is fine-tuned for a target application domain. Relation-
based transfer learning can be used when shared relationships exist between
agricultural systems. For instance, a model trained to optimize management
strategies for one crop can leverage its understanding of environmental
factors to benefit other crops, improving decision support and reducing the
need for extensive, crop-specific data.

Acquiring high-quality, domain-specific data in the target task domain remains
crucial for successful model transfer. In addition to collecting and curating
new data, active learning and self-training strategies focus on a more
efficient data acquisition [78]. Active learning involves selecting the most
informative data points, with human input for potential labeling. On the other
hand, self-training allows the model to refine itself without human
interaction, but it runs the risk of including inaccurate data. However,
determining a suitable selection criterion for samples is still an ongoing
research question [79].

To date, research in this field has mainly focused on deep machine learning,
with a limited number of use cases. These investigations have primarily taken
a model-centric approach, analyzing pre-trained model architectures originally
designed for non-agricultural domains [80, 75, 81]. However, there are still
important questions regarding the identification of valuable agricultural data
for foundation models and data that can be effectively transferred. This
challenge is amplified by the constraints of data collection in agricultural
contexts, highlighting the need for precise and efficient data selection.

####  4.2.2 Privacy-preserving models

Integrating data-driven models, robotics, and emerging technologies such as
edge computing in agriculture brings technological advancements and raises
critical ethical considerations [82, 83]. Privacy and fairness are among the
primary ethical concerns that require careful attention during the development
and deployment of these technologies. Furthermore, it is necessary to ensure
an unbiased data collection strategy and examine unbiased algorithms to ensure
that they do not unintentionally disadvantage certain groups of farmers.

Privacy-preserving machine learning techniques have been developed to tackle
these ethical concerns. These techniques protect sensitive data while enabling
effective model training and inference. This is particularly important when
working with agricultural data containing personal, financial, or proprietary
information. Methods such as secure multi-party computation or federated
learning can be employed to ensure data privacy in digital agriculture [84,
85, 86].

However, there is a trade-off between the benefits of open data and knowledge
sharing and the need to protect sensitive agricultural data. The challenge is
to find the proper balance or develop hybrid approaches that preserve privacy
while advancing the field of digital agriculture. Striking this balance is a
technical challenge and a moral and societal imperative as we navigate the
ethical landscape of an increasingly digitized and data-driven agricultural
sector. These considerations are particularly relevant in survey data
collection, where maintaining the confidentiality and privacy of respondents
is paramount.

####  4.2.3 Sustainable machine learning

Especially in the field of digital agriculture, sustainability is gaining
recognition [4, 87, 88, 6]. As the global community becomes more aware of its
critical importance, it is essential to consider not only how digital
technologies can contribute to sustainable agriculture but also how these
technologies can be designed sustainably [89, 43]. In the computer vision and
machine learning communities, promising directions are known as scalable
machine learning, efficient machine learning, tiny machine learning, or frugal
machine learning [90, 91]. These directions are of particular interest as they
parallel the emergence of generally large foundation models. In digital
agriculture, sustainability has several facets and concerns the hardware (such
as sensors, robots, low-power processors, and energy-efficient GPUs), the
data, and the methods. Existing systems are mostly model-centric and focus on
the energy efficiency and computational complexity of methods by designing
them to be light-weight, transferable, scalable, and reusable [92, 93, 94].
Most works attempt to immediately handle the vast amount of data, treating it
as static and disregarding the potential to guide model building through data
collection and curation, including selection. To design solutions that align
with local agricultural and environmental contexts, interdisciplinary
collaboration between agricultural experts, environmental scientists, data
scientists, and machine learning engineers is necessary. By building on
existing machine learning models, tailoring them effectively to the specific
needs of a region or crop, and complementing the existing data with well-
conceived data collection and curation, sustainability can be enhanced. This
approach avoids the creation of complete datasets and the building of models
from scratch. Generalization and specialization can be performed together,
supporting sustainability (Sec. 4.1.3). Furthermore, a core challenge is
striking a balance between an open data culture and the responsible use of
data. It is also crucial to ensure that the benefits of machine learning are
accessible across different agricultural systems, from small-scale farms to
large agribusinesses.

###  4.3 Model evaluation and deployment

####  4.3.1 Evaluating the applicability of agricultural models

Comprehensive evaluation strategies in agriculture are necessary to ensure the
accuracy of models, but also generalizability and adaptability to assess the
performance across various agricultural scenarios, encompassing different
crops and environmental conditions [95]. Previous research in this field has
mainly focused on traditional machine learning evaluation metrics for
accuracy, including overall accuracy, precision, recall, and F1 score.
Additionally, metrics like mean absolute error and root mean square error have
been used.

So far, evaluating the generalization ability is mainly done by cross-
validation with random partitions of a fixed data set into training and
evaluation set [96, 97]. The results on different evaluation sets are
averaged. However, to understand a model’s performance comprehensively, it is
crucial to move beyond aggregate performance metrics and focus on fine-grained
non-random subgroup analysis [98, 99]. This involves evaluating the model’s
performance across attributes such as crop varieties, environmental factors,
and geographical locations. Fine-grained subgroup evaluation provides deeper
insights into the model’s limitations and helps address potential biases or
disparities in its performance.

In addition to subgroup analysis, evaluating the area of applicability of the
model is becoming increasingly important [100]. Area of applicability refers
to understanding where the model performs well and where its performance may
decline. This includes testing the model on data points within and outside its
training distribution. By systematically exploring the boundaries of the
model’s applicability with carefully designed evaluation sets, scenarios can
be identified in which the model may exhibit uncertainty or lower performance.
This approach is particularly valuable in digital agriculture, where diverse
and evolving conditions necessitate a thorough understanding of a model’s
capabilities across various agricultural contexts.

Agricultural models, mainly those built on a limited number of labeled
samples, face challenges associated with data shortcuts and spurious
correlations that models may unintentionally rely on. Therefore, evaluation
data should reflect diverse real-world settings to check whether the model’s
performance is not overly reliant on specific characteristics of the training
data [83]. If these characteristics are causally wrong, this is also known as
the Clever Hans effect [101, 102]. Data ablation methods that systematically
remove or alter portions of the input data to analyze the impact on a model’s
performance can be employed to identify potential model shortcuts and prevent
the decision-making process from relying on such spurious correlations [102].

In recent years, generative models have become powerful tools in digital
agriculture. They can generate synthetic data, augment limited labeled
datasets, and serve as data-driven growth models [103, 22]. However,
evaluating these models can be challenging due to the lack of reference data
for direct comparison. Evaluation strategies used in the computer vision
community may not be applicable in agriculture, as they have different
objectives mostly related to perceptual quality. As a result, evaluation
strategies for generative models often involve proxy tasks, such as plant size
prediction or assessing the realism of generated images. With the increasing
use of generative models in agriculture, it is crucial to develop robust
evaluation methodologies that address the specific challenges of synthetic
data generation. This will ensure the reliability and effectiveness of these
models in diverse agricultural contexts.

####  4.3.2 Uncertainty quantification and explainability machine learning

In the dynamic landscape of digital agriculture, the reliability and
trustworthiness of model results are of great importance for informed
decision-support [104, 85]. Two critical elements in this field are
explainability and uncertainty, which intersect and have significant
implications for agricultural practices [105]. Explainable machine learning
combines domain knowledge with interpretation tools that present model
components and the decision process of the model in a human-understandable way
[104, 102]. At the same time, uncertainty encompasses potential variability in
results due to uncertainties in agricultural data or knowledge gaps in the
model [106, 62]. Research in explainability and uncertainty offers
transformative opportunities for robust model evaluation, a deeper
understanding of the modeling processes, and actionable insights from
agricultural data. However, ensuring the reliability and calibration of
uncertainty estimates in machine learning models remains a key challenge in
data-centric digital agriculture [107, 108]. Inaccurate uncertainty
estimations make it challenging to identify out-of-domain scenarios, which
means unknown environments [109] and lead to unexpected decisions [110, 111],
creating a risk that is difficult to calculate.

While research has explored ’planning under uncertainty’ and ’decision making
under uncertainty’, these approaches assume the existence of uncertainty
estimates or estimate these uncertainties independently of explanations for
the decisions and the origin of the uncertainties. Until now, explainable
machine learning and uncertainty quantification have rarely been considered
jointly. Moreover, to obtain reliable results for decision-support in digital
agriculture, it is crucial to not only quantify uncertainty but also assess
and calibrate the models [112, 107]. Calibration of model outputs is also
essential for meaningful interpretations and explanations that support
agricultural interventions and decision support. Efforts have been made to
distinguish between model uncertainties and data uncertainties, which aids in
identifying the sources of uncertainty [113, 114]. However, combining
explanations of specific decisions with information about the responsible
components in the input and model can facilitate targeted model improvement
[115]. In digital agriculture, the iterative integration of uncertainty and
explainability can guide the data collection process through active sensing
and active labeling, enhancing the overall quality and usefulness of the
collected data.

In this context, it is highly important that the expectations of the domain
expert are clearly stated and the boundaries of possible explanations are well
understood (see also confirmation bias, [116]). Furthermore, as already
pointed out in [63], it is mandatory for any application to have domain
experts involved to arrive at domain-specific explanations and not just
machine learning-derived interpretations.

###  4.4 Fostering data accessibility and adoption

The FAIR principle is crucial in ensuring open access to high-quality research
data. Data is considered FAIR when it is findable / discoverable, accessible,
interoperable, and reusable. There is a growing trend among funding agencies,
as demonstrated by the Bill & Melinda Gates Foundation [117] and the Science
for Humanity’s Greatest Challenges (CGIAR, [118]), to require compliance with
this principle.

However, the utility of a collected dataset or a trained model published in
scientific papers might be limited to academic purposes and may not translate
into practical applications. Bridging this gap demands additional efforts. A
promising example can be drawn from the widespread availability of
meteorological forecast data, such as that provided by the Deutscher
Wetterdienst [119], which can serve as a template for similar implementations
in agricultural forecasting.

Beyond ensuring open access to data, the digitization of agricultural
practices requires practitioners to be motivated and willing to adopt new
technologies. This necessitates a concerted effort in policy development,
farmer education [120], and showcasing the economic benefits of these
technologies to the broader society [121]. Incorporating feedback loops into
the agricultural landscape system, crucial for continuous model adaptation,
relies on farmers sharing their data. Data sharing can either be facilitated
by policies coupling this to subsidies or by farmers perceiving themselves as
the primary beneficiaries of data sharing [122]. Facilitation by policies
requires creating a data exchange platform that works on the ’once-only’
principle: The same data does not have to be entered manually multiple times.
In many countries, farmers are required to enter their data into federal
systems to receive subsidies essential for their operation. By creating an
exchange platform that allows data exchange between several applications,
digital possibilities can be used efficiently, and critical data can be
requested from farmers without a high administrative burden. Beyond the
implementation of such ’once-only’ platforms, clearly defined data standards
and measures to ensure high data quality need to be realized. Most
importantly, clear rules are needed as to who has access to which data and
what it may be used for. The development of these principles requires a great
deal of knowledge on the side of policy-making federal or regional offices and
cooperation with private industry to couple such solutions with standard farm-
management systems.

##  5 Conclusion

The future development and adoption of digital agriculture depend on several
factors, with a key consideration being the balance between costs and
benefits. To advance the field, a central goal is to develop agricultural
models that are accurate, have a high generalization ability, and are
adaptable to different agricultural systems and tasks. Achieving this
ambitious goal requires advancements in data generation, collection, curation,
and utilization through machine learning paradigms.

An often underestimated aspect in digital agriculture is the importance of
data quality. High-quality data in this context is characterized by diversity,
completeness, accuracy, unbiasedness, consistency, and relevance to the
intended agricultural task. These dimensions highlight the need for a
comprehensive understanding proven through practical experience. By
prioritizing data quality, the digital agricultural ecosystem can strengthen
machine learning models, preventing critical gaps and ensuring accurate
alignment with true or expected values.

The paradigm of data-centric digital agriculture emerges as a promising next
step, emphasizing the important role of data. Through systematic and
algorithmic approaches, data-centric methodologies optimize datasets in a
dynamic cycle, ensuring they evolve in parallel with model development. This
differs from the current model-centric approach to digital agriculture, which
treats data as a static benchmark and overlooks the dynamic nature of the
agricultural environment. By addressing this challenge, data-centric
strategies recognize and utilize valuable datasets, taking into account their
dynamic nature. This fosters the development of accurate and transferable
models that have a high generalization power.

Looking ahead, a data-centric approach has the potential to transform the
agricultural landscape, fostering innovations in field arrangements and
agricultural practices. With supportive policies, farmers can adapt their
systems in real-time using predictions from models built on high-quality data.
This reciprocal process, where both policy-makers and farmers contribute new
data, facilitates insights across different systems and borders, demonstrating
the potential of data-centric digital agriculture.

We argue that in the future of data-centric digital agriculture, key areas of
focus include novel sensor and robotic technologies, active sensing
strategies, data generalization and specification considerations, the use of
model transfer strategies, privacy-preserving models, sustainable machine
learning, a comprehensive evaluation of the applicability of agricultural
models, and the use of uncertainty quantification and explainable machine
learning. Another important aspect, although not discussed in this
perspective, is the interdisciplinary collaboration between different
communities and modelers. Therefore, data-centric digital agriculture does not
imply exclusive reliance on data-driven models but instead emphasizes the need
for action in this area.

Box: The terminology of data Different types of data play a role in digital
agriculture. _Raw observations_ refer to the perception and recording of
events, phenomena, or information with our senses and sensors. In agriculture,
it is common to observe approximation or indirect indicators (proxies) rather
than the actual phenomena of interest, such as biodiversity. This is due to
practical limitations, feasibility constraints, and the complexity of directly
measuring certain aspects in the field. _Data_ in general refers to factual or
numerical information that is processed for the purpose of analysis to produce
meaningful insights or patterns to support decision-making. Data can be
generated in many ways, such as by reproducing our knowledge, by using
proximal and remote sensors, by computer simulations, or by instruments such
as GPS devices. A data set can further contain labels or targets needed in the
context of machine learning, in addition to the actual, processed raw
observations. In the area of agricultural data, there is a fundamental
distinction between _experimental data_ and _non-experimental data_ , based on
their origin [123]. Experimental data is systematically derived through
controlled experiments designed to investigate cause-effect relationships
while minimizing confounding factors [124]. Examples include the thorough
evaluation of new crop cultivars in variety testing before release [125] or
on-farm site-specific crop management experiments in precision agriculture
[126]. These experiments follow standard design principles, including
replications, randomization, and blocking. On the other hand, non-experimental
data stem from the natural and often uncontrolled settings of the agricultural
landscape. This category encompasses a wide range, from coarse, aggregated
data such as regional yield and quality values to high-resolution geospatial
information collected by modern farming devices. Harvesters equipped with
advanced sensors generate yield maps, while unmanned aerial vehicles (UAVs),
satellites, and ground-based robots contribute geospatial information through
remote and proximal sensing [127, 128, 129]. Additionally, farmers’ knowledge
and survey data are considered non-experimental data. Both experimental and
non-experimental data play crucial roles in agricultural modeling,
particularly in the training and testing of machine learning models, although
with distinct objectives. Experimental data, due to their controlled nature,
contribute to hypothesis validation and the understanding of causal
relationships. The results enable targeted interventions and foster models
that generalize well within specific parameters. Mechanistic models, pre-
calibrated based on existing functional relationships between input and output
data, are rooted in these controlled experiments. On the other hand, non-
experimental data provide a broader scope of observations, capturing the
intricacies of real-world variability and complexity. These data, often
collected due to the impracticality of setting up experiments in dynamic
agricultural environments, serve as an invaluable resource for training
machine learning models. These models excel at detecting patterns,
correlations, and trends that naturally emerge. The synergy between
experimental and non-experimental data is indispensable, offering
comprehensive insights that bridge the gap between controlled experiments and
the practical challenges of agriculture. _Data-driven methods_ refer to
approaches that use data and apply data analysis techniques, statistical
modeling, and machine learning to extract patterns and insights for informed
decision support.

Box: The concurrent development of technologies shown by means of variable
rate application of nitrogen In modern agriculture, N is applied in a site-
specific manner with variable rate application (VRA) technology. The spatial
resolution of sensing techniques for VRA rapidly evolved over time, from
manual soil sampling to automatic satellite-based sensing. Nowadays, the first
generation of satellites provides open-access multi-spectral data in the tens
of meter range (e.g., Landsat, [130]), and a new, second generation of
satellites offers paywall-protected data in the meter to sub-meter range
(e.g., Planet, SkySat). Unmanned aerial vehicles (UAVs) offer another often
localized, high-resolution, and high-quality data source for precision
agriculture [131], provided that the basic concept of flight preparation and
execution is taken into account [132]. While sensing technologies have reached
sub-meter ranges, most modern farms still use fertilizer spreaders with
working widths of 15 meters or more [131] to apply fertilizer uniformly across
two or three management zones divided by farmers using farm-management
information systems [133]. Sensing technique Spatial resolution Rate
application level Field of application Traditional agriculture Qualitative
Manual, field level Field level Generic Modern agriculture Soil sampling
Manual, few sites Site-specific ($\geq$ 50 m) Very large fields Remote sensing
Satellites ($\geq$ 30 m2) Tractor lane ($\geq$ 15 m) Very large fields 1st
generation Remote sensing Satellites (10 – 0.7 m2) Tractor lane ($\geq$ 15 m)
Large fields 2nd generation Farm management systems and robotics Proximal
sensing UAVs ($<$ 1 cm2) Tractor lane ($\geq$ 15 m) Large fields Interactive
sensing UAVs, robots ($<$ 1 cm2) Robot ($\leq$ 1 cm) Small fields (e.g.,
strips)

Acknowledgments

This work has partially been funded by the Deutsche Forschungsgemeinschaft
(DFG, German Research Foundation) under Germany’s Excellence Strategy,
EXC-2070 - 390732324 - PhenoRob. A.W. discloses support for the research of
this work from Swiss National Science Foundation [grant number 200756]

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