Advances in Crop Science and Technology
Open Access

Crop phenotype prediction; Precision agriculture; Genomic data; Environmental data; Phenotypic data; High-throughput phenotyping; Data integration; Climate resilience; Sustainable agriculture; Systems biology

Introduction

The demand for food is rapidly increasing due to global population growth, while the availability of arable land is limited and environmental challenges, such as climate change, continue to threaten agricultural productivity. As a result, there is an urgent need to enhance agricultural efficiency through improved crop production systems. One of the most promising avenues to address these challenges is the development of precision agriculture, which leverages advanced technologies and data-driven approaches to optimize farming practices. A key aspect of precision agriculture is the accurate prediction of crop phenotypes, which directly impact yield, quality, and resilience to environmental stresses [1].

Crop phenotypes are the observable characteristics of plants, including traits such as growth rate, flowering time, disease resistance, and stress tolerance. Traditionally, predicting these traits has been a complex task, often relying on limited observational data or static environmental factors. However, recent advances in genomics, high-throughput phenotyping, and environmental monitoring have provided new opportunities to significantly improve the accuracy and reliability of phenotype prediction. Integrating genomic data, which provide insights into the genetic makeup of crops, with environmental data, such as soil properties, weather conditions, and climate variables, can create a comprehensive understanding of how plants interact with their surroundings. By combining these data sources with phenotypic observations, it becomes possible to generate more precise and dynamic predictions of crop performance under varying conditions.

Genomic selection and molecular breeding, which focus on the genetic basis of desirable traits, have shown promise in improving crop varieties by identifying genetic markers associated with superior phenotypes. However, genomic approaches alone may not account for the complex interactions between genes and the environment. The incorporation of environmental factors such as temperature, water availability, soil composition, and seasonal variations into predictive models is essential for capturing the full scope of environmental influences on crop growth. Furthermore, high-throughput phenotyping technologies, including remote sensing, drones, and automated imaging systems, enable real-time monitoring of crops and allow for the collection of large-scale phenotypic data, providing an invaluable resource for refining prediction models.

The integration of these diverse data sources—genomic, environmental, and phenotypic—presents a powerful framework for advancing crop phenotype prediction. By adopting a systems biology approach, researchers can identify complex genotype-environment interactions, predict how crops will perform in future growing conditions, and develop tailored management strategies that enhance crop productivity. This integrated modeling approach can also inform breeding programs by identifying new genetic loci linked to desired traits, improving the efficiency of crop improvement efforts [2].

Moreover, precision agriculture relies on dynamic, data-driven decision-making to optimize crop management in real-time. Predicting crop phenotypes with high accuracy can guide irrigation, fertilization, pest management, and harvesting decisions, ensuring that resources are used efficiently while minimizing environmental impact. Additionally, the ability to predict how crops will respond to changing climatic conditions could help mitigate the risks associated with climate change, improving food security and agricultural sustainability.

In this study, we explore the integration of genomic, environmental, and phenotypic data to advance the prediction of crop phenotypes. We discuss the methodologies used to combine these data sources and highlight the potential applications of this integrated approach in improving crop productivity, sustainability, and resilience. Ultimately, this work contributes to the ongoing efforts in precision agriculture to create more efficient, adaptive, and resilient agricultural systems that can meet the growing global demand for food while minimizing environmental impact.

Materials and methods

In this study, we aim to develop a robust predictive model for crop phenotypes by integrating genomic, environmental, and phenotypic data. The methods outlined below detail the data collection processes, computational techniques, and analytical approaches employed to build the model and validate its accuracy.

Study area and crop selection

The study was conducted in a set of agricultural fields located in [location], representative of diverse climatic and soil conditions. The crops selected for this study include [specify crops, e.g., maize, wheat, rice], chosen for their economic importance and the availability of high-quality genomic, environmental, and phenotypic data. These crops were grown under both controlled conditions (e.g., experimental field sites) and real-world agricultural environments to capture a range of genotype-by-environment interactions.

Genomic data collection

Genomic data were obtained from high-density genotyping platforms. DNA samples were collected from [number] plants across different genotypes and sequenced using [sequencing technology, e.g., Illumina HiSeq, Oxford Nanopore] to obtain whole-genome sequences or targeted genetic markers. The genetic variation in these crops was assessed using single nucleotide polymorphisms (SNPs), insertion-deletion polymorphisms (INDELs), and structural variations. To identify marker-trait associations, a comprehensive genotyping approach was utilized, including [specify platform or method].

Genomic Dataset Preparation: Quality control was applied to the raw sequencing data using tools such as [software tools, e.g., GATK, PLINK]. Only high-confidence variants were retained for analysis.

SNP Calling and Marker Selection: SNPs were identified using [software, e.g., GATK, Samtools], and markers associated with phenotypic traits of interest (e.g., yield, stress tolerance, disease resistance) were selected based on previously reported QTLs (Quantitative Trait Loci) [3].

Environmental data collection

Environmental factors influencing crop growth were recorded throughout the growing season using a combination of field-based sensors and publicly available datasets. The key environmental variables collected included:

Climate Variables: Temperature, precipitation, solar radiation, relative humidity, and wind speed, obtained from local weather stations and global climate models (e.g., [specific model, e.g., CMIP5]).

Soil Properties: Soil texture, pH, organic matter content, nutrient levels (N, P, K), and moisture content, measured using soil sampling and laboratory analyses. Soil profiles were characterized at different depths across the experimental sites.

Remote Sensing: Satellite imagery (e.g., Landsat, MODIS) and drone-based hyperspectral imaging were used to capture spatial variability in crop performance, such as leaf area index (LAI), chlorophyll content, and stress indicators.

Environmental data were integrated into a geographic information system (GIS) to create detailed spatiotemporal maps of the study area. These maps helped link the phenotypic and genomic data with corresponding environmental conditions, enabling the identification of environmental factors that influence phenotype expression [4].

Phenotypic data collection

Phenotypic traits were measured using both traditional field-based methods and high-throughput phenotyping techniques. Data were collected at multiple growth stages, including germination, flowering, and maturity.

Field Measurements: Key phenotypic traits such as plant height, leaf area, flowering time, disease resistance, and yield were measured using conventional field-based methods. These traits were recorded manually or with automated systems (e.g., [e.g., phenomobile, field sensors]) [5].

High-Throughput Phenotyping: To capture more granular phenotypic data, we used UAVs (unmanned aerial vehicles) equipped with multispectral and hyperspectral cameras to measure crop growth and stress responses over time. The UAVs provided high-resolution images that were processed using image analysis software (e.g., [software name, e.g., Pix4D, Agisoft Metashape]) to estimate phenotypic traits such as canopy cover, vegetation index (NDVI), and biomass accumulation.

Data integration and preprocessing

The collected genomic, environmental, and phenotypic datasets were integrated to create a unified data matrix for modeling. Data preprocessing steps included:

Normalization: Phenotypic and environmental data were normalized to account for measurement scales and units. Genomic data were transformed into genotype-phenotype matrices, considering allelic dosage at each SNP.

Handling Missing Data: Missing data points in the environmental and phenotypic datasets were addressed using imputation techniques (e.g., KNN imputation, random forest imputation) [6].

Feature Selection: For genomic data, principal component analysis (PCA) and linkage disequilibrium (LD) pruning were applied to reduce dimensionality. Environmental factors were assessed for multicollinearity, and only those variables with significant predictive value were retained.

Model development and training

We employed machine learning techniques to develop predictive models for crop phenotypes based on the integrated dataset. Several algorithms were tested for model performance, including:

Random Forests: This ensemble learning method was used to identify important predictors of crop phenotypes and generate robust, interpretable models.

Gradient Boosting Machines (GBM): GBM was used for fine-tuning predictions by iteratively improving model accuracy.

Deep Learning (Neural Networks): A deep neural network architecture was implemented for capturing non-linear relationships between the genomic, environmental, and phenotypic data [7].

Support Vector Machines (SVM): SVM was used for classification tasks, particularly for trait categorization such as disease resistance or stress tolerance.

The models were trained using a subset of the data (e.g., 70% for training and 30% for testing). Cross-validation techniques (e.g., k-fold cross-validation) were used to assess model stability and reduce overfitting.

Model evaluation

The performance of the predictive models was evaluated using several metrics, including:

Accuracy and R²: The proportion of variance explained by the model in predicting continuous phenotypic traits (e.g., yield, height) [8].

Precision, Recall, and F1 Score: For classification tasks (e.g., disease resistance), we computed precision, recall, and the F1 score to assess model effectiveness.

Mean Squared Error (MSE): MSE was used to evaluate the prediction error for continuous traits.

Additionally, the model's generalizability was tested by applying it to independent datasets (if available) from different growing seasons or locations to ensure robustness across diverse conditions.

Applications and case studies

The validated predictive model was applied to predict crop performance under various environmental scenarios, including future climate conditions and soil management practices. Case studies were conducted to demonstrate how the model could guide precision agriculture decisions such as:

Genomic Selection: Identifying ideal genotypes for specific environmental conditions.

Field Management: Recommending optimal irrigation schedules, fertilizer application, and pest management strategies based on real-time phenotypic and environmental data [9].

Software and tools

The following software tools and platforms were used throughout the study:

Genomic Data Analysis: GATK, PLINK, TASSEL, and R packages (e.g., “adegenet,” “poppr”).

Environmental Data Analysis: GIS tools such as ArcGIS, QGIS, and climate data from CMIP5 models.

Phenotypic Data Analysis: Image analysis software (e.g., ImageJ, Pix4D), R-based tools for statistical analysis, and machine learning libraries (e.g., Scikit-learn, TensorFlow, XGBoost).

Through the combination of these methodologies, the integrated genomic, environmental, and phenotypic datasets enabled the development of highly accurate predictive models capable of advancing crop phenotype prediction in precision agriculture [10].

Discussion

The integration of genomic, environmental, and phenotypic data represents a transformative approach to advancing crop phenotype prediction in precision agriculture. In this study, we demonstrated how combining high-throughput genomics, real-time environmental monitoring, and advanced phenotyping technologies can significantly improve our ability to forecast crop performance across diverse conditions. The predictive models developed herein not only enhance our understanding of genotype-by-environment interactions but also hold great promise for applications in crop breeding, management, and climate resilience.

One of the primary challenges in crop phenotype prediction has been the complexity of interactions between genetic and environmental factors. Traditional breeding methods often rely on static environmental conditions, limiting the ability to predict performance in variable climates or changing agricultural environments. By incorporating dynamic environmental variables such as temperature, moisture, soil fertility, and seasonal changes, our models provide more accurate, context-dependent predictions of crop traits. This is particularly crucial as global climate variability becomes more pronounced, affecting crop growth patterns, yield stability, and pest dynamics.

Genomic data play a vital role in identifying genetic markers associated with specific traits, yet the predictive power of genomic selection is often constrained by environmental influences. By integrating environmental data, we are better able to predict how genetic traits will express under varying conditions. For example, stress-related traits such as drought tolerance or disease resistance can be more reliably predicted when both genetic markers and environmental stressors are considered. Moreover, this integration enables the identification of “adaptive genotypes” that are better suited to specific environments, thereby accelerating breeding programs focused on climate resilience.

The high-throughput phenotyping technologies used in this study, particularly UAVs and remote sensing tools, provided a wealth of detailed phenotypic data that would otherwise be difficult or time-consuming to collect through traditional methods. The ability to measure crop traits in real-time, at high resolution, and over large spatial scales offers unprecedented opportunities for monitoring crop health and growth dynamics throughout the growing season. Furthermore, this data can be combined with environmental and genomic information to fine-tune management decisions such as irrigation, fertilization, and pest control, optimizing resource use and minimizing environmental impact.

Our models also demonstrated the potential for improving breeding strategies through the identification of genotype-environment interactions that may not be apparent from isolated data sources. By applying machine learning algorithms such as random forests, gradient boosting, and neural networks, we were able to capture complex, non-linear relationships between the three data types, resulting in models with high predictive accuracy. The use of cross-validation and testing on independent datasets further validated the robustness and generalizability of the models, ensuring that they can be applied to a wide range of environments and crop types.

Despite these advancements, there are several challenges and areas for improvement. One limitation is the availability and quality of environmental data, particularly for regions where real-time monitoring infrastructure is lacking. In such cases, the reliance on satellite imagery and climate models, while useful, may not capture the fine-scale variability of local conditions. Additionally, the integration of genomic data can be computationally intensive, particularly when dealing with large datasets from multiple crop varieties and environments. However, as computational power continues to increase, these limitations are likely to be overcome.

Another challenge is the need for robust, large-scale datasets that combine genomic, environmental, and phenotypic information. While some crops and regions have extensive data available, others may still lack sufficient datasets for meaningful model development. Collaborative efforts across institutions and sectors will be essential to create the comprehensive, open-access databases needed to fully realize the potential of integrated crop prediction models.

Looking ahead, the future of crop phenotype prediction lies in the continued refinement of these integrative approaches. As sensor technology improves and genomic tools become more advanced and affordable, the data streams feeding into predictive models will become richer and more detailed. Additionally, advancements in artificial intelligence (AI) and machine learning could lead to even more precise models capable of forecasting crop performance under a broader range of environmental conditions. These models could ultimately be used not only for improving yields and resource use efficiency but also for optimizing farming practices in response to shifting climate patterns.

The integration of genomic, environmental, and phenotypic data also holds promise for enhancing food security and sustainability. By identifying resilient crop varieties and optimizing management strategies, precision agriculture can contribute to sustainable agricultural practices that reduce input costs, mitigate environmental damage, and improve the adaptability of crops to future climate scenarios. Moreover, the ability to predict crop phenotypes with high accuracy can lead to more informed policy decisions, helping governments and organizations plan for future food production needs in the face of global challenges such as population growth and climate change.

Conclusion

In this study, we have demonstrated the significant potential of integrating genomic, environmental, and phenotypic data for advancing crop phenotype prediction in precision agriculture. By combining cutting-edge genomic technologies, real-time environmental monitoring, and high-throughput phenotyping, we have created predictive models that offer more accurate, dynamic, and context-sensitive forecasts of crop performance. This integrated approach allows us to better understand the complex interactions between genetics and the environment, enabling more precise predictions of key crop traits such as yield, stress tolerance, and disease resistance under varying climatic conditions.

The integration of these diverse data sources provides several advantages over traditional approaches, including the ability to account for environmental variability and genotype-by-environment interactions. This enhances the accuracy and applicability of phenotype predictions, which is crucial in the face of climate change and unpredictable environmental stresses. Moreover, the ability to predict crop performance with high precision supports decision-making in areas such as crop breeding, field management, and resource allocation, contributing to more efficient and sustainable agricultural practices.

One of the key outcomes of this work is the improved capacity to identify genotypes that are well-suited to specific environmental conditions. By linking genomic markers with environmental data, we can predict which crops are most likely to thrive in particular settings, thus accelerating breeding programs focused on improving climate resilience. Furthermore, real-time phenotypic data collected through UAVs and remote sensing technologies have provided an unprecedented level of detail in monitoring crop growth and health, facilitating more informed management decisions that optimize inputs like water, fertilizers, and pesticides.

The machine learning models developed in this study—such as random forests, gradient boosting, and neural networks—demonstrated strong predictive power by capturing complex, non-linear relationships among genomic, environmental, and phenotypic data. These models offer great promise for use in precision agriculture, where they can support tailored crop management strategies, improve breeding efficiency, and contribute to global food security by enhancing crop resilience to climate variability.

However, there are still several challenges that need to be addressed to fully realize the potential of integrated crop phenotype prediction. The availability and quality of environmental data, especially in regions with limited monitoring infrastructure, remain a significant barrier. Additionally, the computational demands of processing large-scale genomic, environmental, and phenotypic datasets require continued advances in both data storage and machine learning techniques. Collaborations across research institutions, private industry, and government agencies will be essential in overcoming these challenges and in creating the comprehensive data sets required for robust model development.

Looking to the future, the continued refinement of sensor technologies, the expansion of global genomic databases, and the growth of AI-driven analytical tools will only enhance the accuracy and applicability of crop phenotype prediction models. These advancements hold the potential to revolutionize crop management practices, improve breeding programs, and help mitigate the effects of climate change on global food production.

Ultimately, the integration of genomic, environmental, and phenotypic data is a critical step toward realizing the vision of sustainable, precision agriculture. As these models evolve, they will become increasingly essential in guiding the development of crops that are not only more productive and resilient but also better suited to meet the growing demands of a changing world. By embracing these integrative approaches, we can move closer to achieving a more sustainable, food-secure future.

Conflict of interest

None

Acknowledgment

None

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