Image-based digitalization has emerged as a transformative tool for the preservation, characterization, and management of germplasm resources (Baek et al. 2020, Choudhury et al. 2019, Tao et al. 2022, Yang et al. 2020, Yu et al. 2023). By capturing visual representations of plant phenotypes, this technology holds substantial potential to expedite plant breeding and crop improvement (Reed et al. 2004). However, the successful deployment of image-based digitalization requires a thorough understanding of its inherent limitations and the formulation of strategies to surmount these challenges (Tao et al. 2022). This paper investigates the technical, phenotypic, and analytical obstacles associated with image-based germplasm digitalization. Furthermore, it proposes innovative methodologies and solutions to address these constraints, thereby facilitating the effective utilization of image data in germplasm management and research

The advent of image-based digitalization in agriculture represents a substantial advancement in germplasm resource management (Laraswati et al. 2021, Yu et al. 2023). Traditional methods of germplasm characterization and preservation are heavily reliant on manual measurements and subjective evaluations, which are both time-consuming and susceptible to human error (Martini et al. 2021, Weise et al. 2020). In contrast, image-based techniques offer a high-throughput and objective approach to capturing detailed phenotypic information (Choudhury et al. 2019, Kwon et al. 2016). This technological transformation enhances the precision of germplasm characterization and enables the rapid screening of large plant populations, thereby accelerating the breeding process.

Despite these advantages, the deployment of image-based digitalization faces numerous challenges. On the technical front, acquiring high-quality images necessitates sophisticated equipment and stringent environmental control to ensure consistency and reproducibility (Choudhury et al. 2019, Tao et al. 2022). Variations in lighting, angle, and background can substantially impact image quality, introducing noise and bias into the data (Xiong et al. 2021, Yang et al. 2020). Additionally, the vast volume of image data generated demands robust storage solutions and efficient data management systems to handle, process, and retrieve information as required (Han et al. 2022, Kenchanmane et al. 2020, Roth et al. 2021).

Phenotypic challenges are prevalent, as plants exhibit a wide range of morphological traits influenced by both genetic and environmental factors. Accurately capturing these traits in images necessitates advanced imaging techniques and specialized software capable of distinguishing subtle differences in plant morphology (Choudhury et al. 2019). Furthermore, the dynamic nature of plant growth and development requires continuous monitoring of phenotypic traits over time, complicating the image acquisition and analysis process (Wang et al. 2023).

Analytically, interpreting image data presents significant challenges. Translating visual information into meaningful phenotypic data requires the application of advanced image processing algorithms and machine learning techniques (Yoosefzadeh-Najafabadi et al. 2021). These methods must be meticulously calibrated to address the inherent variability in plant phenotypes and to minimize errors in trait quantification (Roth et al. 2024). Furthermore, integrating image-derived data with other genetic and environmental datasets demands sophisticated analytical frameworks capable of managing multi-dimensional data (Roth et al. 2024, Yoosefzadeh-Najafabadi et al. 2021).

To address these challenges, this paper investigates a variety of innovative solutions and approaches. Advances in imaging technologies, such as hyperspectral imaging and 3D scanning, present new opportunities for capturing more detailed and accurate phenotypic information (Choudhury et al. 2020, Sarić et al. 2022). Enhanced data processing algorithms and machine learning models are improving the extraction of meaningful insights from image data. Additionally, the development of standardized protocols and guidelines for image acquisition and analysis is essential for ensuring consistency and comparability across studies (Pound et al. 2017).

While image-based digitalization offers substantial promise for the management and utilization of germplasm resources, its successful implementation depends on overcoming a series of technical, phenotypic, and analytical challenges (Kim et al. 2023, Li et al. 2021). Addressing these obstacles through innovative methodologies and collaborative efforts can unlock the full potential of image-based digitalization, leading to significant advancements in plant breeding and crop improvement (Omari et al. 2020). This paper aims to contribute to this ongoing endeavor by providing a comprehensive overview of current challenges and offering actionable solutions for the effective application of image data in germplasm research and management.

Among the various technical limitations affecting the efficacy of image-based digitalization, image quality control is paramount for accurate data extraction. Factors such as resolution, lighting conditions, and camera calibration can significantly impact the quality and interpretability of the images (Omari et al. 2020, Roth et al. 2021). Data storage presents another challenge, as large image datasets necessitate substantial storage capacity, which can be both costly and resource-intensive. Additionally, image analysis and processing require considerable computational resources, demanding powerful hardware and software due to the computationally intensive nature of these tasks (Choudhury et al. 2020). Lastly, phenotypic complexity can constrain the applicability of image-based methods, as certain traits, such as root architecture or internal plant structures, are challenging to capture accurately through imaging (Großkinsky et al. 2015, Shah et al. 2017, Takahashi & pradal 2021).

While image-based digitalization provides valuable insights into plant phenotypes, it has inherent limitations in capturing the full spectrum of plant traits. Incomplete representation is a significant challenge, as imaging captures only visual aspects of a plant while overlooking other critical traits such as taste, aroma, and nutritional content. Dynamic traits, which change over time—such as flowering time or disease resistance—require multiple images for accurate capture and analysis, thereby increasing the complexity of data acquisition and processing (Dong et al. 2024, Roth et al. 2021, Simko et al. 2017, Ying 2023). Additionally, environmental influences on phenotypic expression can obscure the interpretation of image data, complicating the differentiation of genetic effects from environmental effects (Ying 2023).

Data Analysis Challenges

Extracting meaningful information from image data presents several challenges. Image segmentation, which involves accurately distinguishing different plant parts or objects from the background, can be particularly complex, especially when dealing with overlapping or visually similar features. This task requires advanced image processing techniques and algorithms (Pound et al. 2017, Wu et al. 2019). Feature extraction is another critical step, involving the identification and quantification of relevant features from images. This process often necessitates domain expertise to determine which features are most informative, as well as the development of appropriate algorithms for feature extraction. Finally, converting image data into meaningful biological information requires robust statistical modeling and interpretation (Roth et al. 2021, Wu et al. 2019). This step involves analyzing the extracted features to identify patterns, relationships, and correlations with phenotypic traits, which can be intricate and time- consuming.

Cost and Time Considerations

Implementing image-based digitalization necessitates considerable investments in both time and resources. The acquisition of high-quality imaging equipment and the training of personnel for its operation and maintenance represent significant financial expenditures. The processes of image acquisition, preprocessing, and analysis are resource-intensive and require substantial time commitment (Roth et al. 2021). Additionally, the organization and management of large-scale image datasets are time-consuming and labor-intensive, necessitating the development of efficient data management strategies and robust infrastructure (Li et al. 2021).

Ethical Considerations

Protecting sensitive germplasm information and ensuring the equitable use of image data are critical ethical considerations. Data privacy is paramount and necessitates robust measures to safeguard image data, particularly when handling sensitive germplasm information. This includes implementing secure data storage, stringent access controls, and encryption to prevent unauthorized access and disclosure. Furthermore, clarifying intellectual property rights for image data and derived information is essential to avoid disputes and ensure fair attribution of ownership and usage (Le Buanec 2004). This involves establishing explicit guidelines for data ownership, sharing, and commercialization.

Even with these limitations, image-based digitalization of germplasm stock remains a valuable tool for plant breeding and conservation. By meticulously addressing these challenges and implementing appropriate strategies, researchers can maximize the benefits of this technology while mitigating its drawbacks.

Overcoming Challenges in Image-Based Digitalization of Germplasm

Technical Challenges

Several technical challenges must be addressed to effectively implement image-based germplasm digitalization (Großkinsky et al. 2015, Shah et al. 2017, Takahashi & pradal 2021). Optimizing image acquisition and processing is crucial to enhance image quality, efficiency, and standardization. This includes developing standardized protocols for image capture, implementing efficient image processing pipelines, and exploring techniques to improve image resolution and clarity. Advanced image analysis techniques represent another critical area, necessitating the development and application of sophisticated algorithms for accurate object detection, segmentation, and feature extraction. This involves leveraging deep learning and computer vision methods to extract meaningful phenotypic information from images. Lastly, the management of large image datasets requires efficient computational resources and infrastructure (Wang et al. 2023, Xiong et al. 2021). Optimizing processing and storage capabilities is essential to meet the computational demands of image analysis and ensure data accessibility.

Phenotypic Data Extraction

Accurate and efficient extraction of phenotypic data from images is essential for leveraging the potential of image-based digitalization. Quantitative trait analysis requires the development of robust methods to precisely measure complex traits, such as plant height, leaf area, and disease severity, from image data (Li et al. 2021). To maximize the impact of image-based phenotyping, high-throughput phenotyping pipelines must be established to efficiently process large germplasm collections. This entails optimizing image acquisition, analysis, and data management processes to handle high data volumes (Dong et al. 2024, Simko et al. 2017, Ying 2023). Additionally, integrating image data with other data types, such as genomic, environmental, and field data, can provide a more comprehensive understanding of plant phenotypes and facilitate the identification of genotype-phenotype relationships.

Enhancing Image Quality and Data Acquisition

Several strategies can be implemented to ensure optimal image quality and data acquisition for accurate phenotypic analysis (Pound et al. 2017, Wu et al. 2019). First, standardized imaging protocols should be developed, outlining specific guidelines for image capture, including lighting conditions, camera settings, and background control. This standardization enhances data consistency and comparability across different image sets. Second, image calibration using reference objects or color charts is crucial to maintain consistent color and scale accuracy throughout the image dataset (Roth et al. 2021). Third, investing in high-resolution imaging equipment enables the capture of fine details and reduces image noise, thereby improving overall image quality. Fourth, acquiring images from multiple angles and under varying conditions is essential for capturing complex traits that may not be apparent in a single image. Finally, image preprocessing techniques, such as noise reduction and sharpening, can be applied to enhance image quality before analysis further.

Improving Phenotypic Data Extraction

To optimize the extraction of phenotypic information from images, several advanced techniques can be employed. Sophisticated image analysis methods, such as deep learning and computer vision algorithms, are essential for accurate object detection, segmentation, and feature extraction. These algorithms enable precise identification and quantification of plant structures and traits within images. Additionally, 3D imaging techniques, including structured light and stereo vision, can be utilized to capture the complex architecture of plants, providing valuable insights into plant growth and development (Das Choudhury et al. 2020). Multispectral imaging extends beyond the visible spectrum, allowing for the assessment of additional phenotypic traits, such as chlorophyll content and water stress (Tao et al. 2022, Wu et al. 2024). Finally, developing comprehensive phenotypic databases is crucial for integrating image data with ground truth measurements and other relevant information, thereby facilitating effective data management, analysis, and interpretation (Xiong et al. 2021).

Addressing Data Management and Analysis Challenges

Effective management and analysis of large image datasets are critical for extracting meaningful insights. Cloud-based platforms provide scalable solutions for storing, processing, and sharing extensive image data (Khan et al. 2020, Liu et al. 2020). To optimize storage, data compression techniques can be utilized to minimize file sizes without compromising image quality. Accurate and consistent annotation of image features is essential for the training and evaluation of machine learning models. Therefore, developing standardized annotation tools is imperative for efficient and reliable data labeling. Integrating machine learning models into the analytical pipeline facilitates automated image analysis and feature extraction, thereby accelerating the process and reducing manual effort (Wu et al. 2019). Finally, interactive visualization tools enhance the exploration and interpretation of image data, allowing researchers to discern patterns, trends, and relationships within the dataset.

Mitigating Ethical Considerations

Ensuring the security of sensitive germplasm information and ensuring responsible data sharing is fundamental for ethical image-based digitalization (Tao et al. 2022). Implementing robust data privacy policies is essential to safeguard sensitive germplasm information from unauthorized access and misuse. Restricting access to image data to authorized personnel is crucial for maintaining data confidentiality. Clearly defining intellectual property rights related to image data and derived information is vital to protect ownership and usage rights. Additionally, establishing data-sharing agreements with explicit terms and conditions is necessary for collaborative research, ensuring the protection of data integrity and intellectual property.

Further Insights and Considerations

To maximize the value of image-based digitalization, several supplementary considerations are essential. Integrating image data with additional data types, such as genomic and environmental information, provides a more holistic understanding of germplasm and facilitates the identification of complex relationships between genotype, phenotype, and environment. Engaging domain experts in the development and evaluation of image analysis algorithms ensures both accuracy and relevance in the analysis. Lastly, the ongoing refinement of image acquisition and analysis protocols is crucial for incorporating emerging technologies and best practices, thereby enhancing the overall efficiency and effectiveness of the process (Yang et al. 2020).

By addressing these challenges and implementing innovative strategies, we can fully leverage the potential of image-based digitalization to advance germplasm management, plant breeding, and conservation efforts.

Applications and Impact

Image-based digitalization holds transformative potential for various facets of plant science and agriculture. It can significantly enhance germplasm characterization and evaluation by enabling efficient and precise assessment of phenotypic traits through image data. This advancement facilitates the identification of valuable germplasm accessions for breeding programs. Integrating image-based phenotyping into plant breeding workflows can expedite the development of new cultivars with desired traits by providing high-throughput capabilities that enable the selection of superior genotypes and the reduction of breeding cycles. Additionally, employing image analysis in precision agriculture and crop management offers opportunities to optimize crop production through real-time monitoring of plant health, growth, and stress conditions. This approach supports data-driven decision-making, ultimately improving crop yields and resource utilization.

Image-based digitalization of germplasm stock represents a transformative advancement in plant breeding and conservation. This technology facilitates efficient characterization, evaluation, and management of germplasm resources by capturing and analyzing visual phenotypes. Nevertheless, the successful implementation of this technology necessitates addressing significant challenges related to image quality, data analysis, and phenotypic complexity.

This research was supported by a grant from the Standardization and Integration of resources information for seed-cluster in Hub-Spoke material bank program (Project No. PJ01587004), Rural Development Administration, Republic of Korea.