AI is transforming seed quality analysis with advanced techniques like deep learning and non-destructive imaging. This newsletter highlights recent research innovations in seed viability, vigour, purity, and classification, paving the way for smarter, more efficient agriculture.

Deep learning techniques in high-throughput seed phenotyping technologies (Zhou et al., 2025)

The research paper, titled “Application of Deep Learning for High-Throughput Phenotyping of Seed: A Review”, is authored by Chen Jin, Lei Zhou, Yuanyuan Pu, Chu Zhang, Hengnian Qi, and Yiying Zhao. These researchers are affiliated with leading institutions across China 🇨🇳 and Ireland 🇮🇪, including:

• Huzhou University, China
• Nanjing Forestry University, China
• South East Technological University, Ireland
• Zhejiang Academy of Agricultural Sciences, China

###Background of the Research

This study reviews the integration of deep learning techniques into high-throughput seed phenotyping technologies, offering a non-destructive, accurate, and scalable solution for evaluating seed quality.

Schematic flowchart of hyperspectral Imaging data acquisition. (H: height, W: width). Source: Zhou et al., 2025

The research highlights the potential of these technologies in addressing global agricultural challenges, such as:

1. Improving food security
2. Tackling climate change impacts
3. Optimizing seed selection and breeding processes

The general flowchart of deep learning methods for seed phenotyping. Source: Zhou et al., 2025

The paper emphasizes the synergy between deep learning models (e.g., CNNs, Transformers, RNNs) and advanced imaging technologies like hyperspectral, X-ray, and terahertz imaging for evaluating critical seed traits, including variety classification, defect detection, vigor analysis, and purity assessment.

General construction process of dual channel convolutional neural network. Source: Zhou et al., 2025

The overview of the application of deep learning methods for high-throughput phenotyping of seeds. Source: Zhou et al., 2025

Key Insights:

• Seed Variety Classification Accuracy: Several studies using Convolutional Neural Networks (CNN) achieved classification accuracies up to 100% for rice, wheat, and maize varieties using spectral and machine vision data,
• Seed Vigour Detection: Hyperspectral imaging combined with CNN models achieved near 100% accuracy in predicting seed vitality for various crop species like rice and maize
• Seed Defect Detection: Advances in CNN-based approaches have enabled defect detection in seeds with accuracies exceeding 97.84%, as demonstrated in soybean and maize seed quality evaluations.

AI Tools for Seed Quality Assessment (Kumar Singh et al., 2025)

The research paper, titled “Artificial Intelligence-based Tools for Next-Generation Seed Quality Analysis,” is authored by a multidisciplinary team of experts. The authors include Sumeet Kumar Singh, Rashmi Jha, Saurabh Pandey, Chander Mohan, Chetna, Saipayan Ghosh, Satish Kumar Singh, Sarita Kumari, and Ashutosh Singh. They represent prestigious institutions in India 🇮🇳, such as the Dr. Rajendra Prasad Central Agricultural University (RPCAU) in Bihar, Guru Nanak Dev University in Punjab, and the Ministry of Agriculture and Farmer Welfare, New Delhi.

Background of the Research

The research focuses on addressing critical challenges in modern agriculture, particularly the need for high-quality seeds to ensure optimal crop yields amidst growing food demand, climate change, and disrupted agricultural supply chains. Traditionally, seed quality assessment relies on manual, time-consuming, and error-prone methods, which struggle to meet the rising efficiency and precision requirements of the agricultural industry.

Principle of remote sensing for seed quality assessment. Source: Kumar Singh et al., 2025

The paper explores the integration of Artificial Intelligence (AI) and non-destructive techniques like x-ray imaging, hyperspectral imaging, multispectral imaging, near-infrared (NIR) spectroscopy, and remote sensing to revolutionize seed quality evaluation. These innovative tools offer faster, more accurate, and cost-effective methods for assessing seed viability, vigor, germination, and health. The research underscores how advancements in machine learning, imaging technology, and spectral analysis are bridging the gap between traditional approaches and the modern demands of global agriculture.

Simplified block diagram for representation of different step NIR spectroscopy. Source: Kumar Singh et al., 2025

With applications ranging from automated seed screening to detecting internal seed damage and grading seed quality, this study holds the potential to transform how seeds are inspected and distributed worldwide, ensuring better productivity and supporting global food security. The research is set against a backdrop of technological evolution in agriculture, leveraging big data, IoT, and AI innovations to streamline seed quality testing for both research and commercial use.

Stepwise representation of non-destructive electric nose (E-nose) system for shelf life evaluation of seeds. Source: Kumar Singh et al., 2025

Key Insights:

• AI-Based Seed Viability Accuracy: Techniques like FT-NIR spectroscopy and canonical variates analysis (ECVA) achieved nearly 100% accuracy in predicting the viability of soybean seeds.
• Seed Coating Classification Efficiency: Multispectral Imaging (MSI) demonstrated up to 93.33% accuracy in classifying coated seeds of different cultivars.
• Seed Vigor Classification: Using Fourier Transform Near-Infrared (FT-NIR) spectroscopy, research achieved a high classification accuracy of 91% in distinguishing viable from non-viable seeds.

Why are these two research papers important to know about?

Focus of Research:

• Kumar Singh et al., 2025: Emphasizes AI-based tools for seed quality analysis, specifically targeting viability, vigor, and seed coating classification with methods like FT-NIR spectroscopy and multispectral imaging.
• Zhou et al., 2025: Focuses on the integration of deep learning techniques with high-throughput phenotyping technologies for comprehensive seed quality evaluation, including classification, vigor, defect detection, and purity analysis.

Accuracy Achievements:

• Kumar Singh et al., 2025: Achieved up to 100% accuracy in seed viability detection (soybean seeds) and 93.33% accuracy in seed coating classification.
• Zhou et al., 2025: Demonstrated 100% accuracy in some seed variety classification tasks (e.g., rice) and near 100% accuracy in seed vigor detection using hyperspectral imaging and CNNs.

Technological Approaches:

• Kumar Singh et al., 2025: Relies on non-destructive imaging techniques like FT-NIR spectroscopy and canonical variates analysis, alongside machine learning.
• Zhou et al., 2025: Incorporates deep learning models (CNNs, RNNs, Transformers) with advanced imaging technologies such as hyperspectral, X-ray, and terahertz imaging for richer, automated data analysis.

Applications and Scope:

• Kumar Singh et al., 2025: Focuses on specific use cases like seed viability, coating detection, and vigor classification, targeting practical applications in seed production and quality control.
• Zhou et al., 2025: Covers a broader range of applications, including seed classification, defect detection, composition measurement, vigor prediction, and purity analysis, with an emphasis on scalability.

Key Challenges Addressed:

• Kumar Singh et al., 2025: Addresses the limitations of manual and traditional methods by enhancing precision and efficiency for specific seed quality parameters.
• Zhou et al., 2025: Tackles challenges in data scarcity, high-dimensionality of spectral data, and scaling automation using deep learning for large-scale, high-throughput phenotyping.

While both studies enhance seed quality analysis, Kumar Singh et al., 2025 focuses on applying established AI tools to specific seed traits, whereas Zhou et al., 2025 offers a comprehensive approach by combining deep learning with advanced imaging technologies for diverse and large-scale seed phenotyping tasks.

Practical application of Deep Learning for Seed Quality Assurance

Check the online-based tool for Seed Quality Assurance (Soybean edition):

Petiole Pro: Soybean Seeds Quality Assurance Web-Tool

If you would like to customize this web tool for your seed QA needs for FREE, please, let us know - support@petiolapp.com

Place the soybean seeds on or next to the calibrating plate to obtain an accurate seed count, average seed area, and standard deviation. Source: Petiole Pro

An automatic seed counter can process a high volume of seeds. In the figure above, 603 soybean seeds have been detected. Source: Petiole Pro

Seed area distribution analysis available for each photo. Source: Petiole Pro

Calibrating Plate for Petiole Pro

Without the calibrating plate, you will only obtain the seed count.

With the calibrating plate, you can obtain:

• seed count,
• average seed area, and
• standard deviation.

You can download the calibration plate from Google Drive to print it using your own printer or purchase it directly from us.🌱