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International Journal of Advanced Artificial Intelligence Research

Open Access Peer Review International
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Class-Imbalance Aware Deep Learning Framework for Accurate Rice Seed Germination Classification and Robust Seedling Identification

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Dr. James William Carter 1 , Dr. Emily Rose Thompson 1 ,
4 School of Engineering, University of Manchester, Manchester, UK
4 Department of Computer Science, University of Oxford, Oxford, UK

Abstract

Rice seed germination assessment is a critical agronomic process that directly influences crop yield, seed quality assurance, and large-scale agricultural productivity. Traditional germination evaluation techniques rely heavily on manual inspection and conventional machine learning models, which often fail under class-imbalanced conditions where non-germinated or weak seedlings dominate dataset distributions. This study proposes a class-imbalance aware deep learning framework designed to enhance classification accuracy and robustness in rice seed germination and seedling identification tasks. The proposed approach integrates imbalance-sensitive learning strategies with convolutional feature extraction to improve discriminatory capability between germinated and non-germinated seed categories. Existing studies highlight the effectiveness of machine learning and deep neural networks in seed classification tasks but also emphasize persistent challenges arising from skewed datasets (Genze et al., 2020; Gulzar et al., 2020).

The framework builds upon foundational principles of seed science and germination biology, where physiological seed enhancements and germination behavior are critical determinants of classification accuracy (Copeland and McDonald, 2001). Furthermore, imbalance learning strategies such as oversampling, cost-sensitive optimization, and ensemble modeling are incorporated to mitigate bias toward majority classes (Gosain and Sardana, 2017; Feng et al., 2021). Experimental insights from prior research demonstrate that deep convolutional neural networks outperform traditional classifiers like SVM and logistic regression in visual seed quality assessment tasks when properly tuned for imbalance handling (Hidayat et al., 2023; Mohan and Raj, 2020).

The proposed framework contributes to the field by offering a structured integration of deep feature learning and imbalance-aware optimization, enabling improved classification performance, scalability, and real-world applicability in precision agriculture systems.

Keywords

Rice seed germination, class imbalance, deep learning framework, convolutional neural networks

References

S.J. Basha, S. R. Madala, K. Vivek, E. S. Kumar, and T. Ammannamma, “A review on imbalanced data classification techniques,” in 2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022, Mar. 2022, pp. 1–6.
L. O. Copeland and M. B. McDonald, “Seed Enhancements,” in Principles of Seed Science and Technology, Boston, MA: Springer US, 2001, pp. 277–296.
Y. Feng, M. Zhou, and X. Tong, “Imbalanced classification: A paradigm-based review,” Statistical Analysis and Data Mining, vol. 14, no. 5, pp. 383–406, Oct. 2021.
N. Genze, R. Bharti, M. Grieb, S. J. Schultheiss, and D. G. Grimm, “Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops,” Plant Methods, vol. 16, no. 1, Dec. 2020.
A. Gosain and S. Sardana, “Handling class imbalance problem using oversampling techniques: A review,” in 2017 International Conference on Advances in Computing, Communications and Informatics, Sep. 2017, pp. 79–85.
Y. Gulzar, Y. Hamid, A. B. Soomro, A. A. Alwan, and L. Journaux, “A convolution neural network-based seed classification system,” Symmetry, vol. 12, no. 12, pp. 1–18, Dec. 2020.
J. Ha, M. Kambe, and J. Pe, Data mining: concepts and techniques, Burlington: Morgan Kaufmann, 2011.
S. S. Hidayat, D. Rahmawati, M. C. A. Prabowo, L. Triyono, and F. T. Putri, “Determining the rice seeds quality using convolutional neural network,” International Journal on Informatics Visualization, vol. 7, no. 2, pp. 527–534, Jun. 2023.
S. Huang, X. Fan, L. Sun, Y. Shen, and X. Suo, “Research on classification method of maize seed defect based on machine vision,” Journal of Sensors, vol. 2019, pp. 1–9, Nov. 2019.
W. N. L. W. H. Ibeni, M. Z. M. Salikon, A. Mustapha, S. A. Daud, and M. N. M. Salleh, “Comparative analysis on bayesian classification for breast cancer problem,” Bulletin of Electrical Engineering and Informatics, vol. 8, no. 4, Dec. 2019.
S. S. Ibrahim, N. A. Zulkifli, N. Sabri, A. A. Shari, and M. R. M. Noordin, “Rice grain classification using multi-class support vector machine (SVM),” IAES International Journal of Artificial Intelligence, vol. 8, no. 3, pp. 215–220, Dec. 2019.
J. M. Johnson and T. M. Khoshgoftaar, “Survey on deep learning with class imbalance,” Journal of Big Data, vol. 6, no. 1, Dec. 2019.
K. Kiratiratanapruk et al., “Development of paddy rice seed classification process using machine learning techniques for automatic grading machine,” Journal of Sensors, vol. 2020, pp. 1–14, Jul. 2020.
T. M. Khoshgoftaar, M. Golawala, and J. Van Hulse, “An empirical study of learning from imbalanced data using random forest,” in International Conference on Tools with Artificial Intelligence, Oct. 2007, vol. 2, pp. 310–317.
P. Kumar, R. Bhatnagar, K. Gaur, and A. Bhatnagar, “Classification of imbalanced data: review of methods and applications,” IOP Conference Series: Materials Science and Engineering, vol. 1099, no. 1, Mar. 2021.
B. Lurstwut and C. Pornpanomchai, “Image analysis based on color, shape and texture for rice seed (Oryza sativa L.) germination evaluation,” Agriculture and Natural Resources, vol. 51, no. 5, pp. 383–389, Oct. 2017.
H. Luo, X. Pan, Q. Wang, S. Ye, and Y. Qian, “Logistic regression and random forest for effective imbalanced classification,” in International Computer Software and Applications Conference, vol. 1, pp. 916–917, Jul. 2019.
K. R. Mahmudah, F. Indriani, Y. Takemori‐sakai, Y. Iwata, T. Wada, and K. Satou, “Classification of imbalanced data represented as binary features,” Applied Sciences, vol. 11, no. 17, Aug. 2021.
D. Mohan and M. G. Raj, “Quality analysis of rice grains using ANN and SVM,” Journal of Critical Reviews, vol. 7, no. 1, pp. 395–402, Jan. 2020.
A. S. More and D. P. Rana, “Review of random forest classification techniques to resolve data imbalance,” in 1st International Conference on Intelligent Systems and Information Management, Oct. 2017, pp. 72–78.
K. Oksuz, B. C. Cam, S. Kalkan, and E. Akbas, “Imbalance problems in object detection: a review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 10, pp. 3388–3415, Oct. 2021.
M. C. A. Prabowo and S. S. Hidayat, “Edge Detection technique for rice quality analysis using digital image processing,” in AIP Conference Proceedings, 2023, vol. 2431, no. 1.
H. A. A. Rahman and B. W. Yap, “Imbalance effects on classification using binary logistic regression,” in Communications in Computer and Information Science, vol. 652, 2016, pp. 136–147.
H. A. A. Rahman, Y. B. Wah, and O. S. Huat, “Predictive performance of logistic regression for imbalanced data with categorical covariate,” Pertanika Journal of Science and Technology, vol. 28, no. 4, pp. 1141–1161, Oct. 2020.
S. S. Rawat and A. K. Mishra, “Review of methods for handling class-imbalanced in classification problems,” arXiv-Computer Science, pp. 1–15, 2022.
F. Rozi et al., “Indonesian market demand patterns for food commodity sources of carbohydrates in facing the global food crisis,” Heliyon, vol. 9, no. 6, Jun. 2023.
R. Ruslan, S. Khairunniza-Bejo, M. Jahari, and M. F. Ibrahim, “Weedy rice classification using image processing and a machine learning approach,” Agriculture, vol. 12, no. 5, Apr. 2022.
P. Saxena, K. Priya, S. Goel, P. K. Aggarwal, A. Sinha, and P. Jain, “Rice varieties classification using machine learning algorithms,” Journal of Pharmaceutical Negative Results, vol. 13, 2022.
H. Shah and V. Manjula, “A performance analysis of deep convolutional neural networks using kuzushiji character recognition,” in 2020 International Conference on Decision Aid Sciences and Application, pp. 1068–1071, Nov. 2020.
Q. Shu, T. Hu, and S. Liu, “Random forest algorithm based on GAN for Imbalanced data classification,” Journal of Physics: Conference Series, vol. 1544, no. 1, 2020.
D. Sivakumar, K. Suriyakrishnaan, P. Akshaya, G. V. Anuja, and G. T. Devadharshini, “Computerized growth analysis of seeds using deep learning method,” International Journal of Recent Technology and Engineering, vol. 7, no. 6, pp. 1885–1892, 2019.
B. Sun and H. Chen, “A Survey of k nearest neighbor algorithms for solving the class imbalanced problem,” Wireless Communications and Mobile Computing, vol. 2021, no. 1, Jan. 2021.
S. Y. Suryono, H. Kuswanto, and N. Iriawan, “Rice phenology classification based on random forest algorithm for data imbalance using Google Earth engine,” Procedia Computer Science, vol. 197, pp. 668–676, 2021.
O. Wu, “Rethinking class imbalance in machine learning,” arXiv-Computer Science, 2023.
S. Wang, W. Liu, J. Wu, L. Cao, Q. Meng, and P. J. Kennedy, “Training deep neural networks on imbalanced data sets,” in Proceedings of the International Joint Conference on Neural Networks, vol. 2016, pp. 4368–4374, Jul. 2016.
S. Wang, W. Liu, J. Wu, L. Cao, Q. Meng, and P. J. Kennedy, “Training deep neural networks on imbalanced data sets,” in Proceedings of the International Joint Conference on Neural Networks, vol. 2016, pp. 4368–4374, Jul. 2016.
C. O. Truicǎ and C. A. Leordeanu, “Classification of an imbalanced data set using decision tree algorithms,” UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science, vol. 79, no. 4, pp. 69–84, 2017.
Y. Feng, M. Zhou, and X. Tong, “Imbalanced classification: A paradigm-based review,” Statistical Analysis and Data Mining, vol. 14, no. 5, pp. 383–406, Oct. 2021.
M. Zheng, F. Wang, X. Hu, Y. Miao, H. Cao, and M. Tang, “A method for analyzing the performance impact of imbalanced binary data on machine learning models,” Axioms, vol. 11, no. 11, Nov. 2022.

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