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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

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