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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Kabir, Noer Ni'mat Syamsu
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Computer Science & IT Engineering

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Overcoming imbalanced rice seed germination classification: enhancing accuracy for effective seedling identification Mara, Muhlasah Novitasari; Hidayat, Sidiq Syamsul; Putri, Farika Tono; Rahmawati, Dwi; Wahyuni, Sri; Prabowo, Muhamad Cahyo Ardi; Kabir, Noer Ni'mat Syamsu; Indra, Ragil Tri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp62-72

Abstract

This study aimed to automatically classify rice seedling germination on day seven using image analysis. The categories included normal, abnormal, and dead seeds. Due to the rarity of abnormal seedlings, capturing their images resulted in imbalanced data. To address this, abnormal categories were combined into a single class. We compared logistic regression, random forest, and deep learning models (VGG19, VGG16, Alex Net) for classification. Surprisingly, logistic regression achieved the highest accuracy (93.89%) and F1-scores (0.96 normal, 0.81 abnormal) despite the imbalanced data and complex task. The effectiveness of logistic regression for rice seedling classification with imbalanced data has been demonstrated in this novel research. Historically, deep learning models dominate image recognition, but our findings suggest simpler models can excel in specific scenarios, especially with limited data availability. This highlights the importance of selecting models based on data characteristics. The urgency for this research stems from the need for efficient and accurate rice seedling evaluation. Improved classification can enhance agricultural practices and optimize resource allocation.
Co-Authors DWI RAHMAWATI Indra, Ragil Tri Mara, Muhlasah Novitasari Prabowo, Muhamad Cahyo Ardi Putri, Farika Tono Sidiq Syamsul Hidayat, Sidiq Syamsul Sri Wahyuni