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All Journal International Journal of Robotics and Control Systems Control Systems and Optimization Letters
Gutierrez, Joshua Balistoy
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Efficient Detection Classifiers for Genetically-Modified Golden Rice Via Machine Learning Gutierrez, Joshua Balistoy; Arboleda, Edwin Romeroso
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1686

Abstract

Rice is a staple food for over half of the global population, especially in the Philippines. However, traditional rice lacks essential micronutrients like vitamin A, contributing to widespread Vitamin A Deficiency (VAD). Golden Rice was developed to combat VAD, and this is biofortified with beta-carotene, a precursor of Vitamin A. However, concerns about cross-contamination, food safety, and ethics have emerged. Current GMO detection methods, such as PCR and ELISA, are not ideal for large-scale or on-site use since these are intended to be performed inside laboratory and requires technical expertise.  This study presents a novel machine learning (ML)-based approach for the detection of genetically modified Golden Rice using RGB image data and several classification models as an efficient, rapid, non-destructive method to detect GMO Golden Rice. Two datasets of rice images (340 samples of GMO Golden Rice and 340 samples of Traditional Rice) were processed and split for training and testing (80-20 ratio). This study found that WEKA's Random Tree and MATLAB's Trilayered Neural Network achieved 100% accuracy in detecting GMO Golden Rice, with the fastest computational efficiency in their respective platforms. Additional metrics, such as Precision and Recall, further verified the robustness of these classifiers.  This research lays the foundation for developing portable, field-deployable detection tools to empower farmers and regulators while enhancing consumer trust in GMO labeling. Furthermore, the application of ML to GMO rice detection opens new possibilities for biofortified crop monitoring. Future work may explore integrating additional rice features and GMO varieties, validating the results, and expanding this methodology to other GMO rice variants and hybrid varieties to further enhance detection accuracy and scalability.
Advancements in AI-driven Cotton Fiber Quality Assessment Through Image Processing: A Comprehensive Review Prudente, Marc Joshua; Arboleda, Edwin R.; Gutierrez, Joshua Balistoy
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.164

Abstract

The integration of artificial intelligence (AI) and image processing techniques has emerged as a transformative solution to address the limitations of traditional cotton fiber quality assessment methods, particularly the High-Volume Instrument (HVI) and Advanced Fiber Information System (AFIS), which require time-consuming manual labor. This comprehensive review examines the convergence of three key technological domains: image processing, AI/machine learning, and IoT/edge computing, in revolutionizing cotton fiber quality assessment. The review focuses on three primary image processing techniques—feature extraction, segmentation, and classification—that enable precise analysis of critical fiber properties including length, fineness, strength, and maturity. Advanced AI algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable success in automating the assessment process, achieving accuracy rates of 82-98% in fiber classification tasks. The integration of Internet of Things (IoT) devices and edge computing has further enhanced the system's capabilities, enabled real-time quality assessments and reduced processing time by up to 60% compared to traditional methods. However, several significant challenges persist, including limited availability of high-quality annotated datasets, variability in image quality due to environmental factors, model generalization across different cotton varieties, and real-time processing constraints in industrial settings. The combination of image data with additional sensor inputs, such as spectral analysis and environmental monitoring, offers potential to further enhance assessment accuracy and robustness. This review emphasizes the transformative potential of AI-driven image processing systems in revolutionizing cotton fiber quality assessment, while also identifying critical areas requiring further research for successful industrial implementation. The findings suggest that continued advancements in AI algorithms, coupled with improved IoT integration and edge computing capabilities, will be crucial for developing more robust and efficient quality assessment systems in the cotton industry.
Co-Authors Arboleda, Edwin R. Edwin Romeroso Arboleda, Edwin Romeroso Prudente, Marc Joshua