A Robust Algae Biomass Growth Rate Estimation using RGB Imaging
RGBイメージングを用いた頑健な藻類バイオマス成長速度推定 (AI 翻訳)
Keshinro Kazeem Kolawole, Mohamad Shukri Bin Zainal Abidin, Mohd Farizal Bin Kamaroddin, Muhammmad Sharul Azwan Bin Ramli, Sikudhan Lucas Mpuhus, Ardiansyah Rizqi
🤖 gxceed AI 要約
日本語
本研究は、RGBイメージングと機械学習を組み合わせた藻類バイオマス推定の低コストで非侵襲的な手法を提案する。屋外培養のクロレラ・ソロキニアナを対象に、色指数やテクスチャ特徴を抽出し、ランダムフォレスト、XGBoost、CNNを訓練した結果、テストデータでR²が90%を超える精度を達成。バイオ燃料や炭素回収、廃水処理への応用が期待される。
English
This study introduces a non-invasive, low-cost method for algae biomass estimation using RGB imaging and machine learning. Applied to outdoor-cultivated Chlorella sorokiniana, features like color indices and texture were extracted, and models (Random Forest, XGBoost, CNN) achieved R² > 90% on test data. The approach supports biofuel, carbon capture, and wastewater treatment applications.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも藻類バイオ燃料やCCUSの研究が進んでおり、屋外環境での簡易なバイオマスモニタリング手法は、実用化に向けた課題解決に貢献する可能性がある。特に、コスト削減とリアルタイム性が求められる現場での活用が期待される。
In the global GX context
For global GX, algae biomass is a key resource for bioenergy and carbon capture. This method addresses the lack of standardized, scalable monitoring, crucial for commercial deployment. Its non-invasive, real-time nature supports efficient operations in outdoor cultivation systems.
👥 読者別の含意
🔬研究者:This paper provides a validated ML-based framework for algae biomass estimation that can be extended to other species and environments.
🏢実務担当者:Companies in algae-based biofuel or carbon capture can adopt this low-cost imaging method for real-time biomass monitoring.
📄 Abstract（原文）
The growing demand for sustainable bioresources has spurred research into algae biomass estimation, a crucial aspect of biofuel production, carbon capture, and wastewater treatment. Despite the development of several methods to estimate algae biomass, a lack of standardized, scalable, and non-invasive biomass estimation models for outdoor environments persists. This study introduces a novel method that utilizes digital image-based RGB analysis of image models, integrated with machine learning (ML) algorithms. Traditional biomass estimation methods typically involve spectrophotometric or chemical analyses, which are labour-intensive and expensive. In contrast, the proposed approach employs low-cost RGB imaging, which enables real-time biomass quantification through image-based analysis. The research process involves data collection from controlled algae cultivation experiments, image preprocessing, and feature extraction using advanced computer vision techniques applied to Chlorella sorokiniana cultivated outdoors. Key features, such as colour indices, texture patterns, and pixel intensity distributions, were extracted from the RGB images. Various ML models, including Random Forest regressor (RFR), Extreme Gradient Boosting regressor (XGBR), and Convolutional Neural Networks (CNNs), were trained and validated to predict algal biomass concentrations. Experimental results demonstrated that the ML models accurately predicted algae biomass, with a correlation coefficient (R²) exceeding 90% in test datasets, showcasing the robustness of the proposed framework. Future research will explore multispectral extensions, adaptive ML models for various algal species, and deployment in real-world industrial settings.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.11113/elektrika.v25n1.664first seen 2026-05-15 20:49:39 · last seen 2026-06-16 05:13:44
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