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<p>Accurate insect species identification is vital for pest monitoring and biodiversity assessment but traditionally demands taxonomic expertise and significant time investment. Low-code AI platforms offer accessible solutions for automating image classification, enabling non-experts to leverage machine learning. This study evaluates two low-code AI platforms—Google Cloud AutoML Vision and Google Teachable Machine—for classifying eight insect species from the Cholistan Desert, Pakistan. A dataset of 9600 images, collected between March and October 2023, was divided into training (80%), validation (10%), and testing (10%) sets. Performance was assessed using accuracy, precision, recall, F1-score, training time, and robustness to noisy data. Google Cloud AutoML Vision achieved a higher accuracy of 96.8%, with superior precision, recall, and robustness (92.3% accuracy on degraded images), while Google Teachable Machine recorded 94.5% accuracy and faster training (1.5&#xa0;h vs. 4.2&#xa0;h). Both platforms effectively classified species, with AutoML Vision excelling in performance and Teachable Machine offering speed and simplicity. These findings underscore the potential of low-code AI in entomological research, balancing accuracy and usability. Specimens are deposited at the Entomological Museum of CUVAS, Bahawalpur, Pakistan.</p>

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Performance Metrics of Low-Code AI for Insect Species Classification

  • 期刊论文

  • 发表日期：

    2025年10月21日

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  • Muhammad Waseem,
  • Santosh Kumar,
  • Sidra Hameed,
  • Muhammad Hasnain,
  • Romana Arshad,
  • Sana Iqbal,
  • Umer Farooq

摘要

Accurate insect species identification is vital for pest monitoring and biodiversity assessment but traditionally demands taxonomic expertise and significant time investment. Low-code AI platforms offer accessible solutions for automating image classification, enabling non-experts to leverage machine learning. This study evaluates two low-code AI platforms—Google Cloud AutoML Vision and Google Teachable Machine—for classifying eight insect species from the Cholistan Desert, Pakistan. A dataset of 9600 images, collected between March and October 2023, was divided into training (80%), validation (10%), and testing (10%) sets. Performance was assessed using accuracy, precision, recall, F1-score, training time, and robustness to noisy data. Google Cloud AutoML Vision achieved a higher accuracy of 96.8%, with superior precision, recall, and robustness (92.3% accuracy on degraded images), while Google Teachable Machine recorded 94.5% accuracy and faster training (1.5 h vs. 4.2 h). Both platforms effectively classified species, with AutoML Vision excelling in performance and Teachable Machine offering speed and simplicity. These findings underscore the potential of low-code AI in entomological research, balancing accuracy and usability. Specimens are deposited at the Entomological Museum of CUVAS, Bahawalpur, Pakistan.