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Deep Learning-Based Plant Health State Classification Using Image Data

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2024, 10(4), pp.43-53
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : June 28, 2024
  • Accepted : August 16, 2024
  • Published : August 31, 2024

Ali Asgher Syed 1 Lee Jaehwan 1 Alvaro Fuentes 1 Sook Yoon 2 Dong Sun Park 1

1전북대학교
2목포대학교

Accredited

ABSTRACT

Tomatoes are rich in nutrients like lycopene, β-carotene, and vitamin C. However, they often suffer from biological and environmental stressors, resulting in significant yield losses. Traditional manual plant health assessments are error-prone and inefficient for large-scale production. To address this need, we collected a comprehensive dataset covering the entire life span of tomato plants, annotated across 5 health states from 1 to 5. Our study introduces an Attention-Enhanced DS-ResNet architecture with Channel-wise attention and Grouped convolution, refined with new training techniques. Our model achieved an overall accuracy of 80.2% using 5-fold cross-validation, showcasing its robustness in precisely classifying the health states of tomato plants

Citation status

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