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<p>Agriculture is vital for human sustenance and economic stability, with increasing global food demand necessitating innovative practices. Traditional farming methods have caused significant environmental damage, highlighting the need for sustainable practices like nutrition management. This paper addresses the emerging integration of artificial intelligence (AI) in agriculture, focusing on the specific challenge of growth stage classification of strawberry plants for optimized nutrition management. While AI has been successfully applied in various agricultural domains, such as plant stress detection and growth monitoring, the precise classification of strawberry growth stages remains underexplored. Accurate growth stage identification is vital for timely nutrient application, directly impacting yield and fruit quality. Our research identifies common gaps in existing literature, including limited or inaccessible datasets, outdated methodologies, and insufficient benchmarking. To overcome these shortcomings, we introduce a robust greenhouse-based dataset covering seven distinct strawberry growth stages, captured under diverse conditions. We then benchmark multiple state-of-the-art models on this dataset, finding that EfficientNetB7 achieves a testing accuracy of 0.837-demonstrating the promise of AI-driven approaches for precise and sustainable nutrient management in horticulture.</p>

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SmartBerry for AI-based growth stage classification and precision nutrition management in strawberry cultivation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月23日

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  • Daison Darlan,
  • Oladayo S. Ajani,
  • Joon Woo An,
  • Nan Yeon Bae,
  • Bram Lee,
  • Tusan Park,
  • Rammohan Mallipeddi

摘要

Agriculture is vital for human sustenance and economic stability, with increasing global food demand necessitating innovative practices. Traditional farming methods have caused significant environmental damage, highlighting the need for sustainable practices like nutrition management. This paper addresses the emerging integration of artificial intelligence (AI) in agriculture, focusing on the specific challenge of growth stage classification of strawberry plants for optimized nutrition management. While AI has been successfully applied in various agricultural domains, such as plant stress detection and growth monitoring, the precise classification of strawberry growth stages remains underexplored. Accurate growth stage identification is vital for timely nutrient application, directly impacting yield and fruit quality. Our research identifies common gaps in existing literature, including limited or inaccessible datasets, outdated methodologies, and insufficient benchmarking. To overcome these shortcomings, we introduce a robust greenhouse-based dataset covering seven distinct strawberry growth stages, captured under diverse conditions. We then benchmark multiple state-of-the-art models on this dataset, finding that EfficientNetB7 achieves a testing accuracy of 0.837-demonstrating the promise of AI-driven approaches for precise and sustainable nutrient management in horticulture.