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<p>Aeroponic vertical tower farming is a cost-effective, sustainable method for optimizing the food crop-<i>Lactuca Sativa</i> (lettuce-a greeny leaf vegetable); yet accurate biomass prediction of the lettuce crop remains challenging due to the non-linear relationship between the climatic conditions and the variable lettuce growth parameters. To address this challenge, a robust machine learning model called UniTriRob regression model has been developed. This model primarily focuses on mitigating the effects of outliers and heteroskedastic errors across key growth-related parameters, including pH, total dissolved solids (TDS), temperature, electrical conductivity (EC), turbidity, humidity, light intensity and growth. The experimental validation highlights the model’s capability with high R-squared value of 97.8386% and the minimized error rate of 0.46, that outperforms the conventional forecasting methods. Hence, the model presents a viable alternative for maximizing aeroponic lettuce production efficiency and increasing yield forecast accuracy, contributing to sustainable agricultural practices.</p>

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UniTriRob: a robust machine learning regression model for predicting lettuce yields in aeroponic vertical farming

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月2日

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  • Gowtham Rajendiran,
  • Jebakumar Rethnaraj,
  • Shrikant Zade,
  • Ramakrishna Guttula,
  • Krishna Kant Pandey

摘要

Aeroponic vertical tower farming is a cost-effective, sustainable method for optimizing the food crop-Lactuca Sativa (lettuce-a greeny leaf vegetable); yet accurate biomass prediction of the lettuce crop remains challenging due to the non-linear relationship between the climatic conditions and the variable lettuce growth parameters. To address this challenge, a robust machine learning model called UniTriRob regression model has been developed. This model primarily focuses on mitigating the effects of outliers and heteroskedastic errors across key growth-related parameters, including pH, total dissolved solids (TDS), temperature, electrical conductivity (EC), turbidity, humidity, light intensity and growth. The experimental validation highlights the model’s capability with high R-squared value of 97.8386% and the minimized error rate of 0.46, that outperforms the conventional forecasting methods. Hence, the model presents a viable alternative for maximizing aeroponic lettuce production efficiency and increasing yield forecast accuracy, contributing to sustainable agricultural practices.