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

Abstract

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.

Plant phenotyping relevance

レタスのバイオマス・収量という植物形質を予測する機械学習回帰モデルの開発と実験的検証が研究の中心であり、単なる農業実験のルーチン測定ではない。

Code and data availability

The paper's aeroponic lettuce phenotype dataset (IoT sensor and growth measurements) and the UniTriRob source code are not publicly deposited; the Data availability statement says both are available from the corresponding author upon reasonable request.

No evidence-backed public reproduction asset is currently recorded.