Morphophysiological Responses of Lettuce to Irrigation Depths and Wastewater Sources with a Machine Learning Approach

Abstract

The increasing pressure on water resources has stimulated the use of treated wastewater in agricultural irrigation, although its effects on plant development remain uncertain. This study evaluated the effects of wastewater treatments and irrigation depths on the morphophysiological development of lettuce (Lactuca sativa L.). A split-plot experiment was conducted with crop cycles in the main plots and a factorial arrangement in the subplots, consisting of five water sources and five irrigation depths (50% to 150% ETc), with three replications. Seven variables were evaluated, including growth traits and water productivity. Irrigation depth significantly affected all variables (p ≤ 0.01) and was the main driver of vegetative growth, increasing shoot fresh mass, stem diameter, and plant height. In contrast, water sources showed smaller effects. Water productivity decreased with increasing irrigation depth and showed weak correlations with other variables (r ≤ 0.468). Machine learning models achieved moderate accuracy for irrigation depth prediction (≈55%), with confusion among adjacent classes, indicating detection of a gradient rather than precise classification. Prediction of water sources was low (<30%), confirming limited morphological differentiation. Plant height and stem diameter were the most informative variables. These results indicate that irrigation management has a stronger influence on lettuce growth than water source.