Estimation of tissue nitrogen content in hydroponic basil using low-cost RGB imaging and deep learning-based computer vision

Abstract Nitrogen is a critical macronutrient for plant development, yet conventional monitoring methods are often invasive, labour-intensive and costly. This study evaluates affordable, non-invasive RGB-stereo imaging to estimate nitrogen content in hydroponically grown basil in a vertical farm. Daily top-down images were used to derive plant height, projected leaf area, growth rate and the Normalised Green-Red Difference Index, which were related to chemically measured nitrogen. Plant height and growth rate emerged as the most reliable predictors, explaining over 65. 7 % of the variance in nitrogen content in the full dataset (R adj 2 = 0. 613 R₀₃₉^2=0. 613, n = 106) and over 76. 4 % in a reduced dataset (R adj 2 = 0. 714 R₀₃₉^2=0. 714, n = 73). The findings demonstrate that low-cost imaging can support data-driven, site-specific nutrient diagnostics in vertical farming while reducing destructive sampling.