Predicting foliar nutrient concentrations and nutrient deficiencies of hydroponic lettuce using hyperspectral imaging

Effective management of plant essential nutrients is necessary for hydroponically grown lettuce to achieve high yields and maintain production. This study investigated in situ hyperspectral imaging of hydroponic lettuce for predicting nutrient concentrations and identifying nutrient deficiencies for: nitrogen (N), phosphorous (P), potassium (K), calcium (Ca), magnesium (Mg), and sulphur (S). A greenhouse study was conducted using 'Salanova Green' lettuce grown with controlled solution treatments with varying macronutrient fertility rates of 0, 8, 16, 32, 64, and 100% each for N, P, K, Ca, Mg, and S. Plants were imaged using a hyperspectral line scanner at six and eight weeks after transplanting; then, plant tissues were sampled, and nutrient concentrations measured. Partial least squares regression (PLSR) models were developed to predict nutrient concentrations for each nutrient individually (PLS1) and for all six nutrient concentrations (PLS2). Several binary classification models were also developed to predict nutrient deficiencies. The PLS1 and PLS2 models predicted nutrient concentrations with Coefficient of Determination (R2) values from 0.60 to 0.88 for N, P, K, and S, while results for Ca and Mg yielded R2 values of 0.12-0.34, for both harvest dates. Similarly, plants deficient in N, P, K, and S were classified more accurately compared to plants deficient in Ca and Mg for both harvest dates, with F1 values (F-scores) ranging from 0.71 to 1.00, with the exception of K which had F1 scores of 0.40-0.67. Overall, results indicate that both leaf tissue nutrient concentration and nutrient deficiencies can be predicted using hyperspectral data collected for whole plants. & COPY; 2023 IAgrE. Published by Elsevier Ltd. All rights reserved.