Rapid detection of enzyme activities in tomato leaves using visible-near infrared hyperspectral imaging integrated with image segmentation

Tomatoes (<i>Solanum lycopersicum</i> L.) are a globally consumed vegetable, rich in essential nutrients. Enzymatic activity in tomato leaves serves as a key indicator of plant health, stress response, and disease resistance. However, traditional enzyme measurement methods are destructive and time-consuming. This study employed hyperspectral imaging (HSI) combined with image segmentation to non-destructively assess three critical enzymes: peroxidase (POD), phenylalanine ammonia-lyase (PAL), and &alpha;-amylase (&alpha;-AMS). Hyperspectral images of 125 tomato leaf samples (370–1715 nm) were analyzed using a novel segmentation algorithm to extract reflectance data. Chemical assays provided reference enzyme levels (POD: 5.108 mU/g; PAL: 7.347 U/g; &alpha;-AMS: 35.856 U/g). Genetic algorithm (GA) and successive projection algorithm (SPA) optimized wavelength selection, with GA-PLS (partial least squares regression) emerging as the top-performing model. Three alternative prediction models were developed by combining GA-selected spectra with support vector machine (SVM), particle swarm optimization-backpropagation neural network (PSO-BP), and random forest (RF) algorithms. It was found that the GA-PLS model had better prediction performance for the three enzymes in the near infrared band of leaves with R_p(average coefficient of determination for the three enzymes) and RMSEP(average root means square error prediction of the three enzymes) of 0.873 and 1.489, respectively. Results confirmed that near-infrared leaf spectra provided superior enzyme prediction compared to stems. The proposed segmentation method improved accuracy over manual techniques. This study demonstrates HSI’s potential for rapid, non-destructive phytochemical analysis, offering valuable applications in precision agriculture and food quality control.