An Application of Deep Learning for Predicting Tomato Growth After Seed Irradiation

Optimizing growth conditions for tomato crops is essential due to their sensitivity to environmental factors. Pre-sowing seed treatments, particularly irradiation with specific wavelengths, can significantly influence germination and subsequent plant development. This study investigates how five laser wavelengths (red 630 nm, green 530 nm, blue 470 nm, ultraviolet 390 nm, infrared 780 nm) and four exposure durations (15, 30, 45, 60 minutes), along with a control sample, affected two tomato cultivars (Moneymaker and Bull's Heart). A total of 42 treatment combinations were tested using tabular experimental data (numeric input features: cultivar, wavelength, exposure time), with growth outcomes, such as plant height and fruit yield, recorded. A feedforward neural network with two hidden layers was trained to predict the final height of the plant from the seed treatment parameters. The model achieved a strong predictive accuracy (R2≈0.92 and MSE≈9.5 cm2) using an 80:20 train-test data split. This study demonstrates that deep learning can effectively model plant growth responses to physical seed priming and can be used to optimize treatment protocols for improved agricultural outcomes.