An Analytical Model for Assessing Tobacco Seedling Uniformity

To achieve rapid assessment and efficient analysis of the uniformity of tobacco seedlings in an intensive seedling factory, this study employs a generalized additive model (GAM) to analyze tobacco seed nursery data and screen for indicators of tobacco seedling uniformity. We evaluated Random Forest algorithm, BP Neural Network algorithm, and Support Vector Machine (SVM) algorithm. Particle Swarm Optimization (PSO) is then applied to optimize each of these models separately. This study constructs image recognition models for assessing the uniformity of tobacco seed nursery using deep learning algorithms, two optimizers, Adam and Nadam, specifically AlexNet, ResNet-101, and GoogLeNet. The research results indicate that the plant height, stem circumference, and number of effective leaves of tobacco seedlings have a significant impact on the uniformity of the tobacco seedlings. The Particle Swarm Optimized Random Forest model demonstrates the best performance, with an accuracy of 88.00%, an R² value of 0.69, and a Mean Absolute Error (MAE) of 0.13. The Adam-GoogLeNet model shows the best recognition performance, achieving an averaged accuracy of 93.89%. Overall, findings of this study provide a reasonable basis for the scientific evaluation of tobacco nursery bed uniformity and offer support for the development of tobacco seedling uniformity image recognition systems.