The mechanisms of lettuce growth in plant factories under artificial light (PFALs) are well known, whereby the crop is generally used as a model in horticultural science. Deep learning has also been tested several times using PFALs. Despite their numerous advantages, the performance of deep learning models is commonly evaluated based only on their accuracy. Therefore, the objective of this study was to train deep neural networks and analyze the deeper abstraction of the trained models. In total, 443 images of three lettuce cultivars were used for model training, and several deep learning algorithms were compared using multivariate linear regression. Except for linear regression, all models showed adequate accuracies for the given task, and the convolutional neural network (ConvNet) model showed the highest accuracy. Based on color mapping and the distribution of the two-dimensional t-distributed stochastic neighbor embedding (t-SNE) results, ConvNet effectively perceived the differences among the lettuce cultivars under analysis. The extension of the target domain knowledge with complex models and sufficient data, similar to ConvNet with multitask learning, is possible. Therefore, deep learning algorithms should be investigated from the perspective of feature extraction.

Although greenhouse microclimates typically exhibit gradual and near-linear transitions, abrupt fluctuations in external weather conditions and actuator operations introduce nonlinear dynamics that complicate accurate interpretation and prediction. Predicting greenhouse microclimate is a key element for achieving stable and energy efficient crop production, particularly in strawberry greenhouse. However, existing greenhouse microclimate deterministic prediction models do not adequately reflect the nonlinear, time-varying characteristics of greenhouses and the inherent uncertainty in data, limiting probabilistic decision-making. In this study, we developed a probabilistic deep learning framework to estimate and interpret uncertainty while simultaneously predicting greenhouse microclimate quantitatively. The proposed one-dimensional convolutional neural network model learned the time-series characteristics of greenhouse internal and external environmental information and control data, predicting a total of nine parameters, including three-dimensional predicted values 3 h later and six-dimensional covariance elements. The model demonstrated high sharpness and calibration performance, with an average R² of 0.93, a negative log likelihood of 2.08, and a Coverage 90% of 0.901 for three microclimates. In addition, the estimated covariance matrix was used to interpret the time-varying correlations between microclimate variables, confirming local simultaneous variability not captured by global correlation analysis. These results suggest that the model in this study can provide greenhouse operators with explainable uncertainty interpretation and robust control decision support information.

Crop growth information is collected through destructive investigation, which inevitably causes discontinuity of the target. Real-time monitoring and estimation of the same target crops can lead to dynamic feedback control, considering immediate crop growth. Images are high-dimensional data containing crop growth and developmental stages and image collection is non-destructive. We propose a non-destructive growth prediction method that uses low-cost RGB images and computer vision. In this study, two methodologies were selected and verified: an image-to-growth model with crop images and a growth simulation model with estimated crop growth. The best models for each case were the vision transformer (ViT) and one-dimensional convolutional neural network (1D ConvNet). For shoot fresh weight, shoot dry weight, and leaf area of lettuce, ViT showed R² values of 0.89, 0.93, and 0.78, respectively, whereas 1D ConvNet showed 0.96, 0.94, and 0.95, respectively. These accuracies indicated that RGB images and deep neural networks can non-destructively interpret the interaction between crops and the environment. Ultimately, growers can enhance resource use efficiency by adapting real-time monitoring and prediction to feedback environmental controls to yield high-quality crops.

Mechanochromic sensors are promising for structural health monitoring as they can visually monitor the deformation caused by discoloration. Most studies have focused on the large deformation problems over 100% strain; however, it is necessary to investigate the discoloration characteristics in a small deformation range to apply it to engineering structures, such as reinforced concrete. In this study, a photonic crystal-based discoloration sensor was investigated to determine the discoloration characteristics of the red, green, and blue (RGB) as well as hue, saturation, and value (HSV) color spaces according to displacement levels. B and S showed the highest sensitivity and linear discoloration at displacements < 1 mm, whereas R and H showed significant discoloration characteristics at displacements > 1 mm. The Vision Transformer model based on RGB and HSV channels was linearly predictable up to 4 mm displacement with an accuracy of R² 0.89, but errors were found at the initial displacement within 2 mm.