The greenhouse environment plays a crucial role in providing favorable conditions for crop growth, significantly improving their quality and yield. Accurate prediction of greenhouse environmental factors is essential for their effective control. Although artificial intelligence technologies for predicting greenhouse environments have been researched recently, there are limitations in applying these to general greenhouse environments due to computing resources or issues with interpretability. Moreover, research on environmental prediction models specifically for melon greenhouses is also lacking. In this study, machine learning models based on MLR (Multiple Linear Regression), SVM (Support Vector Machine), ANN (Artificial Neural Network), and XGBoost were developed to predict the internal temperature, relative humidity, and CO₂ conditions of melon greenhouses 30 min in advance. The XGBoost model demonstrated high accuracy and stability, with an R² value of up to 0.9929 and an RPD (Residual Predictive Deviation) of 11.8464. Furthermore, the analysis of the XGBoost model’s feature importance and decision trees revealed that the model learned the complex relationships and impacts among greenhouse environmental factors. In conclusion, this study successfully developed a predictive model for a greenhouse environment for melon cultivation. The model developed in this study can facilitate an understanding and efficient management of the greenhouse environment, contributing to improvements in crop yield and quality.

Ensuring the quality of medicinal herbs in the herbal market is crucial. However, the genetic and physical similarities among medicinal materials have led to issues of mixing and counterfeit distribution, posing significant challenges to quality assurance. Recent advancements in deep learning technology, widely applied in the field of computer vision, have demonstrated the potential to classify images quickly and accurately, even those that can only be distinguished by experts. This study aimed to develop a classification model based on deep learning technology to distinguish RGB images of seeds from Ziziphus jujuba Mill. var. spinosa, Ziziphus mauritiana Lam., and Hovenia dulcis Thunb. Using three advanced convolutional neural network (CNN) architectures—ResNet-50, Inception-v3, and DenseNet-121—all models demonstrated a classification performance above 98% on the test set, with classification times as low as 23 ms. These results validate that the models and methods developed in this study can effectively distinguish Z. jujuba seeds from morphologically similar species. Furthermore, the strong performance and speed of these models make them suitable for practical use in quality inspection settings.

Herbal medicines have significant industrial value in East Asia. Zizyphus jujuba Mill. var. spinosa, used in Korea for treating insomnia, is often confused with Zizyphus mauritiana Lam., which has unverified medicinal properties yet is sold at premium prices. This misclassification undermines consumer trust and poses health risks. This study proposes a deep learning-based classification system trained on RGB-GE data, combining grayscale and edge-detected images with RGB inputs to enhance feature extraction while reducing color-dependency. Our method achieves superior generalization while maintaining cost-effectiveness. The system incorporates Grad-CAM for model interpretation and reliability. By comparing accuracy and speed across basicCNN, DenseNet, and InceptionV3 models, we identified an optimal solution for on-site herbal medicine classification, achieving 98.36% accuracy with basicCNN, ensuring reliable quality control.

Water availability critically affects basil (Ocimum basilicum L.) growth and physiological performance, making the early and precise monitoring of water-deficit responses essential for precision irrigation. However, conventional visual or biochemical methods are destructive and unsuitable for real-time assessment. This study presents a multimodal optical biosensing and 3D convolutional neural network (3D-CNN) fusion framework for phenotyping physiological responses of basil under water-deficit stress. RGB, depth, and chlorophyll fluorescence (CF) imaging were integrated to capture complementary morphological and photosynthetic information. Through the fusion of 130 optical parameter layers, the 3D-CNN model learned spatial and temporal–spectral features associated with resistance and recovery dynamics, achieving 96.9% classification accuracy—outperforming both 2D-CNN and traditional machine-learning classifiers. Feature-space visualization using t-SNE confirmed that the learned latent representations reflected biologically meaningful stress–recovery trajectories rather than superficial visual differences. This multimodal fusion framework provides a scalable and interpretable approach for the real-time, non-destructive monitoring of crop water stress, establishing a foundation for adaptive irrigation control and intelligent environmental management in precision agriculture.

Strawberry greenhouse cultivation is increasingly supported by sensing technologies, artificial intelligence (AI), and decision-support infrastructure, but their horticultural value depends on whether heterogeneous measurements can be translated into biologically meaningful crop states and practical management decisions. This review synthesizes strawberry phenotyping, multimodal sensing, AI-based crop-state interpretation, and supervised agentic coordination as a phenotyping-to-action framework for greenhouse strawberry cultivation. The reviewed studies show substantial progress in measuring and interpreting vegetative, reproductive, fruit-quality, stress-related, and environmental crop states through imaging, spectral, environmental, root-zone, and modeling approaches. However, much of the literature still emphasizes measurement accuracy, model performance, or infrastructure capability, whereas fewer studies validate whether AI-derived outputs improve crop response, management decisions, workflow, resource use, or production outcomes. The review therefore distinguishes sensing technologies for data acquisition and measurement from AI-based methods for interpretation and prediction, and examines how crop-state information can be connected to practical greenhouse decision making. It also compares established decision technologies, including expert systems, model predictive control, digital twins, and closed-loop coordination, with supervised agentic coordination as bounded decision-support concepts rather than as evidence of unrestricted autonomous control. Future work should emphasize phenotype-to-action validation, domain-aware benchmarking, and supervised deployment studies that connect model outputs with decision rules, crop outcomes, operational constraints, and grower oversight. By grounding sensing technologies and AI-based interpretation methods in crop-response validation, strawberry greenhouse systems can progress toward supervised, crop-state-driven decision support.

Accurate identification of herbal medicines is essential for drug safety, yet organoleptic evaluation of cross-sections is subjective and limited by the declining number of experts. The engineering application addressed in this work is automated and auditable herbal medicine identification. Its artificial intelligence (AI) contribution is a framework that quantifies and improves the agreement between deep model attention and pharmacopoeia-defined diagnostic regions. These diagnostic regions are the anatomical structures designated by pharmacopoeia guidelines, such as the cambium, medullary rays, and pith. We convert these criteria into per-species radial location priors through automatic cambium detection and sigma-based boundary definition, and we measure the overlap between model activation maps and these priors using the Intersection over Union (IoU). Across four Polygonaceae species, five deep networks classified accurately (F1 score 0.88 to 0.97) on background-removed images, yet their attention showed low overlap with the pharmacopoeia priors (IoU 0.18 to 0.30) on held-out data. We then introduce an architecture-aware attention supervision loss that extracts the native attention of convolutional neural network and Transformer models and guides it toward the priors. Supervision raised the overlap in four of five models (for example ConvNeXt-B from 0.267 to 0.374 and DenseNet-121 from 0.245 to 0.355, false discovery rate corrected p < 0.001) while maintaining classification accuracy. The framework provides a reproducible and quantitative basis for auditing and improving the agreement between model attention and pharmacopoeia criteria in explainable herbal medicine identification.