The accurate prediction of crop yield and key indicator compounds has become crucial in high-value agriculture under complex environmental conditions. This article proposes a structured multilayer neural network model that integrates the semantic grouping of climate variables, intragroup and intergroup interaction learning, and time-series cumulative response analysis. Climate and environmental variables are categorized into six semantic groups (time, temperature, humidity, pressure/cloud, wind, and precipitation/snowfall). Further, a one-dimensional convolutional neural network (CNN) is employed to capture intragroup interactions, an artificial neural network (ANN) is employed to model intergroup nonlinear relationships, and a long short-term memory (LSTM) network is employed to learn cumulative temporal effects. In addition, sparse events, e.g., rainfall and snowfall, are categorized into independent groups, and local temporal patterns are extracted using sequential CNN filters, structurally restoring their predictive contributions. The proposed model is validated experimentally on real climate and phenological data from five European hop varieties, achieving R² ≥ 0.94 in phenol contents prediction, outperforming conventional ANN and CNN–LSTM models. To enhance the training speed and scalability, the proposed model incorporates high-performance computing features, e.g., OpenMP-style CPU parallel settings and GPU-aware memory optimization within the TensorFlow framework. The proposed framework offers generalizability and practical applicability for precision agriculture and smart-farming systems by effectively modeling sparsity, temporality, and variable interactions.

Recent studies have focused on using big-data-based machine learning to address the effects of climate change scenarios on the production and quality of medicinal plants. Challenges relating to data collection can hinder the analysis of key feature variables that affect the quality of medicinal plants. In the study presented herein, we analyzed feature variables that affect the phenolic content of Korean Cnidium officinale Makino (C. officinale Makino) under different climate change scenarios. We applied different climate change scenarios based on environmental information obtained from Yeongju city, Gyeongsangbuk-do, Republic of Korea, and cultivated C. officinale Makino to collect data. The collected data included 3237, 75, and 45 records, and data augmentation was performed to address this data imbalance. We designed a function based on the DPPH value to set the phenolic content grade in C. officinale Makino and proposed a stacking ensemble model for predicting the total phenol contents and classifying the phenolic content grades. The regression model in the performance evaluation presented an improvement of 6.23–7.72% in terms of the MAPE; in comparison, the classification model demonstrated a 2.48–3.34% better performance in terms of accuracy. The classification accuracy was >0.825 when classifying phenol content grades using the predicted total phenol content values from the regression model, and the area under the curve values of the model indicated high model fitness (0.987–0.981). We plan to identify the key feature variables for the optimal cultivation of C. officinale Makino and explore the relationships among these feature variables.