Tomato yield prediction plays an important role in agricultural production planning and management, market supply and demand balance, and agricultural risk management. To solve the problems of low accuracy and high uncertainty of tomato yield prediction methods in solar greenhouses, based on experimental data for water and fertilizer consumption by greenhouse tomatoes in different regions over many years, this paper investigated the prediction models of greenhouse tomato yields under three different soil fertility conditions (low, medium, and high). Under these three different soil fertility conditions, greenhouse tomato yields were predicted using the neural network prediction model (NN), the neural network prediction model based on particle swarm optimization (PSO–NN), the neural network prediction model based on an adaptive inertia weight particle swarm optimization algorithm (AIWPSO–NN), and the neural network prediction model based on the improved particle swarm optimization algorithm (IPSO–NN). The experimental results demonstrate that the evaluation indexes (mean square error, mean absolute error, and R²) of the IPSO–NN prediction model proposed in this paper were superior to the other three prediction models (i.e., NN prediction model, AIWPSO–NN prediction model, and IPSO–NN prediction model) under three different soil fertility conditions. Among them, compared with the NN prediction model, the MSE of the other three prediction models under high soil fertility decreased to 0.0082, 0.0041, and 0.0036; MAE decreased to 0.0759, 0.0511, and 0.0489; R² decreased to 0.8641, 0.9323, and 0.9408. These results indicated that the IPSO–NN prediction model had a higher predictive ability for greenhouse tomato yields under three different soil fertility conditions. In view of the important role of tomato yield prediction in greenhouses, this technology may be beneficial to agricultural management and decision support.

Facility-based agriculture has rapidly advanced due to its capacity for high-intensity and year-round crop cultivation. This study evaluated the effects of different nitrogen fertilizer application rates on the growth of greenhouse tomatoes, while utilizing ¹⁵N tracing technology to explore nitrogen utilization efficiency during the growth process of facility-grown tomatoes. The results indicate that nitrogen application rates within the range of N60–N80 (93–128 kg N ha⁻¹) can optimally balance yield, nitrogen-use efficiency, and crop growth. Application rates exceeding this range do not enhance yield and lead to reduced nitrogen-use efficiency. Tomato plants exhibited a low N requirement during the seedling stage, relying primarily on native soil N stocks during the flowering stage. Fertilizer-derived N use increased during the fruiting stage. These findings demonstrate that excessive N inputs lead to diminishing returns and potential nutrient imbalances, while fully utilizing soil N stocks during the seedling and flowering stages is essential. This study emphasizes the importance of adjusting nitrogen input according to the developmental stages of the crop to optimize yield and resource utilization.

To reach the target yield of crops, nutrient management is essential. Selecting the appropriate prediction model and adjusting the nutrient supply based on the actual situation can effectively improve the nutrient utilization efficiency, crop yield, and product quality. Therefore, a prediction model of the NPK fertilizer application rate for greenhouse tomatoes under the target yield was studied in this study. Under low, medium, and high soil fertility conditions, a neural network prediction model based on the sparrow search algorithm (SSA-NN), a neural network prediction model based on the improved sparrow search algorithm (ISSA-NN), and a neural network prediction model based on the hybrid algorithm (HA-NN) were used to predict the NPK fertilizer application rate for greenhouse tomatoes. The experimental results indicated that the evaluation indexes (i.e., the mean square error (MSE), explained variance score (EVS), and coefficient of determination (R²)) of the HA-NN prediction model proposed in this study were superior than the SSA-NN and ISSA-NN prediction models under three different soil fertility conditions. Under high soil fertility, compared with the SSA-NN prediction model, the MSE of the ISSA-NN and HA-NN prediction models decreased to 0.007 and 0.005, respectively; the EVS increased to 0.871 and 0.908, respectively; and the R² increased to 0.862 and 0.899, respectively. This study showed that the HA–NN prediction model was superior in predicting the NPK fertilizer application rate for greenhouse tomatoes under three different soil fertility conditions. Due to the significance of NPK fertilizer application rate prediction for greenhouse tomatoes, this technique is expected to bring benefits to agricultural production management and decision support.

Rice serves as the staple food for over 50% of the world's population, making its yield prediction crucial for food security. The number of panicles per unit area is a core parameter for estimating rice yield. However, traditional manual counting methods suffer from low efficiency and significant subjective bias, while unmanned aerial vehicle (UAV) images used for panicle detection face challenges such as densely distributed panicles, large scale variations, and severe occlusion. To address the above challenges, this paper proposes a rice panicle detection model based on an improved You Only Look Once version 11x (YOLOv11x) architecture. The main improvements include: 1) Introducing a Bi-level Routing Attention (BRA) mechanism into the backbone network to improve the feature representation capability for small objects; 2) Adopting a Transformer-based detection head (TransHead) to capture long-term spatial dependencies; 3) Integrating a Selective Kernel (SK) Attention module to achieve dynamic multi-scale feature fusion; 4) Designing a multi-level feature fusion architecture to enhance multi-scale adaptability. Experimental results demonstrate that the improved model achieves an mAP@0.5 of 89.4% on our self-built dataset, representing a 3% improvement over the baseline YOLOv11x model. It also achieves a Precision of 87.3% and an F1-score of 84.1%, significantly outperforming mainstream algorithms such as YOLOv8 and Faster R-CNN. Additionally, panicle counting tests conducted on 300 rice panicle images show that the improved model achieves R² = 0.85, RMSE = 2.33, and rRMSE = 0.13, indicating a good fitting effect. The proposed model provides a reliable solution for intelligent in-field rice panicle detection using UAV images and holds significant importance for precise rice yield estimation.

Aiming at the fault diagnosis problem with uncertain information, this paper proposes an interval multi-dimensional discrete wavelet neural network (IMDWNN). Based on the single-scale multi-dimensional radial wavelet frame, the network construction, parameter design and weight learning algorithm are presented in detail, which effectively avoids the curse of dimensionality and interval expansion problems caused by constructing a multi-scale frame via a tensor product. Comparative experiments between the proposed IMDWNN and five mainstream methods are conducted on a public dataset, and the results demonstrate that the IMDWNN achieves superior diagnostic performance. Furthermore, the IMDWNN is applied to the rolling bearing fault diagnosis of an X-ray orientation instrument with uncertain information, and its diagnostic results are analyzed and discussed in comparison with those of the point-valued multi-dimensional discrete wavelet neural network, confirming the effectiveness and practicability of the IMDWNN.

Effective prediction of greenhouse environmental parameters can help determine the optimal environmental conditions required for crop growth, thereby improving product quality. This paper systematically analyzed various aspects of existing research in greenhouse environmental parameter prediction, including data sources, data preprocessing techniques, predicted environmental factors, and prediction models. It was observed that research in the field of greenhouse environmental parameter prediction in China primarily focused on northern regions, with daylight greenhouses as the main research target. Existing prediction models for greenhouse environmental parameters were predominantly single-factor predictions, and multi-environmental factor prediction models faced challenges such as extensive data requirements, lengthy model training times, and high modeling costs. The current state of greenhouse environmental parameter prediction models was discussed, covering machine learning models, deep neural network models, and models based on multi-method fusion applications. Recommendations were made for the development of prediction models, including the establishment of models suitable for different regions, the reduction of unnecessary information through reasonable data preprocessing and feature engineering, moderate optimization of deep model network structures, and the creation of prediction models through the fusion of multiple methods.