Rice blast is the primary disease affecting rice yield and quality, and its effective detection is essential to ensure rice yield and promote sustainable agricultural production. To address traditional disease detection methods’ time-consuming and inefficient nature, we proposed a method called Pyramid-YOLOv8 for rapid and accurate rice leaf blast disease detection in this study. The algorithm is built on the YOLOv8x network framework and features a multi-attention feature fusion network structure. This structure enhances the original feature pyramid structure and works with an additional detection head for improved performance. Additionally, this study designs a lightweight C2F-Pyramid module to enhance the model’s computational efficiency. In the comparison experiments, Pyramid-YOLOv8 shows excellent performance with a mean Average Precision (mAP) of 84.3%, which is an improvement of 9.9%, 4.3%, 7.4%, 6.1%, 1.5%, 3.7%, and 8.2% compared to the models Faster-RCNN, RT-DETR, YOLOv3-SPP, YOLOv5x, YOLOv9e, and YOLOv10x, respectively. Additionally, it reaches a detection speed of 62.5 FPS; the model comprises only 42.0 M parameters. Meanwhile, the model size and Floating Point Operations (FLOPs) are reduced by 41.7% and 23.8%, respectively. These results demonstrate the high efficiency of Pyramid-YOLOv8 in detecting rice leaf blast. In summary, the Pyramid-YOLOv8 algorithm developed in this study offers a robust theoretical foundation for rice disease detection and introduces a new perspective on disease management and prevention strategies in agricultural production.

Leaf blast is recognized as one of the most devastating diseases affecting rice production in the world, seriously threatening rice yield. Therefore, early detection of leaf blast is extremely important to limit the spread and propagation of the disease. In this study, a leaf blast-specific spectral vegetation index RBVI = 9.78$\left(R_{816}-R_{724}\right)$ − 2.08(${\rho }_{736}/R_{724}$) was designed to qualitatively detect the level of leaf blast disease in the canopy of a field and to improve the accuracy of early detection of leaf blast by remote sensing by unmanned aerial vehicle. Stacking integrated learning, AdaBoost, and SVM were used to compare and analyze the performance of the RBVI and traditional vegetation index for early detection of leaf blast. The results showed that the stacking model constructed based on the RBVI spectral index had the highest detection accuracy (OA: 95.9%, Kappa: 93.8%). Compared to stacking, the detection accuracy of the SVM and AdaBoost models constructed based on the RBVI is slightly degraded. Compared with conventional SVIs, the RBVI had higher accuracy in its ability to qualitatively detect leaf blast in the field. The leaf blast-specific spectral index RBVI proposed in this study can more effectively improve the accuracy of UAV remote sensing for early detection of rice leaf blast in the field and make up for the shortcomings of UAV hyperspectral detection, which is susceptible to interference by environmental factors. The results of this study can provide a simple and effective method for field management and timely control of the disease.

The stochastic and variable nature of power generated by photovoltaic (PV) systems can impact grid stability. Accurately predicting the output power of a solar PV power generation system is crucial for addressing this challenge. While short-term PV power prediction is highly accurate, the accuracy of medium- to long-term photovoltaic power predictions will face great challenges. In order to improve the accuracy of medium and long-term photovoltaic power prediction, a unique hybrid deep learning model named interactive feature trend transformer (IFTformer) has been designed. Initially, deep isolated forest (DIF) and local anomaly factor (LOF) are used to construct a parallel framework that serves as the data preprocessing module, removing outliers from raw data. The time series are subsequently decomposed into seasonal and trend components, which are modelled separately for independent study. Ultimately, the predicted trend components with the seasonal components predicted by the ProSparse Self-attention mechanism based on information interaction are fitted by the multilayer perceptron (MLP) for medium- to long-term PV power prediction. The comprehensive experimental results show that the predictive performance of IFTformer is superior to that of baseline models, with a normalised root mean square error (NRMSE) of 3.64% and a normalised mean absolute error (NMAE) of 2.44%. The IFTformer model proposed in this paper is an effective approach for medium- to long-term PV power prediction, can mitigate the impact of outliers, enhance the feature extraction ability, and improve the prediction accuracy, generalizability and robustness of medium- to long-term predictions, providing a novel perspective on medium- to long-term PV power prediction methods based on deep learning methods.

Introduction: The rapid and non-destructive estimation of rice aboveground biomass (AGB) is vital for accurate growth assessment and yield prediction. However, vegetation indices (VIs) often suffer from saturation due to high canopy coverage and vertical organs, limiting their accuracy across multiple growth stages. Therefore, this study utilizes UAV-acquired RGB and multi-spectral (MS) images during several critical rice stages to explore the potential of multi-source data fusion for accurately and cost-effectively estimating rice AGB.Methods: High-frequency texture features were extracted from RGB images using discrete wavelet transform (DWT), while low-order color moments in RGB and Lab color spaces were calculated. VIs were derived from MS images. Feature selection combined statistical analysis and modeling techniques, with collinearity removed through the Variance Inflation Factor (VIF). The relationships between AGB and the selected features were then analyzed using multiple fitting functions. Both single-type and multi-type features were used to develop individual and ensemble machine learning (ML) models for rice AGB estimation.Results: The findings indicate that: (i) Single-type features result in significant errors and low accuracy within the same sensor, but multi-feature fusion improves performance. (ii) Fusing RGB and MS image features enhances AGB estimation accuracy over single-sensor features. (iii) Ensemble ML models outperform individual models, providing higher accuracy and stability, with the best model achieving an R² of 0.8564 and RMSE of 169.32 g/m².Discussion: This study demonstrates that multi-source UAV image feature fusion with ensemble learning effectively leverages complementary data strengths, offering an efficient solution for monitoring rice AGB across growth stages.

In order to detect rice false smut quickly and accurately, a lightweight false smut detection model, YOLOv8n-MBS, was proposed in this study. The model introduces the C2f_MSEC module to replace C2f in the backbone network for better extraction of key features of false smut, enhances the feature fusion capability of the neck network for different sizes of false smut by using a weighted bidirectional feature pyramid network, and designs a group-normalized shared convolution lightweight detection head to reduce the number of parameters in the model head to achieve model lightweight. The experimental results show that YOLOv8n-MBS has an average accuracy of 93.9%, a parameter count of 1.4 M, and a model size of 3.3 MB. Compared with the SSD model, the average accuracy of the model in this study increased by 4%, the number of parameters decreased by 89.8%, and the model size decreased by 86.9%; compared with the YOLO series of YOLOv7-tiny, YOLOv5n, YOLOv5s, and YOLOv8n models, the YOLOv8n-MBS model showed outstanding performance in terms of model accuracy and model performance detection; compared to the latest YOLOv9t and YOLOv10n models, the average model accuracy increased by 2.8% and 2.2%, the number of model parameters decreased by 30% and 39.1%, and the model size decreased by 29.8% and 43.1%, respectively. This method enables more accurate and lighter-weight detection of false smut, which provides the basis for intelligent management of rice blast disease in the field and thus promotes food security.

When estimating above-ground biomass (AGB) across multiple growth stages, vegetation indices (VIs) have limitations due to saturation under dense canopies and poor sensitivity to vertically growing organs (e.g., panicles). Discrete wavelet transform (DWT) can extract multi-directional, multi-frequency texture features reflecting canopy structure changes, but its application in crop biomass monitoring is underexplored. Therefore, to evaluate whether DWT-based textures can be used to estimate AGB across multiple growth stages and whether combining VIs can improve estimation accuracy, two-year field experiments involving four rice varieties and five nitrogen treatments were conducted. UAV multispectral images were acquired during the critical growth stages, from which Vis and wavelet textures (WTs) were extracted, and novel wavelet texture indices (WTIs) were constructed. Correlation analysis guided feature selection, and simple regression, multiple linear regression, and Optuna-optimized random forest were employed to develop rice AGB estimation models. The results indicated: (1) Compared to a single WT, the WTIs exhibited higher correlation with rice AGB across different growth stages. (2) Among the three models, the RF model performed best. Specifically, using only VIs to estimate AGB during pre-heading yielded relatively higher accuracy (R² = 0.713), while using WTIs to estimate AGB during post-heading and all-stage yielded higher accuracy (R² = 0.709 and 0.668). (3) Combining WTIs with VIs significantly improves the prediction accuracy of AGB at different growth stages (R² = 0.782, 0.769, and 0.732; RMSE = 114.655, 161.779, and 223.654 g/m²), with R² improving by 10–15% and RMSE decreasing by 13–17% compared to the VIs. The study demonstrates that DWT-based textures can effectively assist in the high-precision estimation of rice AGB. Moreover, integrating WTIs with VIs enables accurate and stable prediction of rice AGB under different management practices and varieties, providing an economical and efficient method for estimating rice AGB.