In recent decades, scientific and technological developments have continued to increase in speed, with researchers focusing not only on the innovation of single technologies but also on the cross-fertilization of multidisciplinary technologies. Unmanned aerial vehicle (UAV) technology has seen great progress in many aspects, such as geometric structure, flight characteristics, and navigation control. The You Only Look Once (YOLO) algorithm was developed and has been refined over the years to provide satisfactory performance for the real-time detection and classification of multiple targets. In the context of technology cross-fusion becoming a new focus, researchers have proposed YOLO-based UAV technology (YBUT) by integrating the above two technologies. This proposed integration succeeds in strengthening the application of emerging technologies and expanding the idea of the development of YOLO algorithms and drone technology. Therefore, this paper presents the development history of YBUT with reviews of the practical applications of YBUT in engineering, transportation, agriculture, automation, and other fields. The aim is to help new users to quickly understand YBUT and to help researchers, consumers, and stakeholders to quickly understand the research progress of the technology. The future of YBUT is also discussed to help explore the application of this technology in new areas.

Rice is the world’s most important food crop and is of great importance to ensure world food security. In the rice cultivation process, weeds are a key factor that affects rice production. Weeds in the field compete with rice for sunlight, water, nutrients, and other resources, thus affecting the quality and yield of rice. The chemical treatment of weeds in rice fields using herbicides suffers from the problem of sloppy herbicide application methods. In most cases, farmers do not consider the distribution of weeds in paddy fields, but use uniform doses for uniform spraying of the whole field. Excessive use of herbicides not only pollutes the environment and causes soil and water pollution, but also leaves residues of herbicides on the crop, affecting the quality of rice. In this study, we created a weed identification index based on UAV multispectral images and constructed the WDVI_NIR vegetation index from the reflectance of three bands, RE, G, and NIR. WDVI_NIR was compared with five traditional vegetation indices, NDVI, LCI, NDRE, and OSAVI, and the results showed that WDVI_NIR was the most effective for weed identification and could clearly distinguish weeds from rice, water cotton, and soil. The weed identification method based on WDVI_NIR was constructed, and the weed index identification results were subjected to small patch removal and clustering processing operations to produce weed identification vector results. The results of the weed identification vector were verified using the confusion matrix accuracy verification method and the results showed that the weed identification accuracy could reach 93.47%, and the Kappa coefficient was 0.859. This study provides a new method for weed identification in rice fields.

Tillering fertilization is an important part of field management in rice production. As the first peak fertilizer requirement period of rice, tillering fertilization directly affects the number of tillers and the growth of rice in the middle and late stages. In order to investigate a method of constructing an accurate fertilizer prescription map in the tillering stage using an unmanned aerial vehicle (UAV) remote sensing nitrogen demand diagnosis and reduce the amount of chemical fertilizer while ensuring the rice yield, this study realized the diagnosis of the rice nitrogen nutrient demand using UAV hyperspectral remote sensing during the tilling stage fertilization window. The results showed that the fertilizer amount was determined using the characteristic waveband and remote sensing. The results showed that five rice hyperspectral variables were extracted in the range of 450–950 nm by the feature band selection and feature extraction for the inversion of rice nitrogen content, and the inversion model of rice nitrogen content constructed by the whale-optimized extreme learning machine (WOA-ELM) was better than that constructed by the whale-optimized extreme learning machine (ELM). The model coefficient of determination was 0.899 and the prescription map variable fertilizer application method based on the nitrogen content inversion results reduced the nitrogen fertilizer by 23.21%. The results of the study can provide data and a model basis for precise variable fertilizer tracking by agricultural drones in the cold rice tillering stage.

Agricultural unmanned aerial vehicles (UAVs), which are a new type of fertilizer application technology, have been rapidly developed internationally. This study combines the agronomic characteristics of rice fertilization with weighted coefficient learning-modified single-neuron adaptive proportional–integral–differential (PID) control technology to study and design an aerial real-time variable fertilizer application control system that is suitable for rice field operations in northern China. The nitrogen deficiency at the target plot is obtained from a map based on a fertilizer prescription map, and the amount of fertilizer is calculated by a variable fertilizer application algorithm. The advantages and disadvantages of the two control algorithms are analyzed by a MATLAB simulation in an indoor test, which is integrated into the spreading system to test the effect of actual spreading. A three-factor, three-level orthogonal test of fertilizer-spreading performance is designed for an outdoor test, and the coefficient of variation of particle distribution Cv (a) as well as the relative error of fertilizer application λ (b) are the evaluation indices. The spreading performance of the spreading system is the best and can effectively achieve accurate variable fertilizer application when the baffle opening is 4%, spreading disc speed is 600 r/min, and flight height is 2 m, with a and b of evaluation indexes of 11.98% and 7.02%, respectively. The control error of the spreading volume is 7.30%, and the monitoring error of the speed measurement module is less than 30 r/min. The results show that the centrifugal variable fertilizer spreader improves the uniformity of fertilizer spreading and the accuracy of fertilizer application, which enhances the spreading performance of the centrifugal variable fertilizer spreader.

Unmanned Aerial Vehicle (UAV), as a new generation of intelligent equipment, has gradually become an essential tool across multiple industries due to its high maneuverability and strong task adaptability. UAV payload technology (UPT) serves as a key support for enhancing mission performance and expanding application scenarios. UPT is being rapidly integrated into agriculture and other key fields, emerging as a driving force for the low-altitude economy and intelligent operations. This study systematically analyzed and discussed the development status of UPT, its typical application scenarios, and the challenges faced. By conducting a comprehensive review of global research on UPT from 2012 to 2025, this review summarized research hotspots and revealed evolutionary trends. The findings demonstrated that UPT had made notable progress in typical application areas, including crop monitoring, precision agricultural operations, agricultural product harvesting and aerial transportation, power line inspection, emergency rescue, and logistics. However, UPT was still constrained by limited autonomous perception and path planning capabilities, insufficient universality of payload platforms, a lack of standardized device interfaces, as well as challenges related to endurance, communication, and operational stability under adverse weather conditions. Future research should focus on lightweight and multifunctional payload design, intelligent operation control, and modular and standardized integration, while building a “satellite-UAV-ground” collaborative perception and decision-making system. The outcomes of this study provide both theoretical reference and practical guidance for promoting UAV adoption in agriculture and other low-altitude application scenarios, thereby contributing to the sustainable development of smart agriculture and the low-altitude economy.

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.