Aiming at the technical bottleneck of monitoring rice stalk, pest, and grass damage in the middle and lower parts of rice, this paper proposes a UAV-based image information acquisition method and disease prediction algorithm model, which provides an efficient and low-cost solution for the accurate early monitoring of rice diseases, and helps improve the scientific and intelligent level of agricultural disease prevention and control. Firstly, the UAV image acquisition system was designed and equipped with an automatic telescopic rod, 360° automatic turntable, and high-definition image sensing equipment to achieve multi-angle and high-precision data acquisition in the middle and lower regions of rice plants. At the same time, a path planning algorithm and ant colony algorithm were introduced to design the flight layout path of the UAV and improve the coverage and stability of image acquisition. In terms of image information processing, this paper proposes a multi-dimensional data fusion scheme, which combines RGB, infrared, and hyperspectral data to achieve the deep fusion of information in different bands. In disease prediction, the YOLOv8 target detection algorithm and lightweight Transformer network are adopted to determine the detection performance of small targets. The experimental results showed that the average accuracy of the YOLOv8 model (mAP@0.5) in the detection of rice curl disease was 90.13%, which was much higher than that of traditional methods such as Faster R-CNN and SSD. In addition, 1496 disease images and autonomous data sets were collected to verify that the system showed good stability and practicability in field environment.

Fungal spores that cause infectious fungal diseases in rice are mainly transmitted through air. The existing fixed, portable or vehicle-mounted fungal spore collection devices used for rice infectious diseases have several disadvantages, such as low efficiency, large volume, low precision and incomplete information. In this study, a mobile fungal spore collection device is designed, consisting of six filters called “Capture-A”, which can collect spores and other airborne particles onto a filter located on a rotating disc of six filters that can be rotated to a position allowing for the capture of six individual samples. They are captured one at a time and designed and validated by capturing spores above the rice field, and the parameters of the key components of the collector are optimized through fluid simulation and verification experiments. The parameter combination of the “Capturer-A” in the best working state is as follows: sampling vessel filter screen with aperture size of 0.150 mm, bent air duct with inner diameter of 20 mm, negative pressure fan with 1500 Pa and spore sampling of cylindrical shape. In the field test, the self-developed “Capturer-A” was compared with the existing “YFBZ3” (mobile spore collection device made by Yunfei Co., Ltd., Zhengzhou, China). The two devices were experimented on at 15 sampling points in three diseased rice fields, and the samples were examined and counted under a microscope in the laboratory. It was found that the spores of rice blast disease and rice flax spot disease of rice were contained in the samples; the number of samples collected by a single sampling vessel of “Capturer-A” was about twice that of the device “YFBZ3”in the test.

As a critical load-bearing system connecting the multi-rotor UAV airframe to the ground, the design quality of the landing gear directly affects flight safety and operational stability during takeoff and landing in complex environments. This paper presents a systematic review of the state-of-the-art in multi-rotor UAV landing gear design, with comparative references to fixed-wing alternatives where relevant. Beginning with an explicit, reproducible literature retrieval protocol, the study examines 86 peer-reviewed sources spanning 2014–2025. Structural configurations—including traditional fixed struts, telescopic landing gear, bio-inspired adaptive systems, modular designs, inflatable supports, and intelligent active-control architectures—are critically analyzed across diverse application scenarios, with particular emphasis on agricultural monitoring in paddy-fields. Key technical challenges are discussed in depth, including lightweight design, multi-environment adaptability, intelligent control, and advanced materials. In light of the specific requirements of agricultural UAVs during field operations, the paper evaluates cutting-edge research directions such as anti-rollover mechanisms, anti-sinkage capabilities, and non-destructive monitoring, supported by quantitative terrain–mechanics analysis. The review further expands the discussion of intelligent landing systems by detailing sensor-fusion architectures (vision, LiDAR, IMU/GNSS-RTK, soil-pressure transducers) and model-predictive adaptive-landing decision-making. Future development trends are outlined, proposing trajectories toward intelligentization, biomimicry, and multifunctional integration, thereby providing theoretical guidance for the optimization of landing gear design in precision agriculture.