Abstract Traditionally, turfgrass color has been assessed through visual ratings or light box‐based digital image analysis, methods that are either subjective or labor‐intensive. In this study, we evaluated the potential of unmanned aerial vehicle (UAV)‐based multispectral and red‐green‐blue (RGB) imagery as a high‐throughput alternative for capturing ...
Ved Parkash +9 more
wiley +1 more source
PHSNet: A Small-Target Infrared Hotspot Detection Network for Photovoltaic Modules in UAV Remote-Sensing Images. [PDF]
Gao B, Yang Y, Chen X, Cai X, Nan X.
europepmc +1 more source
Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering. [PDF]
Camenzind MP, Yu K.
europepmc +1 more source
Abstract Monitoring spatial variations in plant growth and forecasting yield before harvest provides valuable insights for optimizing agronomic decision‐making in potato (Solanum tuberosum L.) cultivation. Although unmanned aerial vehicle (UAV)‐based remote sensing has recently enabled the development of tuber fresh weight (TW) estimation models, their
Yuto Imachi +7 more
wiley +1 more source
HF-NeRF: UAV remote sensing image reconstruction via height-augmented representation and frequency-adaptive sampling. [PDF]
Wu S, Chen X, Ye C, Chen Z, Wu X.
europepmc +1 more source
Research on weed identification method in rice fields based on UAV remote sensing. [PDF]
Yu F +6 more
europepmc +1 more source
Drone‐based phenotyping of maize for multiple disease resistance and yield in breeding field trials
Abstract Improving selection for multiple disease resistance (MDR) and yield in maize (Zea mays L.) requires high‐throughput, objective phenotyping tools, particularly under field conditions where several foliar diseases co‐occur. We evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB ...
Danilo E. Moreta +7 more
wiley +1 more source
Fine-grained identification of tea plantation parcels in UAV remote sensing images based on DVIT-UNet. [PDF]
Liu Y, Xiao P, Zhou Y, Li D, Gao B.
europepmc +1 more source
Semantic segmentation of UAV remote sensing images based on edge feature fusing and multi-level upsampling integrated with Deeplabv3. [PDF]
Li X, Li Y, Ai J, Shu Z, Xia J, Xia Y.
europepmc +1 more source
Abstract Accurate prediction of grain yield (GY) remains a major challenge in plant breeding due to complex interactions between genotype, environment, and management (G × E × M) factors. Remote sensing data from unmanned aerial vehicles (UAVs) equipped with multispectral sensors have emerged as a pivotal resource for high‐throughput phenotyping.
Swas Kaushal +8 more
wiley +1 more source

