AI meets UAVs: A survey on AI empowered UAV perception systems for precision agriculture

J Su, X Zhu, S Li, WH Chen - Neurocomputing, 2023 - Elsevier
Precision Agriculture (PA) promises to boost crop productivity while reducing agricultural
costs and environmental footprints, and therefore is attracting ever-increasing interests in …

Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review

AD Boursianis, MS Papadopoulou, P Diamantoulakis… - Internet of Things, 2022 - Elsevier
Abstract Internet of Things (IoT) and Unmanned Aerial Vehicles (UAVs) are two hot
technologies utilized in cultivation fields, which transform traditional farming practices into a …

Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier

T Zhang, J Su, Z Xu, Y Luo, J Li - Applied Sciences, 2021 - mdpi.com
Land cover classification is able to reflect the potential natural and social process in urban
development, providing vital information to stakeholders. Recent solutions on land cover …

AquaCrop modeling to explore optimal irrigation of winter wheat for improving grain yield and water productivity

C Zhang, Z **e, Q Wang, M Tang, S Feng… - Agricultural Water …, 2022 - Elsevier
Water shortage is the main constraint resulting in low crop yields in arid and semiarid areas,
where irrigation is essential to sustain agricultural production. Unreasonable irrigation will …

Monitoring of sugar beet growth indicators using wide-dynamic-range vegetation index (WDRVI) derived from UAV multispectral images

Y Cao, GL Li, YK Luo, Q Pan, SY Zhang - Computers and Electronics in …, 2020 - Elsevier
The normalized vegetation index (NDVI) is widely used to monitor the spatial, temporal,
physiological, and biophysical characteristics of vegetation. However, if the ground biomass …

Wheat yellow rust severity detection by efficient DF-UNet and UAV multispectral imagery

T Zhang, Z Yang, Z Xu, J Li - IEEE Sensors Journal, 2022 - ieeexplore.ieee.org
Crop disease seriously affects production because of its highly destructive property. Wheat
under different levels of disease infection should be treated by various chemical strategies to …

When crops meet machine vision: A review and development framework for a low-cost nondestructive online monitoring technology in agricultural production

X Lv, X Zhang, H Gao, T He, Z Lv… - Agriculture …, 2024 - Elsevier
Abstract The Food and Agriculture Organization (FAO) has indicated that digital technology
is key for improving the resilience of food systems. Smart models have been developed for …

Improving UAV hyperspectral monitoring accuracy of summer maize soil moisture content with an ensemble learning model fusing crop physiological spectral …

H Liu, J Chen, Y **ang, H Geng, X Yang, N Yang… - European Journal of …, 2024 - Elsevier
Soil moisture content (SMC) acquisition is vital for crop stress diagnosis and precision
irrigation. However, UAV remote sensing-based SMC monitoring usually suffers from low …

[HTML][HTML] Ir-unet: Irregular segmentation u-shape network for wheat yellow rust detection by UAV multispectral imagery

T Zhang, Z Xu, J Su, Z Yang, C Liu, WH Chen, J Li - Remote Sensing, 2021 - mdpi.com
Crop disease is widely considered as one of the most pressing challenges for food crops,
and therefore an accurate crop disease detection algorithm is highly desirable for its …

Estimating rice yield by assimilating UAV-derived plant nitrogen concentration into the DSSAT model: Evaluation at different assimilation time windows

H Ge, F Ma, Z Li, C Du - Field Crops Research, 2022 - Elsevier
Accurately simulating crop growth and estimating yield play a paramount role in policy
decision making and agriculture management. To improve the accuracy of rice yield …