Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article


Doctor of Philosophy




Jinfei Wang


Precision agriculture uses high spatial and temporal resolution soil and crop information to control the crop intra-field variability to achieve optimal economic benefit and environmental resources sustainable development. As a new imagery collection platform between airborne and ground measurements, Unmanned Aerial Vehicle (UAV) is used to collect high spatial resolution images at a user selected period for precision agriculture. Most studies extract crop parameters from the UAV-based orthomosaic imagery using spectral methods derived from the satellite and airborne based remote sensing. The new dataset, photogrammetric point cloud data (PCD), generated from the Structure from Motion (SfM) methods using the UAV-based images contains the feature’s structural information, which has not been fully utilized to extract crop’s biophysical information. This thesis explores the potential for the applications of the UAV-based photogrammetric PCD in crop biophysical variable retrieval and in final biomass and yield estimation.

First, a new moving cuboid filter is applied to the voxel of UAV-based photogrammetric PCD of winter wheat to eliminate noise points, and the crop height is calculated from the highest and lowest points in each voxel. The results show that the winter wheat height can be estimated from the UAV-based photogrammetric PCD directly with high accuracy. Secondly, a new Simulated Observation of Point Cloud (SOPC) method was designed to obtain the 3D spatial distribution of vegetation and bare ground points and calculate the gap fraction and effective leaf area index (LAIe). It reveals that the ground-based crop biophysical methods are possible to be adopted by the PCD to retrieve LAIe without ground measurements. Finally, the SOPC method derived LAIe maps were applied to the Simple Algorithm for Yield estimation (SAFY) to generate the sub-field biomass and yield maps. The pixel-based biomass and yield maps were generated in this study revealed clearly the intra-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale. The results of this thesis show that the UAV-based photogrammetric PCD is an alternative source of data in crop monitoring for precision agriculture.

Summary for Lay Audience

Precision farming is defined as a farm management system using field and crop information to identify, analyze, and manage variability within fields for optimum profitability, sustainability, and protection of the farm field. Simply, precision farming aims to do the right management practices at the right location, at the right rate, and at the right time. Precision farming offers several benefits, including improved efficiency of field inputs, increased crop productivity or quality, and reduced fertilizer contamination in the environment. Conventional agricultural management operations in the field are based on crop walking and a limited number of sample measurements. As one of the most important elements in precision farming, remote sensing acquires information about the crop and field characteristics without making physical contact with the vegetation and ground surface. The remote sensing techniques help farmers to monitor crop and field status and provide real-time information, including crop water stress, fractional cover, nitrogen content monitoring, biomass, and yield estimation. Furthermore, the products of remote sensing in agriculture can be used by government agencies to make regional policies, track agriculture activities, and provide valuable guidance for farmers on aspects such as crop health status, inventory, and expected market value. In this thesis, the potential of the UAV derived 3D point cloud data was evaluated and analyzed to demonstrate this type of data could be used to extract crop biophysical parameters and estimate the final biomass and yield in a field scale. The results of this thesis reveal that the UAV-derived 3D point cloud data is an alternative in field-scale crop monitoring and forecasting.