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ARS Home » Midwest Area » Wooster, Ohio » Application Technology Research » Research » Publications at this Location » Publication #400725

Research Project: Coordinated Precision Application Technologies for Sustainable Pest Management and Crop Protection

Location: Application Technology Research

Title: Unmanned aerial vehicle based tree canopy characteristics measurement for precision spray applications

item MAHMUD, MD SULTAN - Pennsylvania State University
item HE, LONG - Pennsylvania State University
item HEINEMANN, PAUL - Pennsylvania State University
item CHOI, DAEUN - Pennsylvania State University
item Zhu, Heping

Submitted to: Smart Agricultural Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/20/2023
Publication Date: 1/29/2023
Citation: Mahmud, M., He, L., Heinemann, P., Choi, D., Zhu, H. 2023. Unmanned aerial vehicle based tree canopy characteristics measurement for precision spray applications. Smart Agricultural Technology. 4. Paper No. 100153.

Interpretive Summary: Characterization of tree canopy structures is important for preharvest crop managements in fruit production including agrochemical applications. Ground-based sensing systems for canopy characteristics measurements may not be efficient when driving through large-scale orchards with variable slopes. In this research, unmanned aerial vehicle (UAV) based imaging methods were investigated to measure apple tree canopy characteristics. A high-resolution red-green-blue (RGB) camera was attached to the UAV to capture aerial images during the petal fall growth stage. Special algorithms were developed to generate tree canopy height maps, canopy volume estimation and tree coverage areas. The UAV imagery measurements yielded a minimal average relative error for tree height measurements and a strong correlation between UAV- and ground-based tree canopy volume measurements. However, this approach experienced overestimation and underestimation of tree characteristics due to aspects including coarser spatial resolution, elevation difference, blockage of the lower canopy and overlapping trees. As a result, this research provided baselines for future investigations on UAV-RGB imagery approaches to access tree canopy characteristics that consider these aspects, which could be beneficial to growers and the scientific community on this new alternative technology potentially used for various practices including precision spray applications and canopy pruning and thinning managements.

Technical Abstract: The critical components for applying the correct amount of agrochemicals are the fruit tree characteristics such as canopy height, canopy volume, and canopy cover area. An unmanned aerial vehicle (UAV)-based tree canopy characterization system was developed using image processing approaches. A high-resolution red-green-blue (RGB) camera was mounted on the UAV to capture images of fruit trees. The captured images were used to establish a digital surface model (DSM) and a digital terrain model (DTM). A tree canopy height map was generated from the subtraction of DSM and DTM. A total of 24 apple trees were randomly targeted to measure the canopy characteristics. Region of interest (ROI) was generated across the boundary of each targeted tree. The height of all pixels within each ROI was computed separately. The pixel with maximum height was considered as the height of the respective tree. For computing canopy volume, the sum of all pixel heights from individual ROI was multiplied by the square of ground sample distance (GSD) of 5.69 mm·pixel-1. A segmentation method was employed to calculate the canopy cover area of the individual trees. The segmented canopy pixel area was divided by the total pixel area within the ROI. The results showed an average relative error of 0.2 m (6.64%) while comparing automatically measured tree height with ground measurements. For tree canopy volume, a mean absolute error of 0.25 m3 and a root mean square error of 0.33 m3 were achieved. The estimation of the spray volume from the tree canopy volumes reported the possible agrochemical requirement for spraying the fruit trees, ranging from 0.1 to 0.32 liter. The tree canopy cover measurement showed promising results compared with the visual assessment. The overall investigations suggest that the UAV-based tree canopy characteristics measurements could be a potential tool to calculate the pesticide requirement for precision spraying applications in tree fruit orchards.