UAV-based remote sensing for vegetation cover estimation using NDVI imagery and level sets method

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R2) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively.

Section: Introductionmentioning

confidence: 99%

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

Yao

JingChao

et al. 2017

“…The materials produced during the study can be used for analyses concerning the land cover, such as monitoring the anthropogenic changes of the environment or the extraction of tree tops [ 75 ]. Although it is common practice to use the infrared band during vegetation analysis, and it provides very satisfactory results [ 76 , 77 ], the objective of this article is to show the visible range data application in this case. The data presented in the form of a high-resolution orthophotomap allow for the supervised and unsupervised classification aimed at obtaining the land cover maps.…”

Section: Resultsmentioning

confidence: 99%

Assessment of the Possibility of Using Unmanned Aerial Vehicles (UAVs) for the Documentation of Hiking Trails in Alpine Areas

Ćwiąkała

Kocierz

Puniach

et al. 2017

The research described in this paper deals with the documentation of hiking trails in alpine areas. The study presents a novel research topic, applying up-to-date survey techniques and top quality equipment with practical applications in nature conservation. The research presents the initial part of the process—capturing imagery, photogrammetric processing, quality checking, and a discussion on possibilities of the further data analysis. The research described in this article was conducted in the Tatra National Park (TNP) in Poland, which is considered as one of the most-visited national parks in Europe. The exceptional popularity of this place is responsible for intensification of morphogenetic processes, resulting in the development of numerous forms of erosion. This article presents the outcomes of research, whose purpose was to verify the usability of UAVs to check the condition of hiking trails in alpine areas. An octocopter equipped with a non-metric camera was used for measurements. Unlike traditional methods of measuring landscape features, such a solution facilitates acquisition of quasi-continuous data that has uniform resolution throughout the study area and high spatial accuracy. It is also a relatively cheap technology, which is its main advantage over equally popular laser scanning. The paper presents the complete methodology of data acquisition in harsh conditions and demanding locations of hiking trails on steep Tatra slopes. The paper also describes stages that lead to the elaboration of basic photogrammetric products relying on structure from motion (SfM) technology and evaluates the accuracy of the materials obtained. Finally, it shows the applicability of the prepared products to the evaluation of the spatial reach and intensity of erosion along hiking trails, and to the study of plant succession or tree stand condition in the area located next to hiking trails.

“…A system for estimating vegetation indexes in an area is proposed by [14]. This system consists of a small UAV performing autonomous flights and recording videos of the ground cover using a GoPro camera with a modified infrared filter lens for video acquisition.…”

Section: Introductionmentioning

confidence: 99%

“…Although the use of more robust industrial equipment provides better system performance, this work demonstrates that the performance achieved by the proposed system is sufficient to meet the timing requirements of the application, even running on very low-cost hardware. In addition, there is no need to transmit data for processing on the ground as presented in [14]. Thus, the solution proposed in this work combines different approaches in a new embedded system proposal for autonomous control of a spraying UAV.…”

Section: Introductionmentioning

confidence: 99%

Proposal for an Embedded System Architecture Using a GNDVI Algorithm to Support UAV-Based Agrochemical Spraying

Basso

Stocchero

Henriques

et al. 2019

An important area in precision agriculture is related to the efficient use of chemicals applied onto fields. Efforts have been made to diminish their use, aiming at cost reduction and fewer chemical residues in the final agricultural products. The use of unmanned aerial vehicles (UAVs) presents itself as an attractive and cheap alternative for spraying pesticides and fertilizers compared to conventional mass spraying performed by ordinary manned aircraft. Besides being cheaper than manned aircraft, small UAVs are capable of performing fine-grained instead of the mass spraying. Observing this improved method, this paper reports the design of an embedded real-time UAV spraying control system supported by onboard image processing. The proposal uses a normalized difference vegetation index (NDVI) algorithm to detect the exact locations in which the chemicals are needed. Using this information, the automated spraying control system performs punctual applications while the UAV navigates over the crops. The system architecture is designed to run on low-cost hardware, which demands an efficient NDVI algorithm. The experiments were conducted using Raspberry Pi 3 as the embedded hardware. First, experiments in a laboratory were conducted in which the algorithm was proved to be correct and efficient. Then, field tests in real conditions were conducted for validation purposes. These validation tests were performed in an agronomic research station with the Raspberry hardware integrated into a UAV flying over a field of crops. The average CPU usage was about 20% while memory consumption was about 70 MB for high definition images, with 4% CPU usage and 20.3 MB RAM being observed for low-resolution images. The average current measured to execute the proposed algorithm was 0.11 A. The obtained results prove that the proposed solution is efficient in terms of processing and energy consumption when used in embedded hardware and provides measurements which are coherent with the commercial GreenSeeker equipment.