UAV imaging with low-cost multispectral imaging system for precision agriculture applications

2017 ◽  
Author(s):  
J. L. E. Honrado ◽  
D. B. Solpico ◽  
C. M. Favila ◽  
E. Tongson ◽  
G. L. Tangonan ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Low Cost

scholarly journals Development of a Low-Cost Narrow Band Multispectral Imaging System Coupled with Chemometric Analysis for Rapid Detection of Rice False Smut in Rice Seed

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1209 ◽  
Author(s):  
Haiyong Weng ◽  
Ya Tian ◽  
Na Wu ◽  
Xiaoling Li ◽  
Biyun Yang ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Imaging System ◽  
Narrow Band ◽  
Low Cost ◽  
False Negative ◽  
False Negative Rate ◽  
Rice False Smut ◽  
Negative Rate ◽  
False Smut ◽  
Rice Seeds

Spectral imaging is a promising technique for detecting the quality of rice seeds. However, the high cost of the system has limited it to more practical applications. The study was aimed to develop a low-cost narrow band multispectral imaging system for detecting rice false smut (RFS) in rice seeds. Two different cultivars of rice seeds were artificially inoculated with RFS. Results have demonstrated that spectral features at 460, 520, 660, 740, 850, and 940 nm were well linked to the RFS. It achieved an overall accuracy of 98.7% with a false negative rate of 3.2% for Zheliang, and 91.4% with 6.7% for Xiushui, respectively, using the least squares-support vector machine. Moreover, the robustness of the model was validated through transferring the model of Zheliang to Xiushui with the overall accuracy of 90.3% and false negative rate of 7.8%. These results demonstrate the feasibility of the developed system for RFS identification with a low detecting cost.

scholarly journals Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment

2020 ◽  
Vol 88 (5) ◽  
pp. 407-422 ◽  
Author(s):  
Ulrike Lussem ◽  
Jürgen Schellberg ◽  
Georg Bareth
Keyword(s):  
Plant Community ◽  
Precision Agriculture ◽  
Imaging System ◽  
Low Cost ◽  
Block Design ◽  
Image Data ◽  
Grassland Management ◽  
Forage Yield ◽  
Management Regime ◽  
Aerial Imaging

Abstract Monitoring and predicting above ground biomass yield of grasslands are of key importance for grassland management. Established manual methods such as clipping or rising plate meter measurements provide accurate estimates of forage yield, but are time consuming and labor intensive, and do not provide spatially continuous data as required for precision agriculture applications. Therefore, the main objective of this study is to investigate the potential of sward height metrics derived from low-cost unmanned aerial vehicle-based image data to predict forage yield. The study was conducted over a period of 3 consecutive years (2014–2016) at the Rengen Grassland Experiment (RGE) in Germany. The RGE was established in 1941 and is since then under the same management regime of five treatments in a random block design and two harvest cuts per year. For UAV-based image acquisition, a DJI Phantom 2 with a mounted Canon Powershot S110 was used as a low-cost aerial imaging system. The data were investigated at different levels (e.g., harvest date-specific, year-specific, and plant community-specific). A pooled data model resulted in an R2 of 0.65 with a RMSE of 956.57 kg ha−1, although cut-specific or date-specific models yielded better results. In general, the UAV-based metrics outperformed the traditional rising plate meter measurements, but was affected by the timing of the harvest cut and plant community.

scholarly journals Development of an automated pigmentation phenotyping and low- cost multispectral imaging system

2021 ◽  
Author(s):  
Changhyeon Kim ◽  
Kahlin Wacker ◽  
Benjamin Sidore ◽  
Tony Pham ◽  
Mark Haidekker ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Imaging System ◽  
Low Cost

scholarly journals Portable, low-cost multispectral imaging system: design, development, validation, and utilization

2018 ◽  
Vol 23 (12) ◽  
pp. 1 ◽  
Author(s):  
Frank J. Bolton ◽  
Amir S. Bernat ◽  
Kfir Bar-Am ◽  
David Levitz ◽  
Steven Jacques
Keyword(s):  
System Design ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Low Cost ◽  
Design Development ◽  
Imaging System Design

scholarly journals Spectral Reflectance Estimation Using Projector with Unknown Spectral Power Distribution

2020 ◽  
Vol 2020 (28) ◽  
pp. 205-209
Author(s):  
Hironori Hidaka ◽  
Yusuke Monno ◽  
Masatoshi Okutomi
Keyword(s):  
Power Distribution ◽  
Spectral Reflectance ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Spectral Power ◽  
Low Cost ◽  
Joint Estimation ◽  
Spectral Power Distribution ◽  
Additional Equipment ◽  
Low Dimensional

A lighting-based multispectral imaging system using an RGB camera and a projector is one of the most practical and low-cost systems to acquire multispectral observations for estimating the scene's spectral reflectance information. However, existing projector-based systems assume that the spectral power distribution (SPD) of each projector primary is known, which requires additional equipment such as a spectrometer to measure the SPD. In this paper, we present a method for jointly estimating the spectral reflectance and the SPD of each projector primary. In addition to adopting a common spectral reflectance basis model, we model the projector's SPD by a low-dimensional model using basis functions obtained by a newly collected projector's SPD database. Then, the spectral reflectances and the projector's SPDs are alternatively estimated based on the basis models. We experimentally show the performance of our joint estimation using a different number of projected illuminations and investigate the potential of the spectral reflectance estimation using a projector with unknown SPD.

Portable multispectral imaging system based on Raspberry Pi

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 322-329 ◽  
Author(s):  
Nuria Lopez-Ruiz ◽  
Fernando Granados-Ortega ◽  
Miguel Angel Carvajal ◽  
Antonio Martinez-Olmos
Keyword(s):  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Light Emitting Diode ◽  
Low Cost ◽  
Image Sensor ◽  
Raspberry Pi ◽  
Image Processing Algorithm ◽  
Content Type

Purpose In this work, the authors aim to present a compact low-cost and portable spectral imaging system for general purposes. The developed system provides information that can be used for a fast in situ identification and classification of samples based on the analysis of captured images. The connectivity of the instrument allows a deeper analysis of the images in an external computer. Design/methodology/approach The wavelength selection of the system is carried out by light multiplexing through a light-emitting diode panel where eight wavelengths covering the spectrum from ultraviolet (UV) to near-infrared region (NIR) have been included. The image sensor used is a red green blue – infrared (RGB-IR) micro-camera controlled by a Raspberry Pi board where a basic image processing algorithm has been programmed. It allows the visualization in an integrated display of the reflectance and the histogram of the images at each wavelength, including UV and NIRs. Findings The prototype has been tested by analyzing several samples in a variety of applications such as detection of damaged, over-ripe and sprayed fruit, classification of different type of plastic materials and determination of properties of water. Originality/value The designed system presents some advantages as being non-expensive and portable in comparison to other multispectral imaging systems. The low-cost and size of the camera module connected to the Raspberry Pi provides a compact instrument for general purposes.

scholarly journals Low-Cost Multispectral System Design for Pigment Analysis in Works of Art

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5138
Author(s):  
Tania Kleynhans ◽  
David W. Messinger ◽  
Roger L. Easton ◽  
John K. Delaney
Keyword(s):  
Multispectral Imaging ◽  
Imaging System ◽  
Low Cost ◽  
Scanning System ◽  
Prototype System ◽  
Imaging Data ◽  
Pigment Analysis ◽  
X Ray ◽  
Spectral Bands ◽  
Works Of Art

To better understand and preserve works of art, knowledge is needed about the pigments used to create the artwork. Various noninvasive techniques have been used previously to create pigment maps, such as combining X-ray fluorescence and hyperspectral imaging data. Unfortunately, most museums have limited funding for the expense of specialized research equipment, such as hyperspectral reflectance imaging systems. However, many museums have hand-held point X-ray fluorescence systems attached to motorized easels for scanning artwork. To assist museums in acquiring data that can produce similar results to that of HSI systems, while minimizing equipment costs, this study designed and modeled a prototype system to demonstrate the expected performance of a low-cost multispectral system that can be attached to existing motorized easels. We show that multispectral systems with a well-chosen set of spectral bands can often produce classification maps with value on par with hyperspectral systems. This study analyzed the potential for capturing data with a point scanning system through predefined filters. By applying the system and noise modeling parameters to HSI data captured from a 14th-Century illumination, the study reveals that the proposed multispectral imaging system is a viable option for this need.

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