scholarly journals High Speed Crop and Weed Identification in Lettuce Fields for Precision Weeding

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 455 ◽  
Author(s):  
Lydia Elstone ◽  
Kin Yau How ◽  
Samuel Brodie ◽  
Muhammad Zulfahmi Ghazali ◽  
William P. Heath ◽  
...  
Keyword(s):  
High Speed ◽  
Near Infrared ◽  
Low Cost ◽  
Field Trials ◽  
Growth Stages ◽  
Single Shot ◽  
Plant Identification ◽  
Near Infrared Reflectance ◽  
Location Data ◽  
Post Trial

Precision weeding can significantly reduce or even eliminate the use of herbicides in farming. To achieve high-precision, individual targeting of weeds, high-speed, low-cost plant identification is essential. Our system using the red, green, and near-infrared reflectance, combined with a size differentiation method, is used to identify crops and weeds in lettuce fields. Illumination is provided by LED arrays at 525, 650, and 850 nm, and images are captured in a single-shot using a modified RGB camera. A kinematic stereo method is utilised to compensate for parallax error in images and provide accurate location data of plants. The system was verified in field trials across three lettuce fields at varying growth stages from 0.5 to 10 km/h. In-field results showed weed and crop identification rates of 56% and 69%, respectively. Post-trial processing resulted in average weed and crop identifications of 81% and 88%, respectively.

scholarly journals Low-cost, high-speed near infrared reflectance confocal microscope

2019 ◽  
Vol 10 (7) ◽  
pp. 3497 ◽  
Author(s):  
Cheng Gong ◽  
Nachiket Kulkarni ◽  
Wenbin Zhu ◽  
Christopher David Nguyen ◽  
Clara Curiel-Lewandrowski ◽  
...  
Keyword(s):  
High Speed ◽  
Near Infrared ◽  
Low Cost ◽  
Confocal Microscope ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

scholarly journals The Relative Performance of a Benchtop Scanning Monochromator and Handheld Fourier Transform Near-Infrared Reflectance Spectrometer in Predicting Forage Nutritive Value

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 658
Author(s):  
Matthew F. Digman ◽  
Jerry H. Cherney ◽  
Debbie J. R. Cherney
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Prediction Models ◽  
Low Cost ◽  
Neutral Detergent Fiber ◽  
In Vitro Digestibility ◽  
Near Infrared Reflectance ◽  
Average Decrease ◽  
Manufacturing Techniques

Advanced manufacturing techniques have enabled low-cost, on-chip spectrometers. Little research exists, however, on their performance relative to the state of technology systems. The present study compares the utility of a benchtop FOSS NIRSystems 6500 (FOSS) to a handheld NeoSpectra-Scanner (NEO) to develop models that predict the composition of dried and ground grass, and alfalfa forages. Mixed-species prediction models were developed for several forage constituents, and performance was assessed using an independent dataset. Prediction models developed with spectra from the FOSS instrument had a standard error of prediction (SEP, % DM) of 1.4, 1.8, 3.3, 1.0, 0.42, and 1.3, for neutral detergent fiber (NDF), true in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude protein (CP), respectively. The R2P for these models ranged from 0.90 to 0.97. Models developed with the NEO resulted in an average increase in SEP of 0.14 and an average decrease in R2P of 0.002.

scholarly journals Comparison of Spectral Reflectance-Based Smart Farming Tools and a Conventional Approach to Determine Herbage Mass and Grass Quality on Farm

2020 ◽  
Vol 12 (19) ◽  
pp. 3256
Author(s):  
Leonie Hart ◽  
Olivier Huguenin-Elie ◽  
Roy Latsch ◽  
Michael Simmler ◽  
Sébastien Dubois ◽  
...  
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Vegetation Period ◽  
Conventional Approach ◽  
Spatiotemporal Variability ◽  
Growth Stages ◽  
Harvest Scheduling ◽  
Analysis Model ◽  
Near Infrared Reflectance ◽  
Smart Farming

The analysis of multispectral imagery (MSI) acquired by unmanned aerial vehicles (UAVs) and mobile near-infrared reflectance spectroscopy (NIRS) used on-site has become increasingly promising for timely assessments of grassland to support farm management. However, a major challenge of these methods is their calibration, given the large spatiotemporal variability of grassland. This study evaluated the performance of two smart farming tools in determining fresh herbage mass and grass quality (dry matter, crude protein, and structural carbohydrates): an analysis model for MSI (GrassQ) and a portable on-site NIRS (HarvestLabTM 3000). We compared them to conventional look-up tables used by farmers. Surveys were undertaken on 18 multi-species grasslands located on six farms in Switzerland throughout the vegetation period in 2018. The sampled plots represented two phenological growth stages, corresponding to an age of two weeks and four to six weeks, respectively. We found that neither the performance of the smart farming tools nor the performance of the conventional approach were satisfactory for use on multi-species grasslands. The MSI-model performed poorly, with relative errors of 99.7% and 33.2% of the laboratory analyses for herbage mass and crude protein, respectively. The errors of the MSI-model were indicated to be mainly caused by grassland and environmental characteristics that differ from the relatively narrow Irish calibration dataset. The On-site NIRS showed comparable performance to the conventional Look-up Tables in determining crude protein and structural carbohydrates (error ≤ 22.2%). However, we identified that the On-site NIRS determined undried herbage quality with a systematic and correctable error. After corrections, its performance was better than the conventional approach, indicating a great potential of the On-site NIRS for decision support on grazing and harvest scheduling.

scholarly journals Layer Morphology and Ink Compatibility of Silver Nanoparticle Inkjet Inks for Near-Infrared Sintering

Nanomaterials ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 892
Author(s):  
Dieter Reenaers ◽  
Wouter Marchal ◽  
Ianto Biesmans ◽  
Philippe Nivelle ◽  
Jan D’Haen ◽  
...  
Keyword(s):  
Silver Nanoparticle ◽  
High Speed ◽  
Processing Time ◽  
Near Infrared ◽  
Printed Electronics ◽  
Low Cost ◽  
Low Complexity ◽  
Post Processing ◽  
Drop On Demand ◽  
Processing Step

The field of printed electronics is rapidly evolving, producing low cost applications with enhanced performances with transparent, stretchable properties and higher reliability. Due to the versatility of printed electronics, industry can consider the implementation of electronics in a way which was never possible before. However, a post-processing step to achieve conductive structures—known as sintering—limits the production ease and speed of printed electronics. This study addresses the issues related to fast sintering without scarifying important properties such as conductivity and surface roughness. A drop-on-demand inkjet printer is employed to deposit silver nanoparticle-based inks. The post-processing time of these inks is reduced by replacing the conventional oven sintering procedure with the state-of-the-art method, named near-infrared sintering. By doing so, the post-processing time shortens from 30–60 min to 6–8 s. Furthermore, the maximum substrate temperature during sintering is reduced from 200 °C to 120 °C. Based on the results of this study, one can conclude that near-infrared sintering is a ready-to-industrialize post-processing method for the production of printed electronics, capable of sintering inks at high speed, low temperature and with low complexity. Furthermore, it becomes clear that ink optimization plays an important role in processing inkjet printable inks, especially after being near-infrared sintered.

scholarly journals Fast, Low-Cost and Non-Destructive Physico-Chemical Analysis of Virgin Olive Oils Using Near-Infrared Reflectance Spectroscopy

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2642 ◽  
Author(s):  
Ana Garrido-Varo ◽  
María-Teresa Sánchez ◽  
María-José De la Haba ◽  
Irina Torres ◽  
Dolores Pérez-Marín
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Virgin Olive Oils ◽  
Olive Oils ◽  
Physico Chemical ◽  
Non Destructive

scholarly journals Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

2017 ◽  
Vol 52 (11) ◽  
pp. 1072-1079 ◽  
Author(s):  
Elisiane Alba ◽  
Eliziane Pivotto Mello ◽  
Juliana Marchesan ◽  
Emanuel Araújo Silva ◽  
Juliana Tramontina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Pinus Elliottii ◽  
Growth Stages ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Infrared Band ◽  
Near Infrared Reflectance ◽  
Planning And Management ◽  
Forest Planning And Management

Abstract: The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and near-infrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P.elliottii had an R2adj of 0.67 with a Syx of 31.46 m3 ha-1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.

Use of residual maximum likelihood to evaluate accuracy of two NIRS calibration procedures, relative to Kjeldahl, for determining nitrogen concentration of soybeans

1991 ◽  
Vol 71 (2) ◽  
pp. 385-392 ◽  
Author(s):  
G. B. Schaalje ◽  
H. -H. Mündel
Keyword(s):  
Maximum Likelihood ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Field Trials ◽  
Near Infrared Reflectance ◽  
Residual Maximum Likelihood ◽  
Maximum Likelihood Procedure ◽  
Calibration Methods

The accuracy of estimates of plant properties based on near-infrared reflectance spectroscopy (NIRS) varies with many factors including the biological material in question and the method used to calibrate the NIRS instrument. This study investigated the accuracy, relative to Kjeldahl analysis, of NIRS analysis based on two calibration methods in estimating nitrogen concentration of four stages and/or parts of soybean (Glycine max (L.) Merr.) plants. Samples of whole top growth at anthesis, whole top growth at maturity, whole top growth at maturity excluding seeds, and seeds were obtained from two field trials and one phytotron experiment. Two Kjeldahl determinations of nitrogen concentration were obtained for each sample, as well as reflectance values at each of 19 infrared wavelengths, using a Technicon InfraAlyser 400R. Different subsets of the sample data were used for calibration and assessment of accuracy. The instrument was calibrated using stepwise multiple linear regression (SMLR) and principal component regression (PCR). The residual maximum likelihood procedure was useful in showing that NIRS estimates based on either SMLR or PCR were at least as accurate as Kjeldahl estimates for all stages and/or parts except whole top growth at maturity excluding seeds. Key words: Calibration, principal component regression, stepwise regression

scholarly journals Single-Shot Optical Projection tomography for high-speed volumetric imaging

2021 ◽  
Author(s):  
Connor James Darling ◽  
Samuel P.X. Davis ◽  
Sunil Kumar ◽  
Paul M.W. French ◽  
James A McGinty
Keyword(s):  
High Speed ◽  
Low Cost ◽  
Single Shot ◽  
Optical Projection Tomography ◽  
Data Set ◽  
Volumetric Imaging ◽  
Optical Projection ◽  
Volumetric Reconstruction ◽  
Projection Images

We present a single-shot adaptation of Optical Projection Tomography (OPT) for high-speed volumetric snapshot imaging of dynamic mesoscopic samples. Conventional OPT has been applied to in vivo imaging of animal models such as D. rerio but the sequential acquisition of projection images required for volumetric reconstruction typically requires samples to be immobilised during the acquisition of an OPT data set. We present a proof-of-principle system capable of single-shot imaging of a 1 mm diameter volume, demonstrating camera-limited rates of up to 62.5 volumes/second, which we have applied to 3D imaging of a freely-swimming zebrafish embryo. This is achieved by recording 8 projection views simultaneously on 4 low-cost CMOS cameras. With no stage required to rotate the sample, this single-shot OPT system can be implemented with a component cost of under 5,000GBP. The system design can be adapted to different sized fields of view and may be applied to a broad range of dynamic samples, including fluid dynamics.

scholarly journals Prediction of Texture of Raw Poultry Meat by Visible and Near–Infrared Reflectance Spetroscopy

2013 ◽  
Vol 64 (5) ◽  
Author(s):  
Herlina Abdul Rahim ◽  
Rashidah Ghazali ◽  
Shafishuhaza Sahlan ◽  
Mashitah Shikh Maidin
Keyword(s):  
Prediction Accuracy ◽  
Broiler Chickens ◽  
Near Infrared ◽  
Low Cost ◽  
Nir Spectroscopy ◽  
Food Composition ◽  
Poultry Meat ◽  
Near Infrared Reflectance ◽  
Village Chickens ◽  
Raw Poultry

Near-infrared (NIR) spectroscopy is a non-destructive, low cost and fast measurement technique that is required to improve the meat texture quality prediction. In this research, visible/NIR spectroscopy has been used for the prediction of raw chicken meat texture from different types of chickens by referring to the reference data obtained from destructive measurement using a Volodkevich Bite Jaws texture analyser. The Partial Least Squares analysis shows that the prediction accuracy is higher for the Az-Zain village organic chickens (85–95%) than for village chickens (42–68%) and broiler chickens (42–44%). The high prediction accuracy and low absorbance spectra of Az-Zain village organic chickens compared to broiler and village chickens could be correlated with the food composition of the chicken meal.

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