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.

Prediction of crude protein content in field peas using near infrared reflectance spectroscopy

2006 ◽  
Vol 86 (1) ◽  
pp. 157-159 ◽  
Author(s):  
G. C. Arganosa ◽  
T. D. Warkentin ◽  
V. J. Racz ◽  
S. Blade ◽  
C. Phillips ◽  
...  
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Correlation Coefficients ◽  
Reflectance Spectroscopy ◽  
Field Pea ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

A rapid, near-infrared spectroscopic method to predict the crude protein contents of 72 field pea lines grown in Saskatchewan, both whole seeds and ground samples, was established. Correlation coefficients between the laboratory and predicted values were 0.938 and 0.952 for whole seed and ground seed, respectively. Both methods developed are adequate to support our field pea breeding programme. Key words: Field pea, near-infrared reflectance spectroscopy, crude protein

EVALUATION OF NEAR INFRARED REFLECTANCE SPECTROSCOPY FOR PREDICTING FORAGE QUALITY IN TIMOTHY

1988 ◽  
Vol 68 (2) ◽  
pp. 471-480 ◽  
Author(s):  
J. SURPRENANT ◽  
R. MICHAUD
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Phleum Pratense ◽  
Standard Errors ◽  
Wavelength Selection ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Laboratory Procedures ◽  
Phleum Pratense L

Near infrared reflectance spectroscopy (NIRS) is a newly attractive technology introduced for the analysis of agricultural products and for which new instruments have been developed. The objective of this experiment was to evaluate the capabilities of the Technicon InfraAlyzer 500 (I/A-500), a scanner monochromator type instrument, with regard to its potential utilization in the breeding of timothy (Phleum pratense L.) for higher nutritional qualities. Special features of this instrument were also used to further assess its capacities once the wavelengths were deliberately reduced and set to make the I/A-500 comparable to a 19 wavelength filter instrument such as typically found in a Technicon I/A-400R model. The investigation was performed using a total of 120 timothy samples collected from spaced plant nurseries in 1982 and 1984 and analyzed for acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP), digestibility (DMM), water solubility (WS), water retention (WR) and packed volume (PV) in the laboratory. The equations developed with the I/A-500 had R2 and r2 larger than 0.85 for ADF, NDF, CP, DDM and WS in both 1982, and 1984, and combined 1982–1984 with the exception of DDM and WS in 1982 that had r2 of 0.76. The lower R2 and r2 obtained for WR and PV were attributed to poor laboratory procedures. The standard errors of calibration of ADF, NDF, CP and DDM were all as good or better than those previously reported, with other cool season grasses. The equations developed by using only the 19 wavelengths typically found in an I/A-400R provided R2 standard errors of calibration, r2 and standard error of analysis which were quite similar to those obtained with the I/A-500. Thus, we concluded that both the unrestricted wavelength selection of the I/A-500 and the restricted wavelength selection available in an I/A-400R would be adequate to evaluate forage quality in timothy. In this experiment, the main limitations appeared to have been related to the precision of the laboratory procedures and to the lack of variation in the populations under evaluation. As a general guideline to evaluate these two factors, it is proposed to use the ratio of the standard deviation of the population over the standard deviation of the laboratory procedure. In our experimentation a ratio larger than 5.0 appeared suitable to obtain adequate calibrations.Key words: Fiber, crude protein, digestibility, forage physical properties, Phleum pratense L.

Sampling requirements for predicting cattle diet quality using faecal near-infrared reflectance spectroscopy (F.NIRS) in heterogeneous tropical rangeland pastures

2010 ◽  
Vol 32 (4) ◽  
pp. 435 ◽  
Author(s):  
I. A. White ◽  
L. P. Hunt ◽  
D. P. Poppi ◽  
S. R. Petty
Keyword(s):  
Sample Size ◽  
Protein Content ◽  
Crude Protein ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Crude Protein Content ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Collection Method

Faecal near-infrared reflectance spectroscopy (F.NIRS) provides predictive information on cattle diets and nutritional levels, useful for livestock management or for research purposes. Potential errors exist throughout the entire F.NIRS process, including the collection method. The accepted collection method involves aggregating equal amounts of faecal material from 5 to 15 animals, mixing and removing a single sample for analysis. The adequacy of this method was tested by collecting and analysing up to 70 samples from individual cattle in different paddocks. Two methods were used to determine sample size based on observed variability in dietary attributes. Variability of dietary non-grass material and crude protein content increased with paddock size, so required sample size also increased. For dietary F.NIRS predictions to be used for research, our results suggest from 20 to 51 samples are needed in small to large paddocks to accurately predict the proportion of dietary non-grass material, from 12 to 50 samples for crude protein content and from 6 to 34 samples for dry matter digestibility. Composite samples from 15 cattle provided representative means in less than 50% of the situations investigated using biologically significant precision levels, but would be adequate for management of animal nutrition. Analysis of individual samples provided additional measures of range and variability which were also informative.

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.

scholarly journals The Importance of Cultivated Plants in the Diet of Red and Roe Deer and Mouflon

2016 ◽  
Vol 64 (3) ◽  
pp. 813-819 ◽  
Author(s):  
Jiří Kamler ◽  
Miloslav Homolka
Keyword(s):  
Main Part ◽  
Red Deer ◽  
Near Infrared ◽  
Roe Deer ◽  
Vegetation Period ◽  
Food Sources ◽  
Cultivated Plants ◽  
Natural Food ◽  
Near Infrared Reflectance

The botanical composition of red and roe deer and mouflon diet was studied in the mosaic landscape in Drahanská vrchovina highlands, Czech Republic. We focused on the proportion and quality of agricultural crops and natural forest plants and estimated quality of the herbivore diet. Diet quality was monitored by the near infrared reflectance spectroscopy on the basis of nutritional quality of diet items. Red deer, roe deer and mouflon ingested all cultivated plants growing close to forest. However, the proportion of cultivated plants varied between seasons and herbivore species. The peak of crops consumption occurred in summer – when cereals spikes were ripe. The average proportion of corn for red deer was 40%. Cultivated plants were well accessible for herbivores in the study area and during vegetation period formed an important part of their diet, but the importance of cultivated plants for herbivores was lower compared with natural food resources present in forests during vegetation period. Although the main natural food sources had lower nutritional value, they formed the main part of herbivore diet in the study area. The availability of cultivated plants increases the quality of food supply during the growing season, but for herbivores the natural food sources are crucial, forming the main part of their diet both in summer and in winter. Wildlife management should reckon with feeding preferences of herbivores.

ANALYSIS OF HAY, HAYLAGE AND CORN SILAGE SAMPLES BY NEAR INFRARED REFLECTANCE SPECTROSCOPY

1985 ◽  
Vol 65 (3) ◽  
pp. 753-760 ◽  
Author(s):  
E. V. VALDES ◽  
L. G. YOUNG ◽  
I. McMILLAN ◽  
J. E. WINCH
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Corn Silage ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Mineral Contents ◽  
Infrared Reflectance Spectroscopy ◽  
Filter Type

Separate calibrations for hay, haylage and corn silage were developed to predict chemical composition by near infrared reflectance spectroscopy (NIR). A scanning type of NIR instrument was used to select the best set of wavelengths (λ) while a filter type was used to evaluate the calibrations. Reflectance (R) was recorded as log (1/R). Bias (nonrandom error) was corrected for each set of samples before the NIR analysis. Percent crude protein (CP), acid detergent fiber (ADF), calcium (Ca) and phosphorus (P) were studied in the hay samples. In addition, potassium (K) and magnesium (Mg) were included for the haylage and corn silage samples. Six hundred samples, including calibration (C) and prediction sets (PRE1 and PRE2) were used. PRE1 samples came from the same population as the C samples, whereas PRE2 samples were obtained in a different year. Accuracy of the predictions was assessed by the coefficients of determination (r2), standard error of the estimate (SEE), and coefficients of variation (CV). Crude protein was the parameter best predicted by NIR with r2, SEE and CV ranging from 0.72 to 0.96, 0.43 to 1.17 and 5.6 to 10.4, respectively. The highest SEE for crude protein were associated with the PRE2 samples for haylage and hay samples (1.09 and 1.17), respectively. NIR predictions of ADF had r2, SEE and CV values ranging from 0.21 to 0.92, 1.44 to 2.53 and 5.3% to 7.9%, respectively. Corn silage had the lowest SEE for ADF in both C and PRE1 sets. Predicting mineral contents by NIR gave high CV (10.5%–34.5%) and low r2 values (0.02–0.75). Calcium predictions had the highest variability, and P and Mg predictions the lowest.These results indicate that CP was successfully predicted by NIR. The higher SEE values for ADF may have been due to variation in the wet chemistry values of some samples. Minerals were not adequately predicted by NIR as assessed by r2, SEE and CV values. Key words: Near infrared reflectance spectroscopy, forage, chemical analysis

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