Applications of NIR Spectroscopy in Agricultural Production

In the future, the agriculture and food production industry will surely rely heavily on scientific advancement to see a rise in yields and ensure quality. Near Infrared (NIR) spectroscopy is one development that has tremendous potential for agricultural applications. For example, in grain production, NIR spectroscopy is being explored for its practicality in qualitative and quantitative measurements of a variety of cereal grains.

Photo by Sebastian Soerensen from Pexels

Photo by Sebastian Soerensen from Pexels

The Avantes NIRLine spectrometers offer remarkable performance in the NIR from 800-2500 nm. It includes options like InGaAs (Indium Gallium Arsenide), back-thinned CCD or CMOS detectors, triple-stage Peltier thermoelectric cooling, and next-generation ultra-low-noise electronics for a signal to noise ratio that is far superior than others.

Markers of Plant Health

Grain farmers are somewhat concerned about plant health and quite rightly so as it directly affects grain yields, and consequently profits. Plant health is also a worry for consumers due to grain contamination potentially having severe health consequences.

NIR Spectroscopy is an innovative process that requires the absorption or reflection of near-infrared light in order to facilitate qualitative and quantitative analysis of chemical and physical properties of the test sample. NIR spectroscopy is commonly used in agricultural operations to control crop parameters, such as water content, sugar content, and other indicators of ripeness.

Additionally, it measures chlorophyll fluorescence to determine the need for nitrogen-based fertilizers, or scans for bruising that is not visible to the human eye. This non-destructive and quick method of inspection has a place in every step of the food production cycle from production to grading and sorting.

Close-up of stem rust.

Close-up of stem rust. Photo by Yue Jin

Fusarium Infection Detection in Barley

The U.S. Department of Agriculture (USDA) and the National Academy of Agricultural Sciences, Rural Development Administration of South Korea, recently sponsored a joint research project. The project investigated the use of Near-Infrared Spectroscopy to recognize Fusarium infection in barley. The fungus, Fusarium, decreases grain yields and produces a toxin that can be harmful to humans and livestock that ingest infected grains.

This research group used the AvaSpec-NIR256-2.2-TEC instrument with an InGaAs (Indium, Gallium, and Arsenide) thermoelectric cooled detector with an integration time of 20 milliseconds to measure in the wavelength range of 1175-2170 nm. Using partial least squares analysis and regression modeling, a discrimination prediction model was established and offered 98-100% accuracy in recognizing grains contaminated with Fusarium spores.

Researchers detected a reflectance peak between 1555-1575 nm for all samples. Additionally, rising peaks at 1305 and 2000 nm and a falling peak in the 1900 nm wavelength range were found. The largest differences were in reflective intensity. For the normal (uninfected) hulled barley, intensity of reflectivity was 8000 counts while infected grains was at an average intensity of 9600 counts.

To trial their discrimination model, investigators in the study tested hundreds of samples. When testing uninfected samples, their model showed only one false positive and a discrimination accuracy of 99.8%. Within the infected samples, zero false negatives were identified and a discrimination accuracy of 100% was seen. Similar models for detecting Fusarium infections in other grains will be investigated in future research.

Recognizing Rice Blast Fungus

A fungus called Magnaporthe oryzae and its anamorphs such as pyricularia grisea are what cause rice blast in the agriculture industry. In many parts of the world, it is known as a major risk to food safety and stability because of the severe yield loss that it causes.

Rice Blast Symptoms on Rice Stalks.

Rice Blast Symptoms on Rice Stalks. Photo by Donald Groth

Until recently, the process used to detect rice blast was a physical inspection on the ground. This method was time-consuming and near impossible to perform comprehensive visual inspection for large-scale operations. Nowadays, an alternative is the use of great amounts of pesticides and fungicides, which itself poses risks to health and the environment, in addition to increasing the costs of production.

Near-infrared spectroscopy has been established as a cost-effective and accurate method for identifying other plant diseases at the leaf and canopy levels. Early detection technologies appropriate for large-scale operations is possible by proving the correlation between rice blast disease index and IR spectra. This allows for more effective use of agrichemicals and a more sustainable method of crop management.

In the development of their modeling for rice blast detection, researchers at the China National Rice Research Institute and the Academy of Agricultural Sciences in Hangzhou, China employed neural networks to analyze reflectance spectra. The objective was to detect spectral regions where rice reflectance changed dependent on rice neck blast disease index. Moreover, researchers selected the key wavelength bands with the sensitivity to analyze disease severity and validated their neural network-based spectral model for qualifying disease severity.

During this investigation, rice presenting with a moderate disease index displayed high raw reflectance in the 805-1000 nm range, compared to rice with a higher disease index that exhibited a lower raw reflectance under 940 nm, but higher reflectance in the 960-1000 nm range. These results were comparable to an earlier study that correlated moderate fungus infection with high reflectance in the SWIR 1135-2400 nm range and low reflectance in the NIR 709-1134 nm range.

In addition, a more serious fungal infection with low reflectance under 1297 nm and a high reflectance between 1298 and 2400 nm was identified. The re-engineered AvaSpec-NIR256/512-2.5-HSC-EVO was not available at the time this research was conducted, however is the ideal instrument for grain analysis available today.

Measurements of Crop Health

For many years, Yara International ASA has used Avantes spectrometers in a module which attaches to farm tractors used with their fertilizer applicators and measures real-time crop health. The device consists of two spectrometers focused on the visible and near-infrared in order to detect chlorophyll in plants during the application of fertilizers. The device utilizes the sun as a passive illuminant to enable the reflection measurement on crops. Based on the spectral measurements, the system controls the application of fertilizers in real time.

Yara Grain Analyzer

Yara Grain Analyzer

Chemometric Models to Predict Protein Levels in Wheat

Avantes has been in partnership with the US Department of Agriculture to develop projects that aim to measure grains during harvest. Researchers at the USDA have developed chemometric models to be able to predict protein levels in wheat and other grains. The modeling used the third overtone of the near infrared (800-1100 nm) which provides for an economical means of analyzing grains. The AvaSpec-ULS2048X16 and AvaSpec-ULS2048XL-EVO are perfect candidates for this analysis.

Use of NIR Spectroscopy in Disease Detection

There are several applications for NIR spectroscopy in the detection of plant diseases and biological contaminants in agricultural production. For example, in the detection of aflatoxins in corn, late blight disease, yellow leaf curl virus in tomatoes, leaf spot or powdery mildew in sugar beets, or yellow rust in wheat. On top of these, there are countless other crop diseases that affect yield and quality and can be detected through NIR spectroscopy.

In addition to NIR spectroscopy for agricultural disease detection, it now has a potential place in human disease detection, particularly for non-invasive diabetes monitoring, and cancer diagnosis and treatment.

The Avantes and NIR

Avantes is continually progressing in the field of NIR spectroscopy with innovative instruments being developed. Incorporation of the low-noise, high-speed communication AS7010 electronics throughout the NIRLine of spectrometers proposes a quicker signal processing. Paired with the 256 pixel or high-resolution 512-pixel TE Cooled InGaAs detector array and high-sensitivity 100 mm focal length optical bench with NA of 0.13, the AvaSpec-NIR group of instruments offer the ideal balance between sensitivity and resolution for analysis of grain, polymers, and process monitoring in the NIR 1000-2500 nm wavelength range.

References

  1. Zhang, Hao, et al. "Estimation of rice neck blasts severity using spectral reflectance based on BP-neural network." Acta physiologiae plantarum 33.6 (2011): 2461-2466.
  2. Lim, Jong Guk, et al. "Rapid and nondestructive discrimination of Fusarium Asiaticum and Fusarium Graminearum in hulled barley (Hordeum vulgare L.) using near-infrared spectroscopy." Journal of Biosystems Engineering 42.4 (2017): 301-313.

This information has been sourced, reviewed and adapted from materials provided by Avantes BV.

For more information on this source, please visit Avantes BV.

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