Correlation dependence between feed moisture and its optical properties using sunflower cake as an example
https://doi.org/10.32634/0869-8155-2025-392-03-110-115
Abstract
Each type of agricultural feed has unique optical properties and nutritional value characteristics that must be taken into account at the stage of drawing up an animal feeding diet to ensure the rational management of economic processes at industrial livestock enterprises.
Arbitrage chemical methods for assessing the moisture content and nutritional value of agricultural feed are laborious in the implementation. World practice shows that optical methods can serve as an effective alternative for the development and manufacture of a new generation instrument base that allows determining the qualitative properties of materials, including agricultural feed (nutritional value).
The most time-consuming procedure for developing optical devices is to obtain optical calibrations (see definition), which provide interpretation of the values of an indirect parameter that characterizes the nutritional value of agricultural feed.
The study describes the process of obtaining optical calibrations by varying the control indicator (using the example of feed moisture), followed by building a correlation between the value of an indirect parameter (photoluminescence intensity) and the control indicator. Including in a portable express analyzer operating on the basis of photoluminescence.
The proposed method of forming a control indicator can be used to obtain optical calibrations for rapid determination of total fat content and other indicators of nutritional value.
About the Authors
D. A. Blagov
Federal Scientific Agroengineering Center VIM
Russian Federation
Russian Federation
Dmitry A. Blagov - Candidate of Biological Sciences, Laboratory of Innovative Technologies and Technical Means of Feeding in Animal Husbandry.
5/1 Institutsky proezd, Moscow, 109428
E. A. Nikitin
Federal Scientific Agroengineering Center VIM
Russian Federation
Russian Federation
Evgeny A. Nikitin - Candidate of Technical Sciences, Laboratory of Innovative Technologies and Technical Means of Feeding in Animal Husbandry.
5/1 Institutsky proezd, Moscow, 109428
M. V. Belyakov
Federal Scientific Agroengineering Center VIM
Russian Federation
Russian Federation
Mikhail V. Belyakov - Doctor of Technical Sciences, Laboratory of Innovative Technologies and Technical Means of Feeding in Animal Husbandry.
5/1 Institutsky proezd, Moscow, 109428
References
1. Kirsanov V.V., Belyakov M.V., Nikitin E.A., Blagov D.A., Mikhailichenko S.M. Spectral analysis as a tool for determining the mixing quality of a multicomponent feed mixture. Vestnik Bashkir State Agrarian University. 2023; (3): 41‒46 (in Russian). https://www.elibrary.ru/rcobbx
2. Pavkin D.Y., Belyakov M.V., Nikitin E.A., Efremenkov I.Y., Golyshkov I.A. Determination of the Dependences of the Nutritional Value of Corn Silage and Photoluminescent Properties. Applied Sciences. 2023; 18(13): 10444. https://doi.org/10.3390/app131810444
3. Morozov N.M., Kirsanov V.V., Tsench Yu.S. Historical and Analytical Assessment of Automation and Robotization for Milking Processes. Agricultural Machinery and Technologies. 2023; 17(1): 11–18 (in Russian). https://doi.org/10.22314/2073-7599-2023-17-1-11-18
4. Dorokhov A.S., Kirsanov V.V., Pavkin D.Yu., Nikitin E.A., Mikhailichenko S.M., Blagov D.A. Analysis of robotic feeders for livestock complexes for the cattle breeding. Electrical technology and equipment in the agro-industrial complex. 2023; 70(1): 94‒104 (in Russian). https://doi.org/10.22314/2658-4859-2023-70-1-94-104
5. Buelvas R.M., Adamchuk V.I., Leksono E., Tikasz P., Lefsrud M., Holoszkiewicz J. Biomass estimation from canopy measurements for leafy vegetables based on ultrasonic and laser sensors. Computers and Electronics in Agriculture. 2019; 164: 104896. https://doi.org/10.1016/j.compag.2019.104896
6. Giménez-Gallego J., González-Teruel J.D., Soto-Valles F., Jiménez-Buendía M., Navarro-Hellín H., Torres-Sánchez R. Intelligent thermal image-based sensor for affordable measurement of crop canopy temperature. Computers and Electronics in Agriculture. 2021; 188: 106319. https://doi.org/10.1016/j.compag.2021.106319
7. Michelucci U., Venturini F. Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence Sensing. Applied Sciences. 2019; 9(22): 4748. https://doi.org/10.3390/app9224748
8. Qin Y. et al. Acid Detection through a Rapid and Sensitive Amplified Luminescent Proximity Homogeneous Assay. Toxins. 2023; 15(8): 501. https://doi.org/10.3390/toxins15080501
9. Belyakov M.V. Optical spectral qualities of plant seeds with different moisture. Bulletin NGIEI. 2016; (4): 38‒50 (in Russian). https://www.elibrary.ru/vyuqrb
10. Zhang D., Wang Z., Jin N., Gu C., Chen Y., Huang Y. Evaluation of Efficacy of Fungicides for Control of Wheat Fusarium Head Blight Based on Digital Imaging. IEEE Access. 2020; 8: 109876–109890. https://doi.org/10.1109/ACCESS.2020.3001652
11. Sanjay M., Kalpana B. Early Mass Diagnosis of Fusarium Wilt in Banana Cultivations using an E-Nose Integrated Autonomous Rover System. International Journal of Applied Sciences and Biotechnology. 2017; 5(2): 261–266. https://doi.org/10.3126/ijasbt.v5i2.17621
12. Karadağ K., Tenekeci M.E., Taşaltın R., Bilgili A. Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance. Sustainable Computing: Informatics and Systems. 2020; 28: 100299. https://doi.org/10.1016/j.suscom.2019.01.001
13. Hong S. et al. Visualization analysis of crop spectral index based on RGB-NIR image matching. Spectroscopy and Spectral Analysis. 2019; 39(11): 3493‒3500 (in Chinese).
14. Yuan H. et al. Recent Advances in Fluorescent Nanoprobes for Food Safety Detection. Molecules. 2023; 28(14): 5604. https://doi.org/10.3390/molecules28145604
15. Liu S. et al. Fluorescence spectra of nutrients in chicken and skin under baking conditions. Optik. 2020; 18: 164795. https://doi.org/10.1016/j.ijleo.2020.164795
16. Alshehawy A.M., Mansour D.-E.A., Ghali M., Lehtonen M., Darwish M.M.F. Photoluminescence Spectroscopy Measurements for Effective Condition Assessment of Transformer Insulating Oil. Processes. 2021; 9(5): 732. https://doi.org/10.3390/pr9050732
17. Iyer S.N., Behary N., Nierstrasz V., Guan J., Chen G. Study of photoluminescence property on cellulosic fabric using multifunctional biomaterials riboflavin and its derivative Flavin mononucleotide. Scientific Reports. 2019; 9: 8696. https://doi.org/10.1038/s41598-019-45021-5
18. Ding K., Xiao L., Weng G. Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation. Signal Processing. 2017; 134: 224‒233. https://doi.org/10.1016/j.sigpro.2016.12.021
19. Jin R., Weng G. Active contours driven by adaptive functions and fuzzy c-means energy for fast image segmentation. Signal Processing. 2019; 163: 1‒10. https://doi.org/10.1016/j.sigpro.2019.05.002
20. Chaikov L.L. et al. Two Convenient Methods for Detection of Non-Dairy Fats in Butter by Dynamic Light Scattering and Luminescence Spectroscopy. Applied Sciences. 2023; 13(15): 8563. https://doi.org/10.3390/app13158563
21. Halachmi I., Ben Meir Y., Miron J., Maltz E. Feeding behavior improves prediction of dairy cow voluntary feed intake but cannot serve as the sole indicator. Animal. 2016; 10(9): 1501‒1506. https://doi.org/10.1017/S1751731115001809
Review
For citations:
Blagov D.A., Nikitin E.A., Belyakov M.V. Correlation dependence between feed moisture and its optical properties using sunflower cake as an example. Agrarian science. 2025;(3):110-115. https://doi.org/10.32634/0869-8155-2025-392-03-110-115
Views:
119
Email this article 