<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">sibvest</journal-id><journal-title-group><journal-title xml:lang="ru">Сибирский вестник сельскохозяйственной науки</journal-title><trans-title-group xml:lang="en"><trans-title>Siberian Herald of Agricultural Science</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">0370-8799</issn><issn pub-type="epub">2658-462X</issn><publisher><publisher-name>Siberian Federal Scientific Centre of Agro-BioTechnologies of the Russian Academy of Sciences</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26898/0370-8799-2025-1-11</article-id><article-id custom-type="elpub" pub-id-type="custom">sibvest-2174</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>МЕХАНИЗАЦИЯ, АВТОМАТИЗАЦИЯ, МОДЕЛИРОВАНИЕ И ИНФОРМАЦИОННОЕ ОБЕСПЕЧЕНИЕ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>MECHANISATION, AUTOMATION, MODELLING AND DATAWARE</subject></subj-group></article-categories><title-group><article-title>Перспективный метод диагностики болезней растений и определения их фенотипа</article-title><trans-title-group xml:lang="en"><trans-title>A promising method for diagnosing plant diseases and determining their phenotype</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Алейников</surname><given-names>А. Ф.</given-names></name><name name-style="western" xml:lang="en"><surname>Aleynikov</surname><given-names>A. F.</given-names></name></name-alternatives><bio xml:lang="ru"><p>главный научный сотрудник, доктор технических наук, профессор, профессор кафедры</p><p>630501; а/я 463; Новосибирская область; Краснообск; Новосибирск</p></bio><bio xml:lang="en"><p>Alexander F. Aleynikov, Head Researcher, Doctor of Science in Engineering, Professor; Chair Professor</p><p>630501; PO Box 463; Novosibirsk region; Krasnoobsk; Novosibirsk</p></bio><email xlink:type="simple">fti2009@yandex.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Сибирский федеральный научный центр агробиотехнологий Российской академии наук; Новосибирский государственный технический университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Siberian Federal Scientific Centre of Agro-BioTechnologies of the Russian Academy of Sciences; Novosibirsk State Technical University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>31</day><month>08</month><year>2025</year></pub-date><volume>55</volume><issue>1</issue><fpage>90</fpage><lpage>106</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Алейников А.Ф., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Алейников А.Ф.</copyright-holder><copyright-holder xml:lang="en">Aleynikov A.F.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://sibvest.elpub.ru/jour/article/view/2174">https://sibvest.elpub.ru/jour/article/view/2174</self-uri><abstract><p>   Традиционные методы ранней диагностики болезней, такие как метод чистых культур, микроскопический, микологический, полимеразой цепной реакции, иммуноферментного анализа, инвазивны, требуют наличия высококвалифицированного персонала, дорогостоящего оборудования и непригодны для их результативного использования на практике. В связи с тем, что здоровье растений является основополагающим показателем при оценке фенотипа культурного растения, рассмотрены современные неинвазивные методы ранней диагностики и определения фенотипа растений.</p><p>   Цель исследования – выбор рационального метода для осуществления ранней диагностики болезней растений и определения их фенотипа непосредственно на поле возделываемой культуры.</p><p>   Приведены преимущества и недостатки метода технического зрения, основанного на анализе изменений цветовых параметров полученных RGB-изображений листьев растений; флуоресцентного анализа, при котором оценивают эффективность фотосинтеза; методов мультиспектральной и гиперспектральной визуализации, осуществляемых путем определения ограниченного или непрерывного спектра, отраженного от поверхности листьев растений; метода тепловизионной визуализации, при которой фиксируют распределение инфракрасного излучения, испускаемого растением. Анализ методов показал, что определение рассеивания тепловой энергии – перспективный потенциальный показатель здоровья и наличия болезней. Кроме того, при воздействии большинства факторов окружающей среды изменяются тепловые свойства органов растения – листа, стебля, корня, органов размножения. Объяснена причина ограниченного распространения термометрии при ранней диагностике болезней растения: ложное неприятие исследователями факта, что оно представляет собой высокоорганизованную совокупность комплекса наземной и подземной части организмов. Сформулированы и обоснованы требования к устройствам для получения и обработки тепловых изображений. Разработана экспериментальная установка на основе тепловизионной камеры ТЕ-Q1, способной работать с Android-устройствами. Проведена проверка ее функционирования на образцах земляники садовой.</p></abstract><trans-abstract xml:lang="en"><p>   Traditional methods of early diagnosis of diseases, such as pure culture method, microscopic, mycological, polymerase chain reaction, enzyme immunoassay are invasive and require highly qualified personnel, expensive equipment and are not suitable for their effective use in practice. Since plant health is a fundamental indicator in assessing the phenotype of a crop plant, modern non-invasive methods for early diagnosis and determination of plant phenotype are considered.</p><p>   The purpose of the research is to select a rational method for early diagnosis of plant diseases and determination of their phenotype directly in the field of cultivated crops.</p><p>   Advantages and disadvantages of the vision method based on the analysis of the changes in color parameters of RGB images of plant leaves; fluorescence analysis, in which the efficiency of photosynthesis is estimated; multispectral and hyperspectral imaging methods carried out by determining the limited or continuous spectrum reflected from the surface of plant leaves; thermal imaging method in which the distribution of infrared radiation emitted by the plant is recorded. The analysis of the methods showed that the determination of thermal energy dissipation is a promising potential indicator of health and the presence of disease. In addition, when exposed to most environmental factors, the thermal properties of plant organs, such as leaf, stem, root, and reproductive organs, change. The reason for the limited use of thermometry in the early diagnosis of plant diseases is explained: false rejection by researchers of the fact that it is a highly organized complex of terrestrial and underground organisms. The requirements for devices for obtaining and processing thermal images are formulated and justified. An experimental setup based on the TE-Q1 thermal imaging camera, capable of working with Android devices, has been developed. Its operation has been tested on garden strawberry samples.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>метод</kwd><kwd>ранняя диагностика</kwd><kwd>болезни растений</kwd><kwd>биотические и абиотические стрессы</kwd><kwd>фенотип</kwd><kwd>тепловидение</kwd></kwd-group><kwd-group xml:lang="en"><kwd>method</kwd><kwd>early diagnosis</kwd><kwd>plant diseases</kwd><kwd>biotic and abiotic stresses</kwd><kwd>phenotyping</kwd><kwd>thermal imaging</kwd></kwd-group><funding-group><funding-statement xml:lang="ru">Работа поддержана бюджетным проектом СФНЦА РАН № 0533-2024-0004</funding-statement><funding-statement xml:lang="en">This work was supported by the budgetary project of SFSCA RAS No. 0533-2024-0004</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Dutta K., Talukdar D., Bora S.S. Segmentation of unhealthy leaves in cruciferous crops for early disease detection using vegetative indices and Otsu thresholding of aerial images // Measurement: Journal of the International Measurement Confederation. 2022. Vol. 189. P. 110478. DOI: 10.1016/j.measurement.2021.110478.</mixed-citation><mixed-citation xml:lang="en">Dutta K., Talukdar D., Bora S.S. Segmentation of unhealthy leaves in cruciferous crops for early disease detection using vegetative indices and Otsu thresholding of aerial images. Measurement: Journal of the International Measurement Confederation, 2022, vol. 189, p. 110478. DOI: 10.1016/j.measurement.2021.110478.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Tanner F., Tonn S., De Wit J., Van Den Ackerveken G., Berger B., Plett D. Sensor-based phenotyping of above-ground plant-pathogen interactions // Plant Methods. 2022. Vol. 18 (35). P. 2–18. DOI: 10.1186/s13007-022-00853-7.</mixed-citation><mixed-citation xml:lang="en">Tanner F., Tonn S., De Wit J., Van Den Ackerveken G., Berger B., Plett D. Sensor-based phenotyping of above-ground plant-pathogen interactions. Plant Methods, 2022, vol. 18 (35), pp. 2–18. DOI: 10.1186/s13007-022-00853-7.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Najafabadi M.Y., Heidari A., Rajcan I. All In One Pre-processing: A comprehensive preprocessing framework in plant field phenotyping // SoftwareX. 2023. Vol. 23. P. 101464. DOI: 10.1016/j.softx.2023.101464.</mixed-citation><mixed-citation xml:lang="en">Najafabadi M.Y., Heidari A., Rajcan I. All in One Pre-processing: A comprehensive preprocessing framework in plant field phenotyping. SoftwareX, 2023, vol. 23, p. 101464. DOI: 10.1016/j.softx.2023.101464.</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Алейников А.Ф., Минеев В.В., Чешкова А.Ф., Беляев А.А. Обнаружение пятнистостей земляники садовой методом импедансной спектроскопии // Сибирский вестник сельскохозяйственной науки. 2020. Т. 50. № 1. С. 81–91. DOI: 10.26898/0370-8799-2020-1-10.</mixed-citation><mixed-citation xml:lang="en">Alejnikov A.F., Mineev V.V., Cheshkova A.F., Belyaev A.A. Detecting spots of garden strawberry by impedance spectroscopy method. Sibirskij vestnik sel'skokhozyajstvennoj nauki = Siberian Herald of Agricultural Science, 2020, vol. 50, no. 1, pp. 81–91. (In Russian). DOI: 10.26898/0370-8799-2020-1-10.</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Sajitha P., Andrushia A.D., Anand N., Naser M.Z. A review on machine learning and deep learning image-based plant disease classification for industrial farming systems // Journal of Industrial Information Integration. 2024. Vol. 38. P. 100572. DOI: 10.1016/j.jii.2024.100572.</mixed-citation><mixed-citation xml:lang="en">Sajitha P., Andrushia A.D., Anand N., Naser M.Z. A review on machine learning and deep learning image-based plant disease classification for industrial farming systems. Journal of Industrial Information Integration, 2024, vol. 38, p. 100572. DOI: 10.1016/j.jii.2024.100572.</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Bartold M., Kluczek M. Estimating of chlorophyll fluorescence parameter Fv/Fm for plant stress detection at peatlands under Ramsar Convention with Sentinel-2 satellite imagery // Ecological Informatics. 2024. Vol. 81. P. 102603. DOI: 10.1016/j.ecoinf.2024.102603.</mixed-citation><mixed-citation xml:lang="en">Bartold M., Kluczek M. Estimating of chlorophyll fluorescence parameter Fv/Fm for plant stress detection at peatlands under Ramsar Convention with Sentinel-2 satellite imagery. Ecological Informatics, 2024, vol. 81, p. 102603. DOI: 10.1016/j.ecoinf.2024.102603.</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Sarić R., Nguyen V.D., Burge T., Berkowitz O., Trtílek M., Whelan J., Lewsey M.G., Čustović E. Applications of hyperspectral imaging in plant phenotyping // Trends in Plant Science. 2022. Vol. 27. P. 301–315. DOI: 10.1016/j.tplants.2021.12.003.</mixed-citation><mixed-citation xml:lang="en">Sarić R., Nguyen V.D., Burge T., Berkowitz O., Trtílek M., Whelan J., Lewsey M.G., Čustović E. Applications of hyperspectral imaging in plant phenotyping. Trends in Plant Science, 2022, vol. 27, pp. 301–315. DOI: 10.1016/j.tplants.2021.12.003.</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Ye D.P., Wu L.B., Li X.B., Atoba T.O., Wu W.H., Weng H.Y. A synthetic review of various dimensions of non-destructive plant stress phenotyping // Plants-Basel. 2023. Vol. 12 (8). P. 1698. DOI: 10.3390/plants12081698.</mixed-citation><mixed-citation xml:lang="en">Ye D.P., Wu L.B., Li X.B., Atoba T.O., Wu W.H., Weng H.Y. A synthetic review of various dimensions of non-destructive plant stress phenotyping, Plants-Basel, 2023, vol. 12 (8), p. 1698. DOI: 10.3390/plants12081698.</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Hou F.J., Zhang Y., Zhou Y., Zhang M., Lv B., Wu J.Q. Review on infrared imaging technology // Sustainability. 2022. Vol. 14 (18). P. 26. DOI: 10.3390/su141811161.</mixed-citation><mixed-citation xml:lang="en">Hou F.J., Zhang Y., Zhou Y., Zhang M., Lv B., Wu J.Q. Review on infrared imaging technology. Sustainability, 2022, vol. 14 (18), p. 26. DOI: 10.3390/su141811161.</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Ding W., Abdel-Basset M., Alrashdi I., Hawas H. Next generation of computer vision for plant disease monitoring in precision agriculture: A contemporary survey, taxonomy, experiments, and future direction // Information Sciences. 2024. Vol. 665. P. 120338. DOI: 10.1016/j.ins.2024.120338.</mixed-citation><mixed-citation xml:lang="en">Ding W., Abdel-Basset M., Alrashdi I., Hawas H. Next generation of computer vision for plant disease monitoring in precision agriculture: A contemporary survey, taxonomy, experiments, and future direction. Information Sciences, 2024, vol. 665, p. 120338. DOI: 10.1016/j.ins.2024.120338.</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Zheng C., Abd-Elrahman A., Whitaker V. Remote sensing and machine learning in crop phenotyping and management, with an emphasis on applications in strawberry farming // Remote Sensing. 2021. Vol. 13 (3). P. 531. DOI: 10.3390/rs13030531.</mixed-citation><mixed-citation xml:lang="en">Zheng C., Abd-Elrahman A., Whitaker V. Remote sensing and machine learning in crop phenotyping and management, with an emphasis on applications in strawberry farming. Remote Sensing, 2021, vol. 13 (3), p. 531. DOI: 10.3390/rs13030531.</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Sanaeifar A., Yang C., Guardia M.D., Zhang W.K., Li X.L., He Y. Proximal hyperspectral sensing of abiotic stresses in plants // Science of the Total Environment. 2023. Vol. 861. P. 160652. DOI: 10.1016/j.scitotenv.2022.160652.</mixed-citation><mixed-citation xml:lang="en">Sanaeifar A., Yang C., Guardia M.D., Zhang W.K., Li X.L., He Y. Proximal hyperspectral sensing of abiotic stresses in plants. Science of the Total Environment, 2023, vol. 861, p. 160652. DOI: 10.1016/j.scitotenv.2022.160652.</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Elmasry G., Elgamal R., Mandour N., Gou P., Al-Rejaie S., Belin E., Rousseau D. Emerging thermal imaging techniques for seed quality evaluation: principles and applications // Food Research International. 2020. Vol. 131. P. 109025. DOI: 10.1016/j.foodres.2020.109025.</mixed-citation><mixed-citation xml:lang="en">Elmasry G., Elgamal R., Mandour N., Gou P., Al-Rejaie S., Belin E., Rousseau D. Emerging thermal imaging techniques for seed quality evaluation: principles and applications. Food Research International, 2020, vol. 131, p. 109025. DOI: 10.1016/j.foodres.2020.109025.</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Liu L.Y., Wang Z.S., Li J., Zhang X., Wang R.H. A non-invasive analysis of seed vigor by infrared thermography // Plants-Basel. 2020. Vol. 9 (6). P. 768. DOI: 10.3390/plants9060768.</mixed-citation><mixed-citation xml:lang="en">Liu L.Y., Wang Z.S., Li J., Zhang X., Wang R.H. A non-invasive analysis of seed vigor by infrared thermography. Plants-Basel, 2020, vol. 9 (6), p. 768. DOI: 10.3390/plants9060768.</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Francesconi S., Harfouche A., Maesano M., Balestra G.M. UAV-based thermal, RGB imaging and gene expression analysis allowed detection of Fusarium head blight and gave new insights into the physiological responses to the disease in durum wheat // Frontiers in Plant Science. 2021. Vol. 12. P. 19. DOI: 1910.3389/fpls.2021.628575.</mixed-citation><mixed-citation xml:lang="en">Francesconi S., Harfouche A., Maesano M., Balestra G.M. UAV-based thermal, RGB imaging and gene expression analysis allowed detection of Fusarium head blight and gave new insights into the physiological responses to the disease in durum wheat. Frontiers in Plant Science, 2021, vol. 12, p. 19. DOI: 1910.3389/fpls.2021.628575.</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Singh A., Jones S., Ganapathysubramanian B., Sarkar S., Mueller D., Sandhu K., Nagasubramanian K. Challenges and opportunities in machine-augmented plant stress phenotyping // Trends Plant Science. 2021. Vol. 26. P. 53–69. DOI: 10.1016/j.tplants.2020.07.010.</mixed-citation><mixed-citation xml:lang="en">Singh A., Jones S., Ganapathysubramanian B., Sarkar S., Mueller D., Sandhu K., Nagasubramanian K. Challenges and opportunities in machine-augmented plant stress phenotyping. Trends Plant Science, 2021, vol. 26, pp. 53–69. DOI: 10.1016/j.tplants.2020.07.010.</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Bhakta I., Phadikar S., Majumder K., Mukherjee H., Sau A. A novel plant disease prediction model based on thermal images using modified deep convolutional neural network // Precision Agriculture. 2023. Vol. 24. P. 1–17. DOI: 10.1007/s11119-022-09927-x.</mixed-citation><mixed-citation xml:lang="en">Bhakta I., Phadikar S., Majumder K., Mukherjee H., Sau A. A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precision Agriculture, 2023, vol. 24, pp. 1–17. DOI: 10.1007/s11119-022-09927-x.</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Keszthelyi S., Fajtai D., Ponya Z., Somfalvi-Toth K., Donko T. A non-invasive approach in the assessment of stress phenomena and impairment values in pea seeds caused by pea weevil // Plants-Basel. 2021. Vol. 10 (7). Р. 1470. DOI: 10.3390/plants10071470.</mixed-citation><mixed-citation xml:lang="en">Keszthelyi S., Fajtai D., Ponya Z., Somfalvi-Toth K., Donko T. A non-invasive approach in the assessment of stress phenomena and impairment values in pea seeds caused by pea weevil. Plants-Basel, 2021, vol. 10 (7), p. 1470. DOI: 10.3390/plants10071470.</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Bi Y.L., Ma S.P., Gao Y.K.,. Shang J.X, Zhang Y.X., Xie L.L., Guo Y., Christie P. Thermal infrared evaluation of the influence of arbuscular mycorrhizal fungus and dark septate endophytic fungus on maize growth and physiology // Agronomy-Basel. 2022. Vol. 12 (4). Р. 912. DOI: 10.3390/agronomy12040912.</mixed-citation><mixed-citation xml:lang="en">Bi Y.L., Ma S.P., Gao Y.K.,. Shang J.X, Zhang Y.X., Xie L.L., Guo Y., Christie P. Thermal infrared evaluation of the influence of arbuscular mycorrhizal fungus and dark septate endophytic fungus on maize growth and physiology. Agronomy-Basel, 2022, vol. 12 (4), p. 912. DOI: 10.3390/agronomy12040912.</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">El Hoseny M.M., Dahi H.F., El Shafei A.M., Yones M.S. Spectroradiometer and thermal imaging as tools from remote sensing used for early detection of spiny bollworm, Earias insulana (Boisd.) infestation // International Journal of Tropical Insect Science. 2023. Vol. 43. P. 245–256. DOI: 10.1007/s42690-022-00917-0.</mixed-citation><mixed-citation xml:lang="en">El Hoseny M.M., Dahi H.F., El Shafei A.M., Yones M.S. Spectroradiometer and thermal imaging as tools from remote sensing used for early detection of spiny bollworm, Earias insulana (Boisd.) infestation. International Journal of Tropical Insect Science, 2023, vol. 43, pp. 245–256. DOI: 10.1007/s42690-022-00917-0.</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Lipinska E., Pobiega K., Piwowarek K., Blazejak S. Research on the use of thermal imaging as a method for detecting fungal growth in apples // Horticulturae. 2022. Vol. 8 (10). P. 972. DOI: 10.3390/horticulturae8100972.</mixed-citation><mixed-citation xml:lang="en">Lipinska E., Pobiega K., Piwowarek K., Blazejak S. Research on the use of thermal imaging as a method for detecting fungal growth in apples. Horticulturae, 2022, vol. 8 (10), p. 972, DOI: 10.3390/horticulturae8100972.</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Hatton N., Sharda A., Schapaugh W., Van Der Merwe D. Remote thermal infrared imaging for rapid screening of sudden death syndrome in soybean // Computers and Electronics in Agriculture. 2020. Vol. 178. P. 105738. DOI: 10.1016/j.compag.2020.105738.</mixed-citation><mixed-citation xml:lang="en">Hatton N., Sharda A., Schapaugh W., Van Der Merwe D. Remote thermal infrared imaging for rapid screening of sudden death syndrome in soybean. Computers and Electronics in Agriculture, 2020, vol. 178, p. 105738. DOI: 10.1016/j.compag.2020.105738.</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Ashfaq W., Brodie G., Fuentes S., Gupta D. Infrared thermal imaging and morpho-physiological indices used for wheat genotypes screening under drought and heat stress // Plants-Basel. 2022. Vol. 11 (23). P. 3269. DOI: 10.3390/plants11233269.</mixed-citation><mixed-citation xml:lang="en">Ashfaq W., Brodie G., Fuentes S., Gupta D. Infrared thermal imaging and morpho-physiological indices used for wheat genotypes screening under drought and heat stress. Plants-Basel, 2022, vol. 11 (23), p. 3269. DOI: 10.3390/plants11233269.</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Chandel N.S., Rajwade Y.A., Dubey K., Chandel A.K., Subeesh A., Tiwari M.K. Water stress identification of winter wheat crop with state-of-the-art AI techniques and high-resolution thermal-RGB imagery // Plants-Basel. 2022. Vol. 11. P. 3344. DOI: 10.3390/plants11233344.</mixed-citation><mixed-citation xml:lang="en">Chandel N.S., Rajwade Y.A., Dubey K., Chandel A.K., Subeesh A., Tiwari M.K. Water stress identification of winter wheat crop with state-of-the-art AI techniques and high-resolution thermal-RGB imagery. Plants-Basel, 2022, vol. 11, p. 3344. DOI: 10.3390/plants11233344.</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">Banerjee K., Krishnan P. Normalized Sunlit Shaded Index (NSSI) for characterizing the moisture stress in wheat crop using classified thermal and visible images // Ecological Indicators. 2020. Vol. 110. P. 105947. DOI: 10.1016/j.ecolind.2019.105947.</mixed-citation><mixed-citation xml:lang="en">Banerjee K., Krishnan P. Normalized Sunlit Shaded Index (NSSI) for characterizing the moisture stress in wheat crop using classified thermal and visible images. Ecological Indicators, 2020, vol. 110, p. 105947. DOI: 10.1016/j.ecolind.2019.105947.</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">Das S., Christopher J., Apan A., Choudhury M.R., Chapman S., Menzies N.W., Dang Y.P. Evaluation of water status of wheat genotypes to aid prediction of yield on sodic soils using UAV-thermal imaging and machine learning // Agricultural and Forest Meteorology. 2021. Vol. 307. P. 108477. DOI: 10.1016/j.agrformet.2021.108477.</mixed-citation><mixed-citation xml:lang="en">Das S., Christopher J., Apan A., Choudhury M.R., Chapman S., Menzies N.W., Dang Y.P. Evaluation of water status of wheat genotypes to aid prediction of yield on sodic soils using UAV-thermal imaging and machine learning. Agricultural and Forest Meteorology, 2021, vol. 307, p. 108477. DOI: 10.1016/j.agrformet.2021.108477.</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Mulero G., Jiang D., Bonfil D.J., Helman D. Use of thermal imaging and the photochemical reflectance index (PRI) to detect wheat response to elevated CO&lt;sub&gt;2&lt;/sub&gt; and drought // Plant Cell and Environment. 2023. Vol. 46. Р. 76–92. DOI: 10.1111/pce.14472.</mixed-citation><mixed-citation xml:lang="en">Mulero G., Jiang D., Bonfil D.J., Helman D. Use of thermal imaging and the photochemical reflectance index (PRI) to detect wheat response to elevated CO&lt;sub&gt;2&lt;/sub&gt; and drought. Plant Cell and Environment, 2023, vol. 46, pp. 76–92. DOI: 10.1111/pce.14472.</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Menegassi L.C., Benassi V.C., Trevisan L.R., Rossi F., Gomes T.M. Thermal imaging for stress assessment in rice cultivation drip-irrigated with saline water // Engenharia Agricola. 2022. Vol. 42 (5). Р. 20220043. DOI: 10.1590/1809-4430-Eng.Agric.v42n5e20220043/2022.</mixed-citation><mixed-citation xml:lang="en">Menegassi L.C., Benassi V.C., Trevisan L.R., Rossi F., Gomes T.M. Thermal imaging for stress assessment in rice cultivation drip-irrigated with saline water. Engenharia Agricola, 2022, vol. 42 (5), p. 20220043. DOI: 10.1590/1809-4430-Eng.Agric.v42n5e20220043/2022.</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Caruso G., Palai G., Tozzini L., Gucci R. Using visible and thermal images by an unmanned aerial vehicle to monitor the plant water status, canopy growth and yield of olive trees (cvs. Frantoio and Leccino) under different irrigation regimes // Agronomy-Basel. 2022. Vol. 12 (8) P. 1904. DOI: 10.3390/agronomy12081904.</mixed-citation><mixed-citation xml:lang="en">Caruso G., Palai G., Tozzini L., Gucci R. Using visible and thermal images by an unmanned aerial vehicle to monitor the plant water status, canopy growth and yield of olive trees (cvs. Frantoio and Leccino) under different irrigation regimes. Agronomy-Basel, 2022, vol. 12 (8), p. 1904. DOI: 10.3390/agronomy12081904.</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Pradawet C., Khongdee N., Pansak W., Spreer W., Hilger T., Cadisch G. Thermal imaging for assessment of maize water stress and yield prediction under drought conditions // Journal of Agronomy and Crop Science. 2023. Vol. 209. Р. 56–70. DOI: 10.1111/jac.12582.</mixed-citation><mixed-citation xml:lang="en">Pradawet C., Khongdee N., Pansak W., Spreer W., Hilger T., Cadisch G. Thermal imaging for assessment of maize water stress and yield prediction under drought conditions. Journal of Agronomy and Crop Science, 2023, vol. 209, pp. 56–70. DOI: 10.1111/jac.12582.</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Vieira G.H.S., Ferrarezi R.S. Use of thermal imaging to assess water status in citrus plants in greenhouses // Horticulturae. 2021. Vol. 7 (8). Р. 249. DOI: 10.3390/horticulturae7080249.</mixed-citation><mixed-citation xml:lang="en">Vieira G.H.S., Ferrarezi R.S. Use of thermal imaging to assess water status in citrus plants in greenhouses. Horticulturae, 2021, vol. 7 (8), p. 249. DOI: 10.3390/horticulturae7080249.</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">Wen T., Li J.H., Wang Q., Gao Y.Y., Hao G.F., Song B.A. Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses // Science of The Total Environment. 2023. Vol. 899. Р. 165626 DOI: 10.1016/j.scitotenv.2023.165626.</mixed-citation><mixed-citation xml:lang="en">Wen T., Li J.H., Wang Q., Gao Y.Y., Hao G.F., Song B.A. Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses. Science of The Total Environment, 2023, vol. 899, p. 165626. DOI: 10.1016/j.scitotenv.2023.165626.</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
