kaz44Вестник Университета Шакарима. Серия технические наукиBulletin of Shakarim University. Technical Sciences2788-79953006-0524«Шәкәрім университеті» КеАҚ10.53360/2788-7995-2025-4(20)-18kaz44-2157Research ArticleАВТОМАТИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ (ОРИГИНАЛЬНАЯ СТАТЬЯ)AUTOMATION AND INFORMATION TECHNOLOGY (ORIGINAL ARTICLE)АВТОМАТИЗАЦИЯ СБОРА СЕМЯН САКСАУЛА С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТАAUTOMATING THE COLLECTION OF SAXAUL SEEDS USING ARTIFICIAL INTELLIGENCEhttps://orcid.org/0009-0008-4551-0916БудановД. С.BudanovD. S.

Дархан Серикболулы Буданов – докторант по специальности «Электроэнергетические системы», Факультет автоматизация и управление

Республика Казахстан, Алматы

 

Darkhan Serikboluly Budanov – PhD student in the specialty «Electric Power Systems», Faculty of Automation and Control

Republic of Kazakhstan, Almaty 

budanov.darhan@bk.ruhttps://orcid.org/0000-0002-0305-2776ТойгожиноваА. Ж.ToygozhinovaA. Zh.

Айнур Жумахановна Тойгожинова – Директор ИЭиЦТ, Ассистент профессор, PhD, Факультет автоматизация и управление 

Республика Казахстан, Алматы

Ainur Zhumakhanovna Toigozhinova – Director of the Institute of Economics and Technology, Assistant Professor, PhD, Faculty of Automation and Control

Republic of Kazakhstan, Almaty

a.toigozhinova@alt.edu.kzАЛТ Университет им. М.ТынышпаеваКазахстанMukhamedzhan Tynyshbayev ALT UniversityKazakhstan20252501202614(20)145156Copyright © Буданов Д.С., Тойгожинова А.Ж., 20262026Буданов Д.С., Тойгожинова А.Ж.Budanov D.S., Toygozhinova A.Z.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://tech.vestnik.shakarim.kz/jour/article/view/2157

Казахстан борется с проблемой опустынивания и деградации земель, особенно в южных регионах и Приаралье, где ежегодное распространение около 150 миллионов тонн соли вызывает нарушение экологического равновесия. Одним из основных растений, играющих важную роль в закреплении почвенного слоя, является саксаул, площадь которого составляет более 6 млн гектаров. Однако традиционные методы сбора его семян недостаточно эффективны, что замедляет процесс восстановления лесов.Целью данного исследования является анализ научных работ, направленных на разработку автоматизированного механизма уборки семян саксаула с использованием современных технологий, таких как искусственный интеллект (ИИ), удаленный мониторинг и робототехника. При анализе учитывался потенциал спутниковой информации (KazEOSat, Sentinel), методов глубокого обучения (CNN, Random Forest), беспилотных летательных аппаратов (БПЛА), систем Интернета вещей (IoT) и инновационных решений.Результаты исследования показывают, что внедрение современных технологий позволяет повысить производительность лесовосстановительных мероприятий и повысить жизнеспособность растений. Кроме того, эти подходы могут внести значительный вклад в устойчивое озеленение региона Аральского моря. Однако необходимы дополнительные исследования для обеспечения их адаптации к природно-климатическим условиям Казахстана.

Kazakhstan is struggling with the problem of desertification and land degradation, especially in the southern regions and the Aral Sea region, where the annual release of about 150 million tons of salt is causing an ecological imbalance. One of the main plants that plays an important role in soil stabilization is saxaul, which covers an area of more than 6 million hectares. However, traditional methods of collecting its seeds are not effective enough, which slows down the process of restoring forests.The purpose of this study is to analyze scientific works aimed at developing an automated mechanism for collecting saxaul seeds using modern technologies, such as artificial intelligence (AI), remote sensing and robotics. The analysis considered the potential of satellite information (KazEOSat, Sentinel), deep learning methods (CNN, Random Forest), unmanned aerial vehicles (UAV), Internet of Things (IoT) systems and innovative solutions.The results of the study show that the introduction of modern technologies allows to increase the productivity of forest restoration measures and enhance the viability of plants. Moreover, these approaches can make a significant contribution to sustainable greening of the Aral Sea region. However, further research is needed to ensure their adaptation to the natural and climatic conditions of Kazakhstan.

Искусственный интеллектсельское хозяйствосемена саксаулаавтоматизациясбор семянArtificial intelligenceagricultureseed savingautomationseed collectionReferencesInformburo.kz. Саксаул в Казахстане: спасение пустынь. – 2019. – URL: https://informburo.kz/stati/saksaul-v-kazahstane-spasenie-pustyn.Informburo.kz. Saksaul v Kazakhstane: spasenie pustyn'. – 2019. – URL: https://informburo.kz/stati/saksaul-v-kazahstane-spasenie-pustyn. (In Russian).Kitzes J. Shrinkage of the human ecological footprint / J. Kitzes // Proceedings of the National Academy of Sciences. – 2008. – Vol. 105, № 36. – P. 13151-13156. https://doi.org/10.1073/pnas.0711440105.Kitzes J. Shrinkage of the human ecological footprint / J. Kitzes // Proceedings of the National Academy of Sciences. – 2008. – Vol. 105, № 36. – P. 13151-13156. https://doi.org/10.1073/pnas.0711440105. (In English).FAO. Global agriculture towards 2050. – Rome: Food and Agriculture Organization of the United Nations, 2009. http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf.FAO. Global agriculture towards 2050. – Rome: Food and Agriculture Organization of the United Nations, 2009. http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf. (In English).Комитет лесного хозяйства РК. Отчёт о незаконной вырубке саксаула. – 2019. https://www.gov.kz/memleket/entities/les?lang=ru (официальный сайт, точный отчёт требует запроса).Komitet lesnogo khozyaistva RK. Otchet o nezakonnoi vyrubke saksaula. – 2019. https://www.gov.kz/memleket/entities/les?lang=ru (ofitsial'nyi sait, tochnyi otchet trebuet zaprosa). (In Russian).Informburo.kz. Саксаул в Казахстане: спасение пустынь. – 2019. https://informburo.kz/stati/saksaul-v-kazahstane-spasenie-pustyn.Informburo.kz. Saksaul v Kazakhstane: spasenie pustyn'. – 2019. https://informburo.kz/stati/saksaul-v-kazahstane-spasenie-pustyn. (In Russian).Zhang Y. Smart irrigation systems using IoT and machine learning / Y. Zhang // Agricultural Water Management. – 2021. – Vol. 245. – P. 106543. https://doi.org/10.1016/j.agwat.2020.106543. https://www.sciencedirect.com/science/article/abs/pii/S0378377420305435Zhang Y. Smart irrigation systems using IoT and machine learning / Y. Zhang // Agricultural Water Management. – 2021. – Vol. 245. – P. 106543. https://doi.org/10.1016/j.agwat.2020.106543. https://www.sciencedirect.com/science/article/abs/pii/S0378377420305435. (In English).Kamilaris A. Deep learning in agriculture: A survey / A. Kamilaris, F.X. Prenafeta-Boldú // Computers and Electronics in Agriculture. – 2018. – Vol. 147. – P. 70-90. https://doi.org/10.1016/j.compag.2018.02.016. https://www.sciencedirect.com/science/article/abs/pii/S0168169917313736.Kamilaris A. Deep learning in agriculture: A survey / A. Kamilaris, F.X. Prenafeta-Boldú // Computers and Electronics in Agriculture. – 2018. – Vol. 147. – P. 70-90. https://doi.org/10.1016/j.compag.2018.02.016. (In English). https://www.sciencedirect.com/science/article/abs/pii/S0168169917313736.Gebbers R. Precision agriculture and food security / R. Gebbers, V.I. Adamchuk // Science. – 2010. – Vol. 327, № 5967. – P. 828-831. https://doi.org/10.1126/science.1183899. https://www.science.org/doi/10.1126/science.1183899.Gebbers R. Precision agriculture and food security / R. Gebbers, V.I. Adamchuk // Science. – 2010. – Vol. 327, № 5967. – P. 828-831. https://doi.org/10.1126/science.1183899. https://www.science.org/doi/10.1126/science.1183899. (In English).Wang X. Desertification control and restoration of degraded lands / X. Wang, Y. Zhang, Z. Chen // Journal of Arid Environments. – 2019. – Vol. 163. – P. 1-8. https://doi.org/10.1016/j.jaridenv.2018.12.001. https://www.sciencedirect.com/science/article/abs/pii/S0140196318306839.Wang X. Desertification control and restoration of degraded lands / X. Wang, Y. Zhang, Z. Chen // Journal of Arid Environments. – 2019. – Vol. 163. – P. 1-8. https://doi.org/10.1016/j.jaridenv.2018.12.001. https://www.sciencedirect.com/science/article/abs/pii/S0140196318306839. (In English).Fannon E. Drone-based reforestation and its potential for carbon sequestration / E. Fannon // Environmental Research Letters. – 2020. – Vol. 15, № 12. – P. 124025. https://doi.org/10.1088/1748-9326/abc5f1. https://iopscience.iop.org/article/10.1088/1748-9326/abc5f1.Fannon E. Drone-based reforestation and its potential for carbon sequestration / E. Fannon // Environmental Research Letters. – 2020. – Vol. 15, № 12. – P. 124025. https://doi.org/10.1088/1748-9326/abc5f1. https://iopscience.iop.org/article/10.1088/1748-9326/abc5f1. (In English).Le Cun Y. Convolutional networks for images, speech, and time series / Y. Le Cun, Y. Bengio // The Handbook of Brain Theory and Neural Networks. – 1995. – P. 255-258. http://yann.lecun.com/exdb/publis/pdf/lecun-bengio-95a.pdf.Le Cun Y. Convolutional networks for images, speech, and time series / Y. Le Cun, Y. Bengio // The Handbook of Brain Theory and Neural Networks. – 1995. – P. 255-258. http://yann.lecun.com/exdb/publis/pdf/lecun-bengio-95a.pdf. (In English).Mwangi E. Remote sensing for land degradation assessment and monitoring in Kenya / E. Mwangi // Remote Sensing Applications: Society and Environment. – 2018. – Vol. 10. – P. 1-10. https://doi.org/10.1016/j.rsase.2018.01.003. https://www.sciencedirect.com/science/article/abs/pii/S2352938517300991.Mwangi E. Remote sensing for land degradation assessment and monitoring in Kenya / E. Mwangi // Remote Sensing Applications: Society and Environment. – 2018. – Vol. 10. – P. 1-10. https://doi.org/10.1016/j.rsase.2018.01.003. https://www.sciencedirect.com/science/article/abs/pii/S2352938517300991. (In English).Primeminister.kz. Цифровой Казахстан: новые возможности для развития. – 2023. https://primeminister.kz/ru/news/tsifrovoy-kazahstan-novye-vozmozhnosti-dlya-razvitiya.Primeminister.kz. Tsifrovoi Kazakhstan: novye vozmozhnosti dlya razvitiya. – 2023. https://primeminister.kz/ru/news/tsifrovoy-kazahstan-novye-vozmozhnosti-dlya-razvitiya. (In English).Mwangi E. Remote sensing for land degradation assessment and monitoring in Kenya / E. Mwangi // Remote Sensing Applications: Society and Environment. – 2018. – Vol. 10. – P. 1-10. https://doi.org/10.1016/j.rsase.2018.01.003. https://www.sciencedirect.com/science/article/abs/pii/S2352938517300991.Mwangi E. Remote sensing for land degradation assessment and monitoring in Kenya / E. Mwangi // Remote Sensing Applications: Society and Environment. – 2018. – Vol. 10. – P. 1-10. https://doi.org/10.1016/j.rsase.2018.01.003. https://www.sciencedirect.com/science/article/abs/pii/S2352938517300991. (In English).Tyagi A.C. Towards a second green revolution / A.C. Tyagi // Irrigation and Drainage. – 2016. – Vol. 65, № 4. – P. 388-389. https://doi.org/10.1002/ird.2056. https://onlinelibrary.wiley.com/doi/10.1002/ird.2056.Tyagi A.C. Towards a second green revolution / A.C. Tyagi // Irrigation and Drainage. – 2016. – Vol. 65, № 4. – P. 388-389. https://doi.org/10.1002/ird.2056. https://onlinelibrary.wiley.com/doi/10.1002/ird.2056. (In English).Teke A. A survey of machine learning techniques for remote sensing image classification / A. Teke // Journal of Applied Remote Sensing. – 2013. – Vol. 7, № 1. – P. 073595. https://doi.org/10.1117/1.JRS.7.073595. https://www.spiedigitallibrary.org/journals/journal-ofapplied-remote-sensing/volume-7/issue-1/073595/Survey-of-machine-learning-techniques-forremote-sensing-image-classification/10.1117/1.JRS.7.073595.full.Teke A. A survey of machine learning techniques for remote sensing image classification / A. Teke // Journal of Applied Remote Sensing. – 2013. – Vol. 7, № 1. – P. 073595. https://doi.org/10.1117/1.JRS.7.073595. https://www.spiedigitallibrary.org/journals/journal-ofapplied-remote-sensing/volume-7/issue-1/073595/Survey-of-machine-learning-techniques-forremote-sensing-image-classification/10.1117/1.JRS.7.073595.full. (In English).Старостин Я. Интеллектуальная система управления сельскохозяйственными роботами / Я. Старостин, С.А. Давыдова, А.В. Йешин // CyberLeninka. – 2020. https://cyberleninka.ru/article/n/intellektualnaya-sistema-upravleniya-selskohozyaystvennymirobotami.Starostin YA. Intellektual'naya sistema upravleniya sel'skokhozyaistvennymi robotami / YA. Starostin, S.A. Davydova, A.V. Ieshin // CyberLeninka. – 2020. https://cyberleninka.ru/article/n/intellektualnaya-sistema-upravleniya-selskohozyaystvennymirobotami. (In Russian).Kamilaris A. Deep learning in agriculture: A survey / A. Kamilaris, F.X. Prenafeta-Boldú // Computers and Electronics in Agriculture. – 2018. – Vol. 147. – P. 70-90. https://doi.org/10.1016/j.compag.2018.02.016. – URL: https://www.sciencedirect.com/science/article/abs/pii/S0168169917313736.Kamilaris A. Deep learning in agriculture: A survey / A. Kamilaris, F.X. Prenafeta-Boldú // Computers and Electronics in Agriculture. – 2018. – Vol. 147. – P. 70-90. https://doi.org/10.1016/j.compag.2018.02.016. – URL: https://www.sciencedirect.com/science/article/abs/pii/S0168169917313736. (In English).Bastiaanssen W.G. Remote sensing for irrigated agriculture: Examples from research and possible applications / W.G. Bastiaanssen // Agricultural Water Management. – 2000. – Vol. 46, № 2. – P. 137-155. https://doi.org/10.1016/S0378-3774(00)00080-9. https://www.sciencedirect.com/science/article/abs/pii/S0378377400000809.Bastiaanssen W.G. Remote sensing for irrigated agriculture: Examples from research and possible applications / W.G. Bastiaanssen // Agricultural Water Management. – 2000. – Vol. 46, № 2. – P. 137-155. https://doi.org/10.1016/S0378-3774(00)00080-9. https://www.sciencedirect.com/science/article/abs/pii/S0378377400000809. (In English).Liaghat S. A review: The role of remote sensing in precision agriculture / S. Liaghat, S.K. Balasundram // American Journal of Agricultural and Biological Sciences. – 2010. – Vol. 5, № 1. – P. 50-55. https://doi.org/10.3844/ajabssp.2010.50.55. https://thescipub.com/abstract/10.3844/ajabssp.2010.50.55.Liaghat S. A review: The role of remote sensing in precision agriculture / S. Liaghat, S.K. Balasundram // American Journal of Agricultural and Biological Sciences. – 2010. – Vol. 5, № 1. – P. 50-55. https://doi.org/10.3844/ajabssp.2010.50.55. https://thescipub.com/abstract/10.3844/ajabssp.2010.50.55. (In English).

The authors declare that there are no conflicts of interest present.