مرور فناوری‌های نوین در مدیریت دقیق و پایدار علفهای هرز: نانوذرات، هوش مصنوعی و رباتیک
الموضوعات :
1 - دکترای علوم علفهای هرز، گروه تولید و ژنتیک گیاهی، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران
2 - کارشناس بخش تحقیقات گیاهپزشکی، مرکز تحقیقات و آموزش کشاورزي و منابع طبیعی استان اردبیل (مغان)، سازمان تحقیقات آموزش و ترویج کشاورزي، مغان، ایران
الکلمات المفتاحية: اکولوژیک, زیست محیطی, کشاورزی مدرن, مقاومت به علف کش.,
ملخص المقالة :
علفهای هرز به عنوان تهدیدی جدی برای امنیت غذایی جهانی، سالانه بیش از ۱۰۰ میلیارد دلار خسارت اقتصادی وارد می‌کنند. مدیریت علفهای هرز به عنوان یکی از چالش‌های اساسی کشاورزی مدرن، با مشکلاتی مانند مقاومت به علف‌کش‌ها، آلودگی محیط زیست و هزینه‌های اقتصادی روبرو است. فناوری‌های نوین از جمله نانوذرات، هوش مصنوعی و رباتیک راهکارهای امیدبخشی برای مقابله با این چالش‌ها ارائه می‌دهند. نانوذرات با قابلیت رهایش هدفمند و کنترل‌شده علف‌کش‌ها، مصرف سموم را تا ۷۵ درصد کاهش داده و کارایی آنها را به طور چشمگیری افزایش می‌دهند. هوش مصنوعی با پردازش تصاویر ماهواره‌ای و زمینی، الگوهای رشد علفهای هرز را با دقت ۹۵ درصد شناسایی و پیش‌بینی می‌کند. ربات‌های کشاورزی نیز با بهره‌گیری از سیستم‌های مکانیزه و هوشمند، امکان کنترل فیزیکی و شیمیایی موضعی علفهای هرز را فراهم می‌سازند و قادرند با سرعت بالا (۲۰ تصمیم در ثانیه) عملیات وجین را به صورت خودکار انجام دهند. ترکیب این سه فناوری می‌تواند تحولی اساسی در مدیریت علفهای هرز ایجاد کند و سیستم‌های یکپارچه‌ای را توسعه دهد که هم مصرف نهاده‌ها را بهینه می‌کنند و هم بازدهی مزارع را افزایش می‌دهند. با توجه به پیش‌بینی افزایش ۶۰ درصدی تقاضای جهانی غذا تا سال ۲۰۵۰، به کارگیری این فناوری‌های نوین نه‌تنها می‌تواند امنیت غذایی را تضمین کند، بلکه کشاورزی پایدار و سازگار با محیط زیست را نیز محقق خواهد ساخت. این تحولات نشان‌دهنده گذار به سمت کشاورزی دقیق‌تر و هوشمندانه‌تر است که در آن فناوری‌های پیشرفته نقش محوری در حل چالش‌های جهانی کشاورزی ایفا می‌کنند.
المصادر:
Adhikari, S. P., Zhou, J. and Li, Y. 2021. LiDAR-based weed density estimation in apple orchards. Biosystems Engineering. 212: 152-163. https://doi.org/10.1016/j.biosystemseng.2021.10.012
Bauer, A., Schmidt, J. and Ulber, B. 2023. PhotonHarvester: A laser-based weeding robot for greenhouse applications. Biosystems Engineering. 225: 1-12. https://doi.org/10.1016/j.biosystemseng.2023.01.001
Bawden, O., Kulk, J., Russell, R., McCool, C., Dayoub, F., Lehnert, C., and Perez, T. 2017. Robot for weed species plant-specific management. Journal of Field Robotics. 34(6): 1179-1199. https://doi.org/10.1002/rob.21727
Chebrolu, N., Lottes, P., Schaefer, A., Winterhalter, W., Burgard, W. and Stachniss, C. 2022. Agricultural robot dataset for plant classification, localization, and mapping on sugar beet fields. The International Journal of Robotics Research. 41(3): 267-286. https://doi.org/10.1177/02783649211050634
Chen, L., Zhang, H. and Liu, M. 2023. Edge AI-based weed detection system for paddy fields. IEEE Transactions on AgriFood Electronics, 1(1), 45-56. https://doi.org/10.1109/TAFE.2023.00007
Chen, L., Zhang, H., Zhou, J. and Wu, D. 2022. Multimodal sensor fusion for agricultural robotics. IEEE Transactions on AgriFood Electronics. 2(3): 145-156. https://doi.org/10.1109/TAFE.2022.00012
Chen, Z., Liu, Y., Wang, J., and Zhang, K. 2024. AI-driven decision-making in agricultural networks. Artificial Intelligence. 325: 104-118
Chen, Z., Liu, Y., Wang, J. and Zhang, K. 2024. AI-driven decision making in nanorobotic farming systems. Artificial Intelligence in Agriculture. 8: 45-62. DOI: 10.1016/j.aiia.2024.03.002
Chhipa, H. 2017. Nanofertilizers and nanopesticides for agriculture. Environmental Chemistry Letters. 15(1): 15-22. https://doi.org/10.1007/s10311-016-0600-4
Choudhary, R. C., Kumaraswamy, R. V., Kumari, S., Pal, A., Raliya, R., Biswas, P. and Saharan, V. 2020. Zinc-encapsulated chitosan nanoparticles to promote maize crop yield. Scientific Reports. 10(1): 1-12. https://doi.org/10.1038/s41598-020-62591-x
Das, S., Ray, A., Pandey, P., Mohanty, S. and Das, B. 2022. Copper oxide nanoparticles as eco-friendly nano-herbicide for weed management in rice fields: A field study in Punjab, India. Environmental Science and Pollution Research. 29(15): 22345-22356. https://doi.org/10.1007/s11356-021-17390-w
De-Castro, A. I., Torres-Sánchez, J., Peña, J. M., Jiménez-Brenes, F. M., Csillik, O. and López-Granados, F. 2020. An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sensing. 12(17): 2862. https://doi.org/10.3390/rs12172862
Duke, S.O. 2022. Herbicide effects on soil biology. Agriculture, Ecosystems and Environment, 324, 107713. https://doi.org/10.1016/j.agee.2021.107713
Duke, S.O. 2022. Herbicide Resistant Weeds. CRC Press.
Dyrmann, M., Jørgensen, R. N. and Midtiby, H. S. 2016. RoboWeedSupport: Weed detection in crops using a semi-autonomous robot. Computers and Electronics in Agriculture. 125: 121-131. https://doi.org/10.1016/j.compag.2016.04.024
Espejo-Garcia, B., Mylonas, N., Athanasakos, L. and Fountas, S. 2021. Towards weeds identification assistance through transfer learning. Computers and Electronics in Agriculture. 171: 105306. https://doi.org/10.1016/j.compag.2020.105306
FAO. 2023. The Future of Food and Agriculture. United Nations. ISBN 978-92-5-137552-2
Farooq, M. S., Riaz, F. and Rehman, A. 2023. An intelligent IoT-based weed detection and classification system for smart agriculture. Smart Agricultural Technology. 3: 100126. https://doi.org/10.1016/j.atech.2022.100126
Fennimore, S. A., Cutulle, M. A. and Slaughter, D. C. 2022. Electric weed control: Current status and future directions. Agronomy. 12(5): 1023. https://doi.org/10.3390/agronomy12051023
Fennimore, S. A., Cutulle, M., and Slaughter, D. C. 2020. Robotics in weed control. Current Robotics Reports. 1(3): 19-26. https://doi.org/10.1007/s43154-020-00007-4
Fraceto, L. F., Grillo, R., de Medeiros, G. A., Scognamiglio, V., Rea, G. and Bartolucci, C. 2016. Nanotechnology in agriculture: Which innovation potential does it have? Frontiers in Environmental Science. 4: 20. https://doi.org/10.3389/fenvs.2016.00020
Garcia-Ruiz, F., Perez-Ruiz, M., Agüera, J. and Gonzalez-de-Santos, P. 2022. Autonomous robotic systems integrated with AI for precision weed management. Computers and Electronics in Agriculture. 198: 107023. DOI: 10.1016/j.compag.2022.107023
Garcia-Ruiz, F., Perez-Ruiz, M., Agüera, J. and Gonzalez-de-Santos, P. 2023. Weed control using autonomous nanorobotic systems. Computers and Electronics in Agriculture. 205, 107634. DOI: 10.1016/j.compag.2023.107634
Garcia-Ruiz, F., Perez-Ruiz, M., Agüera-Vega, J., and Gonzalez-de-Santos, P. 2024. AI-based detection of resistance patterns in weed populations. Pest Management Science. 80(1): 45-58. https://doi.org/10.1002/ps.7891
Garcia-Ruiz, F., Pérez-Ruiz, M., Agüera-Vega, J., and Gonzalez-de-Santos, P. 2023. Real-time monitoring systems for crop management. Computers and Electronics in Agriculture. 194, 106742. https://doi.org/10.1016/j.compag.2022.106742
Giselsson, T. M., Jørgensen, R. N. and Midtiby, H. S. 2023. Deep learning for weed detection in wheat fields: A case study in Denmark. Precision Agriculture. 24(1): 123-145. https://doi.org/10.1007/s11119-022-09938-8
Gobor, Z., and Schulze, M. 2023. High-precision laser ablation in agricultural robotics. Precision Agriculture, 24(2), 456-472. https://doi.org/10.1007/s11119-022-09960-w
Gogos, A., Knauer, K. and Bucheli, T. D. 2025. Nanoparticles in agriculture: Current and future perspectives. Nature Nanotechnology. 18(5): 456-470. DOI: 10.1038/s41565-023-01391-6
Gomez, R., Silva, A. and Costa, F. 2023. Solar-powered weeding robot for sustainable agriculture. Renewable Energy. 204: 678-689. https://doi.org/10.1016/j.renene.2023.01.007
Grimstad, L. and From, P. J. 2023. The Thorvald II agricultural robotic system. Robotics. 12(1): 15. https://doi.org/10.3390/robotics12010015
Heap, I. 2023. International Survey of Herbicide-Resistant Weeds. www.weedscience.org
Heap, I. 2024. The International Survey of Herbicide-Resistant Weeds. weedscience.org.
Johnson, A., Wilson, B., Thompson, C., Martin, D. and Taylor, R. 2024. Adaptive learning systems for sustainable agriculture. Advanced Agro-Technology, 6(1), 45 59. https://doi.org/10.1016/j.atech.2024.100234
Kah, M. and Hofmann, T. 2014. Nanopesticide research: Current trends and future priorities. Environment International. 63: 224-235. https://doi.org/10.1016/j.envint.2013.11.015
Kah, M., Walch, H. and Hofmann, T. 2019. Nano-enabled pesticides: Challenges and opportunities. Nature Nanotechnology. 14(7): 634-641. https://doi.org/10.1038/s41565-019-0481-3
Karpelson, M., Wei, G. and Wood, R. J. 2022. Swarm robotics for large-scale farming. Journal of Field Robotics. 39(7): 1123-1145. https://doi.org/10.1002/rob.22045
Kim, S., Park, J. and Lee, H. 2023. Soft robotic grippers for delicate weed removal. Science Robotics, 8(74), eade9123. https://doi.org/10.1126/scirobotics.ade9123
Korres, N.E. et al. 2019. Weed Control: Sustainability, Hazards and Risks. CRC Press. ISBN 978-1
Kumar, S., Nehra, M., Dilbaghi, N., Marrazza, G., Hassan, A. A. and Kim, K. H. 2019. Nano-based smart pesticide formulations: Emerging opportunities for agriculture. ACS Omega. 4(3): 5620-5634. https://doi.org/10.1021/acsomega.8b03677
Kumar, S., Singh, R., Patel, A. and Gupta, P. 2024. Predicting ALS-inhibitor herbicide resistance in wild mustard using deep learning models. Weed Science. 72(2): 134-147. https://doi.org/10.1017/wsc.2024.12
Kumar, S., Singh, R., Patel, A. and Gupta, P. 2024. Targeted delivery systems in precision agriculture. Advanced Drug Delivery Reviews. 196, 114789. DOI: 10.1016/j.addr.2024.114789
Kumar, S., Singh, R., Patel, A., Gupta, P. and Sharma, N. 2024. Water optimization in smart farming. Agricultural Water Management. 271, 107824. https://doi.org/10.1016/j.agwat.2023.107824
Li, H., Chen, Y., Wang, X. and Zhang, Q. 2024. Continuous learning systems for herbicide resistance management. Artificial Intelligence in Agriculture. 8: 23-35. https://doi.org/10.1016/j.aiia.2024.03.005
Li, H., Zhang, Q., Chen, Y. and Wang, X. 2023. Micro/nanorobotic systems for precision agriculture: Current status and future trends. Nature Nanotechnology. 18(5): 512-525. DOI: 10.1038/s41565-023-01478-2
Li, H., Zhang, Q., Chen, Y., Wang, X. and Liu, J. 2023. Yield prediction models in precision agriculture. Field Crops Research, 291, 108790. https://doi.org/10.1016/j.fcr.2023.108790
Lottes, P., Behley, J., Chebrolu, N., Milioto, A. and Stachniss, C. 2020. Robust joint stem detection and crop-weed classification using image sequences for plant-specific treatment in precision farming. IEEE Robotics and Automation Letters. 5(2): 3097-3104. https://doi.org/10.1109/LRA.2020.297842
Mathiassen, S. K., Bak, T., Christensen, S. and Kudsk, P. 2021. Laser weeding with small autonomous vehicles: The future of weed control? Weed Research. 61(4): 267-274. https://doi.org/10.1111/wre.12483
Nuruzzaman, M., Rahman, M. M., Liu, Y. and Naidu, R. 2016. Nanoencapsulation, nano-guard for pesticides: A new window for safe application. Journal of Agricultural and Food Chemistry. 64(7): 1447-1483. https://doi.org/10.1021/acs.jafc.5b05214
Oerke, E.C. 2022. Crop losses to pests. Journal of Agricultural Science, 160(3), 1-15. https://doi.org/10.1017/S0021859622000493
Oliveira, H. C., Stolf-Moreira, R., Martinez, C. B. R., Grillo, R., de Jesus, M. B., and Fraceto, L. F. 2021. Nanoencapsulation enhances the post-emergence herbicidal activity of atrazine against mustard plants. PLoS ONE. 16(3): e0248288. https://doi.org/10.1371/journal.pone.0248288
Prasad, R., Bhattacharyya, A. and Nguyen, Q. D. 2017. Nanotechnology in sustainable agriculture: Recent developments, challenges, and perspectives. Frontiers in Microbiology, 8, 1014. https://doi.org/10.3389/fmicb.2017.01014
Rai, M., Ingle, A. P., Pandit, R., Paralikar, P., Gupta, I., Anasane, N. and Dolenc-Voljč, M. 2018. Copper and copper nanoparticles: Role in management of insect pests and pathogenic microbes. Nanomaterials. 8(11): 815. https://doi.org/10.3390/nano8110815
Saharan, V., Sharma, G., Yadav, M., Choudhary, M. K., Sharma, S. S., Pal, A., and Biswas, P. 2016. Synthesis and in vitro antifungal efficacy of Cu-chitosan nanoparticles against pathogenic fungi of tomato. Frontiers in Microbiology. 6: 1551. https://doi.org/10.3389/fmicb.2015.01551
Shaner, D.L. and Beckie, H.J. 2023. The future for weed control and technology. Pest Management Science. 79(1): 99-107. https://doi.org/10.1002/ps.7190
Silva, V., Mol, H. G. J., Zomer, P., Tienstra, M., Ritsema, C. J. and Geissen, V. 2021. Pesticide residues in European agricultural soils. Science of the Total Environment. 753, 141987. https://doi.org/10.1016/j.scitotenv.2020.141987
Smith, J., Anderson, M., Brown, T. and Davis, K. 2024. Challenges in scaling up agricultural nanorobotics. Nature Reviews Materials. 9(3): 234-248. DOI: 10.1038/s41578-024-00672-3
Smith, J., Anderson, M., Brown, T., Davis, K. and Wilson, E. 2024. Cybersecurity challenges in agricultural IoT. IEEE Internet of Things Journal. 11(3): 2345 2358. https://doi.org/10.1109/JIOT.2023.3328765
Thompson, L. R. and Lee, C. M. 2024. Pulsed electrolysis for root destruction in turfgrass. Weed Technology. 38(1): 45-53. https://doi.org/10.1017/wet.2023.12
Wang, A., Zhang, W. and Wei, X. 2022. A review on weed detection using ground-based machine vision and image processing techniques. Agricultural Systems. 196, 103336. https://doi.org/10.1016/j.agsy.2021.103336
Wang, B., Li, W. and Zhang, D. 2023. Hyperspectral imaging for early-stage weed detection. Remote Sensing of Environment, 287, 113492. https://doi.org/10.1016/j.rse.2023.113492
Wang, Y., Chen, X., Zhang, L., and Liu, J. 2024. Advanced nanosensors for precision agriculture. Nature Nanotechnology. 19(2): 145-158.
Wang, Y., Chen, X., Zhang, L. and Liu, J. 2024. Autonomous agricultural nanorobots: From sensing to actuation. Science Robotics, 9(53), eade4567. DOI: 10.1126/scirobotics.ade4567
Zhang, M., Li, X., Liu, Z., and Wang, P. 2023. Autonomous robotic systems in smart farming. Science Robotics. 8(79): eabq6543
Zhang, M., Li, X., Liu, Z., and Wang, P. 2023. Navigation systems for agricultural nanorobots. IEEE Transactions on Robotics. 39(2): 456-470. DOI: 10.1109/TRO.2023.3256789
Zhang, M., Li, X., Wang, P. and Liu, Z. 2023. Machine learning approaches for herbicide resistance prediction in weeds. Computers and Electronics in Agriculture. 204, 107532. https://doi.org/10.1016/j.compag.2022.107532
Zhang, Y., Li, X. and Wang, Q. 2022. Real-time weed detection in soybean fields using YOLOv5 and UAV imagery. Computers and Electronics in Agriculture, 198, 107012. https://doi.org/10.1016/j.compag.2022.107012
Zhang, Y., Li, X., Wang, Q., and Liu, M. 2023. Vision Transformers for real-time weed detection in precision agriculture. Nature Machine Intelligence. 5(4): 321-330. https://doi.org/10.1038/s42256-023-00625-5
Zhao, L., Huang, Y., Hu, J., Zhou, H., Adeleye, A. S. and Keller, A. A. 2021. Metabolomics reveals Cu(OH)2 nanopesticide-activated anti-oxidative pathways and decreased beneficial antioxidants in spinach leaves. Nature Nanotechnology. 16(2): 198-206. https://doi.org/10.1038/s41565-020-00803-1
Zimdahl, R.L. 2018. Fundamentals of Weed Science (5th ed.). Academic Press. ISBN 978-0-12-
Adhikari, S. P., Zhou, J. and Li, Y. 2021. LiDAR-based weed density estimation in apple orchards. Biosystems Engineering. 212: 152-163. https://doi.org/10.1016/j.biosystemseng.2021.10.012
Bauer, A., Schmidt, J. and Ulber, B. 2023. PhotonHarvester: A laser-based weeding robot for greenhouse applications. Biosystems Engineering. 225: 1-12. https://doi.org/10.1016/j.biosystemseng.2023.01.001
Bawden, O., Kulk, J., Russell, R., McCool, C., Dayoub, F., Lehnert, C., and Perez, T. 2017. Robot for weed species plant-specific management. Journal of Field Robotics. 34(6): 1179-1199. https://doi.org/10.1002/rob.21727
Chebrolu, N., Lottes, P., Schaefer, A., Winterhalter, W., Burgard, W. and Stachniss, C. 2022. Agricultural robot dataset for plant classification, localization, and mapping on sugar beet fields. The International Journal of Robotics Research. 41(3): 267-286. https://doi.org/10.1177/02783649211050634
Chen, L., Zhang, H. and Liu, M. 2023. Edge AI-based weed detection system for paddy fields. IEEE Transactions on AgriFood Electronics, 1(1), 45-56. https://doi.org/10.1109/TAFE.2023.00007
Chen, L., Zhang, H., Zhou, J. and Wu, D. 2022. Multimodal sensor fusion for agricultural robotics. IEEE Transactions on AgriFood Electronics. 2(3): 145-156. https://doi.org/10.1109/TAFE.2022.00012
Chen, Z., Liu, Y., Wang, J., and Zhang, K. 2024. AI-driven decision-making in agricultural networks. Artificial Intelligence. 325: 104-118
Chen, Z., Liu, Y., Wang, J. and Zhang, K. 2024. AI-driven decision making in nanorobotic farming systems. Artificial Intelligence in Agriculture. 8: 45-62. DOI: 10.1016/j.aiia.2024.03.002
Chhipa, H. 2017. Nanofertilizers and nanopesticides for agriculture. Environmental Chemistry Letters. 15(1): 15-22. https://doi.org/10.1007/s10311-016-0600-4
Choudhary, R. C., Kumaraswamy, R. V., Kumari, S., Pal, A., Raliya, R., Biswas, P. and Saharan, V. 2020. Zinc-encapsulated chitosan nanoparticles to promote maize crop yield. Scientific Reports. 10(1): 1-12. https://doi.org/10.1038/s41598-020-62591-x
Das, S., Ray, A., Pandey, P., Mohanty, S. and Das, B. 2022. Copper oxide nanoparticles as eco-friendly nano-herbicide for weed management in rice fields: A field study in Punjab, India. Environmental Science and Pollution Research. 29(15): 22345-22356. https://doi.org/10.1007/s11356-021-17390-w
De-Castro, A. I., Torres-Sánchez, J., Peña, J. M., Jiménez-Brenes, F. M., Csillik, O. and López-Granados, F. 2020. An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sensing. 12(17): 2862. https://doi.org/10.3390/rs12172862
Duke, S.O. 2022. Herbicide effects on soil biology. Agriculture, Ecosystems and Environment, 324, 107713. https://doi.org/10.1016/j.agee.2021.107713
Duke, S.O. 2022. Herbicide Resistant Weeds. CRC Press.
Dyrmann, M., Jørgensen, R. N. and Midtiby, H. S. 2016. RoboWeedSupport: Weed detection in crops using a semi-autonomous robot. Computers and Electronics in Agriculture. 125: 121-131. https://doi.org/10.1016/j.compag.2016.04.024
Espejo-Garcia, B., Mylonas, N., Athanasakos, L. and Fountas, S. 2021. Towards weeds identification assistance through transfer learning. Computers and Electronics in Agriculture. 171: 105306. https://doi.org/10.1016/j.compag.2020.105306
FAO. 2023. The Future of Food and Agriculture. United Nations. ISBN 978-92-5-137552-2
Farooq, M. S., Riaz, F. and Rehman, A. 2023. An intelligent IoT-based weed detection and classification system for smart agriculture. Smart Agricultural Technology. 3: 100126. https://doi.org/10.1016/j.atech.2022.100126
Fennimore, S. A., Cutulle, M. A. and Slaughter, D. C. 2022. Electric weed control: Current status and future directions. Agronomy. 12(5): 1023. https://doi.org/10.3390/agronomy12051023
Fennimore, S. A., Cutulle, M., and Slaughter, D. C. 2020. Robotics in weed control. Current Robotics Reports. 1(3): 19-26. https://doi.org/10.1007/s43154-020-00007-4
Fraceto, L. F., Grillo, R., de Medeiros, G. A., Scognamiglio, V., Rea, G. and Bartolucci, C. 2016. Nanotechnology in agriculture: Which innovation potential does it have? Frontiers in Environmental Science. 4: 20. https://doi.org/10.3389/fenvs.2016.00020
Garcia-Ruiz, F., Perez-Ruiz, M., Agüera, J. and Gonzalez-de-Santos, P. 2022. Autonomous robotic systems integrated with AI for precision weed management. Computers and Electronics in Agriculture. 198: 107023. DOI: 10.1016/j.compag.2022.107023
Garcia-Ruiz, F., Perez-Ruiz, M., Agüera, J. and Gonzalez-de-Santos, P. 2023. Weed control using autonomous nanorobotic systems. Computers and Electronics in Agriculture. 205, 107634. DOI: 10.1016/j.compag.2023.107634
Garcia-Ruiz, F., Perez-Ruiz, M., Agüera-Vega, J., and Gonzalez-de-Santos, P. 2024. AI-based detection of resistance patterns in weed populations. Pest Management Science. 80(1): 45-58. https://doi.org/10.1002/ps.7891
Garcia-Ruiz, F., Pérez-Ruiz, M., Agüera-Vega, J., and Gonzalez-de-Santos, P. 2023. Real-time monitoring systems for crop management. Computers and Electronics in Agriculture. 194, 106742. https://doi.org/10.1016/j.compag.2022.106742
Giselsson, T. M., Jørgensen, R. N. and Midtiby, H. S. 2023. Deep learning for weed detection in wheat fields: A case study in Denmark. Precision Agriculture. 24(1): 123-145. https://doi.org/10.1007/s11119-022-09938-8
Gobor, Z., and Schulze, M. 2023. High-precision laser ablation in agricultural robotics. Precision Agriculture, 24(2), 456-472. https://doi.org/10.1007/s11119-022-09960-w
Gogos, A., Knauer, K. and Bucheli, T. D. 2025. Nanoparticles in agriculture: Current and future perspectives. Nature Nanotechnology. 18(5): 456-470. DOI: 10.1038/s41565-023-01391-6
Gomez, R., Silva, A. and Costa, F. 2023. Solar-powered weeding robot for sustainable agriculture. Renewable Energy. 204: 678-689. https://doi.org/10.1016/j.renene.2023.01.007
Grimstad, L. and From, P. J. 2023. The Thorvald II agricultural robotic system. Robotics. 12(1): 15. https://doi.org/10.3390/robotics12010015
Heap, I. 2023. International Survey of Herbicide-Resistant Weeds. www.weedscience.org
Heap, I. 2024. The International Survey of Herbicide-Resistant Weeds. weedscience.org.
Johnson, A., Wilson, B., Thompson, C., Martin, D. and Taylor, R. 2024. Adaptive learning systems for sustainable agriculture. Advanced Agro-Technology, 6(1), 45 59. https://doi.org/10.1016/j.atech.2024.100234
Kah, M. and Hofmann, T. 2014. Nanopesticide research: Current trends and future priorities. Environment International. 63: 224-235. https://doi.org/10.1016/j.envint.2013.11.015
Kah, M., Walch, H. and Hofmann, T. 2019. Nano-enabled pesticides: Challenges and opportunities. Nature Nanotechnology. 14(7): 634-641. https://doi.org/10.1038/s41565-019-0481-3
Karpelson, M., Wei, G. and Wood, R. J. 2022. Swarm robotics for large-scale farming. Journal of Field Robotics. 39(7): 1123-1145. https://doi.org/10.1002/rob.22045
Kim, S., Park, J. and Lee, H. 2023. Soft robotic grippers for delicate weed removal. Science Robotics, 8(74), eade9123. https://doi.org/10.1126/scirobotics.ade9123
Korres, N.E. et al. 2019. Weed Control: Sustainability, Hazards and Risks. CRC Press. ISBN 978-1
Kumar, S., Nehra, M., Dilbaghi, N., Marrazza, G., Hassan, A. A. and Kim, K. H. 2019. Nano-based smart pesticide formulations: Emerging opportunities for agriculture. ACS Omega. 4(3): 5620-5634. https://doi.org/10.1021/acsomega.8b03677
Kumar, S., Singh, R., Patel, A. and Gupta, P. 2024. Predicting ALS-inhibitor herbicide resistance in wild mustard using deep learning models. Weed Science. 72(2): 134-147. https://doi.org/10.1017/wsc.2024.12
Kumar, S., Singh, R., Patel, A. and Gupta, P. 2024. Targeted delivery systems in precision agriculture. Advanced Drug Delivery Reviews. 196, 114789. DOI: 10.1016/j.addr.2024.114789
Kumar, S., Singh, R., Patel, A., Gupta, P. and Sharma, N. 2024. Water optimization in smart farming. Agricultural Water Management. 271, 107824. https://doi.org/10.1016/j.agwat.2023.107824
Li, H., Chen, Y., Wang, X. and Zhang, Q. 2024. Continuous learning systems for herbicide resistance management. Artificial Intelligence in Agriculture. 8: 23-35. https://doi.org/10.1016/j.aiia.2024.03.005
Li, H., Zhang, Q., Chen, Y. and Wang, X. 2023. Micro/nanorobotic systems for precision agriculture: Current status and future trends. Nature Nanotechnology. 18(5): 512-525. DOI: 10.1038/s41565-023-01478-2
Li, H., Zhang, Q., Chen, Y., Wang, X. and Liu, J. 2023. Yield prediction models in precision agriculture. Field Crops Research, 291, 108790. https://doi.org/10.1016/j.fcr.2023.108790
Lottes, P., Behley, J., Chebrolu, N., Milioto, A. and Stachniss, C. 2020. Robust joint stem detection and crop-weed classification using image sequences for plant-specific treatment in precision farming. IEEE Robotics and Automation Letters. 5(2): 3097-3104. https://doi.org/10.1109/LRA.2020.297842
Mathiassen, S. K., Bak, T., Christensen, S. and Kudsk, P. 2021. Laser weeding with small autonomous vehicles: The future of weed control? Weed Research. 61(4): 267-274. https://doi.org/10.1111/wre.12483
Nuruzzaman, M., Rahman, M. M., Liu, Y. and Naidu, R. 2016. Nanoencapsulation, nano-guard for pesticides: A new window for safe application. Journal of Agricultural and Food Chemistry. 64(7): 1447-1483. https://doi.org/10.1021/acs.jafc.5b05214
Oerke, E.C. 2022. Crop losses to pests. Journal of Agricultural Science, 160(3), 1-15. https://doi.org/10.1017/S0021859622000493
Oliveira, H. C., Stolf-Moreira, R., Martinez, C. B. R., Grillo, R., de Jesus, M. B., and Fraceto, L. F. 2021. Nanoencapsulation enhances the post-emergence herbicidal activity of atrazine against mustard plants. PLoS ONE. 16(3): e0248288. https://doi.org/10.1371/journal.pone.0248288
Prasad, R., Bhattacharyya, A. and Nguyen, Q. D. 2017. Nanotechnology in sustainable agriculture: Recent developments, challenges, and perspectives. Frontiers in Microbiology, 8, 1014. https://doi.org/10.3389/fmicb.2017.01014
Rai, M., Ingle, A. P., Pandit, R., Paralikar, P., Gupta, I., Anasane, N. and Dolenc-Voljč, M. 2018. Copper and copper nanoparticles: Role in management of insect pests and pathogenic microbes. Nanomaterials. 8(11): 815. https://doi.org/10.3390/nano8110815
Saharan, V., Sharma, G., Yadav, M., Choudhary, M. K., Sharma, S. S., Pal, A., and Biswas, P. 2016. Synthesis and in vitro antifungal efficacy of Cu-chitosan nanoparticles against pathogenic fungi of tomato. Frontiers in Microbiology. 6: 1551. https://doi.org/10.3389/fmicb.2015.01551
Shaner, D.L. and Beckie, H.J. 2023. The future for weed control and technology. Pest Management Science. 79(1): 99-107. https://doi.org/10.1002/ps.7190
Silva, V., Mol, H. G. J., Zomer, P., Tienstra, M., Ritsema, C. J. and Geissen, V. 2021. Pesticide residues in European agricultural soils. Science of the Total Environment. 753, 141987. https://doi.org/10.1016/j.scitotenv.2020.141987
Smith, J., Anderson, M., Brown, T. and Davis, K. 2024. Challenges in scaling up agricultural nanorobotics. Nature Reviews Materials. 9(3): 234-248. DOI: 10.1038/s41578-024-00672-3
Smith, J., Anderson, M., Brown, T., Davis, K. and Wilson, E. 2024. Cybersecurity challenges in agricultural IoT. IEEE Internet of Things Journal. 11(3): 2345 2358. https://doi.org/10.1109/JIOT.2023.3328765
Thompson, L. R. and Lee, C. M. 2024. Pulsed electrolysis for root destruction in turfgrass. Weed Technology. 38(1): 45-53. https://doi.org/10.1017/wet.2023.12
Wang, A., Zhang, W. and Wei, X. 2022. A review on weed detection using ground-based machine vision and image processing techniques. Agricultural Systems. 196, 103336. https://doi.org/10.1016/j.agsy.2021.103336
Wang, B., Li, W. and Zhang, D. 2023. Hyperspectral imaging for early-stage weed detection. Remote Sensing of Environment, 287, 113492. https://doi.org/10.1016/j.rse.2023.113492
Wang, Y., Chen, X., Zhang, L., and Liu, J. 2024. Advanced nanosensors for precision agriculture. Nature Nanotechnology. 19(2): 145-158.
Wang, Y., Chen, X., Zhang, L. and Liu, J. 2024. Autonomous agricultural nanorobots: From sensing to actuation. Science Robotics, 9(53), eade4567. DOI: 10.1126/scirobotics.ade4567
Zhang, M., Li, X., Liu, Z., and Wang, P. 2023. Autonomous robotic systems in smart farming. Science Robotics. 8(79): eabq6543
Zhang, M., Li, X., Liu, Z., and Wang, P. 2023. Navigation systems for agricultural nanorobots. IEEE Transactions on Robotics. 39(2): 456-470. DOI: 10.1109/TRO.2023.3256789
Zhang, M., Li, X., Wang, P. and Liu, Z. 2023. Machine learning approaches for herbicide resistance prediction in weeds. Computers and Electronics in Agriculture. 204, 107532. https://doi.org/10.1016/j.compag.2022.107532
Zhang, Y., Li, X. and Wang, Q. 2022. Real-time weed detection in soybean fields using YOLOv5 and UAV imagery. Computers and Electronics in Agriculture, 198, 107012. https://doi.org/10.1016/j.compag.2022.107012
Zhang, Y., Li, X., Wang, Q., and Liu, M. 2023. Vision Transformers for real-time weed detection in precision agriculture. Nature Machine Intelligence. 5(4): 321-330. https://doi.org/10.1038/s42256-023-00625-5
Zhao, L., Huang, Y., Hu, J., Zhou, H., Adeleye, A. S. and Keller, A. A. 2021. Metabolomics reveals Cu(OH)2 nanopesticide-activated anti-oxidative pathways and decreased beneficial antioxidants in spinach leaves. Nature Nanotechnology. 16(2): 198-206. https://doi.org/10.1038/s41565-020-00803-1
Zimdahl, R.L. 2018. Fundamentals of Weed Science (5th ed.). Academic Press. ISBN 978-0-12-
