اخبار و اعلانات

 آمار

تعداد نشریات55
تعداد شماره‌ها2,140
تعداد مقالات22,121
تعداد مشاهده مقاله96,128,619
تعداد دریافت فایل اصل مقاله34,100,968
کیرتیواسان, ام., کوکیلاوانی, اس., شانتی, ام., دیباکاران, گا., پنگایار سلوِی, آر., مورُوگان, ام., ایلایاباراتی, تی., شانموگام, پی. اس., سلوِوا کومار, ام.. (1404). مروری بر یادگیری ماشین برای تشخیص آفات گوجه‌فرنگی. سامانه مدیریت نشریات علمی, (), -. doi: 10.22067/jam.2025.90762.1314
ام. کیرتیواسان; اس. کوکیلاوانی; ام. شانتی; گا. دیباکاران; آر. پنگایار سلوِی; ام. مورُوگان; تی. ایلایاباراتی; پی. اس. شانموگام; ام. سلوِوا کومار. "مروری بر یادگیری ماشین برای تشخیص آفات گوجه‌فرنگی". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.22067/jam.2025.90762.1314
کیرتیواسان, ام., کوکیلاوانی, اس., شانتی, ام., دیباکاران, گا., پنگایار سلوِی, آر., مورُوگان, ام., ایلایاباراتی, تی., شانموگام, پی. اس., سلوِوا کومار, ام.. (1404). 'مروری بر یادگیری ماشین برای تشخیص آفات گوجه‌فرنگی', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.22067/jam.2025.90762.1314
کیرتیواسان, ام., کوکیلاوانی, اس., شانتی, ام., دیباکاران, گا., پنگایار سلوِی, آر., مورُوگان, ام., ایلایاباراتی, تی., شانموگام, پی. اس., سلوِوا کومار, ام.. مروری بر یادگیری ماشین برای تشخیص آفات گوجه‌فرنگی. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.22067/jam.2025.90762.1314

مروری بر یادگیری ماشین برای تشخیص آفات گوجه‌فرنگی

ماشین های کشاورزی
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 04 شهریور 1404    اصل مقاله (821.39 K)
نوع مقاله: مقاله مروری انگلیسی
شناسه دیجیتال (DOI): 10.22067/jam.2025.90762.1314
نویسندگان
ام. کیرتیواسان1؛ اس. کوکیلاوانی* 1؛ ام. شانتی2؛ گا. دیباکاران1؛ آر. پنگایار سلوِی3؛ ام. مورُوگان4؛ تی. ایلایاباراتی4؛ پی. اس. شانموگام4؛ ام. سلوِوا کومار1
1مرکز پژوهش اقلیم‌کشاورزی، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند
2مرکز مطالعات حفاظت گیاه، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند
3گروه علوم فیزیکی و فناوری اطلاعات، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند
4گروه حشره‌شناسی کشاورزی، دانشگاه کشاورزی تامیل نادو، کویمباتور، تامیل نادو، هند
چکیده
تاثیر یک مولفه جوی یا متغیر هواشناسی بر میزبان، پاتوژن یا تعامل آن‌ها در محیط‌های کنترل‌شده، بخش عمده‌ای از تاثیر تغییرات اقلیمی بر آفات و بیماری‌های گیاهان را به خود اختصاص داده است. تغییرات اقلیمی می‌تواند منجر به تغییراتی در مراحل و سرعت رشد آفات و بیماری‌ها، مقاومت میزبان و فیزیولوژی تعاملات میزبان-پاتوژن یا میزبان-آفت شود که می‌تواند باعث آسیب قابل‌توجه و کاهش عملکرد محصول گوجه‌فرنگی گردد. در سال‌های گذشته، رویکردهای مختلف در پیش‌بینی دقیق آفات و بیماری‌ها ناموفق بوده‌اند. ولی پیشرفت قابل‌توجه در شبکه‌های عصبی کانولوشن عمیق (DCNN)، باعث ایجاد انقلابی در تشخیص زودهنگام آفات و بیماری‌ها در محصولات کشاورزی شده است. این مدل‌های هوش مصنوعی با تجزیه و تحلیل مقادیر زیادی از داده‌های اقلیمی حال حاضر و گذشته، در کنار شناسایی اشیا و طبقه‌بندی تصاویر، می‌توانند شیوع بیماری‌ها را با دقت چشمگیری پیش‌بینی کنند. با این حال، درک ریزاقلیم خاص مناسب برای هر آفت و بیماری برای مداخله موثر بسیار مهم است. ترکیب دو عنصر داده‌های اقلیمی با پردازش تصویر، یک رویکرد قدرتمند و هدفمند برای حفاظت از محصولات ایجاد می‌کند. یک سیستم هشداردهنده می‌تواند به کاهش استفاده از آفت‌کش‌ها کمک کند و در نتیجه هزینه تولید محصول و آلودگی محیط‌زیست را کاهش دهد. برای پیش‌بینی بهتر آفات و بیماری‌ها در شرایط مختلف، باید از سرورهای ابری مناسب و شبکه‌های حسگر مبتنی بر اینترنت اشیا استفاده کرد.
کلیدواژه‌ها
CNN؛ پیش‌بینی آفات؛ خرد اقلیم؛ گوجه‌فرنگی؛ یادگیری ماشین
مراجع
  1. Agarwal, M., Gupta, S. K., & Biswas, K. (2020). Development of Efficient CNN model for Tomato crop disease identification. Sustainable Computing: Informatics and Systems, 28, 100407. https://doi.org/10.1016/j.suscom.2020.100407
  2. Ahmed, F. (2018). An IoT-big data based machine learning technique for forecasting water requirement in irrigation field. In Research and Practical Issues of Enterprise Information Systems: 11th IFIP WG 8.9 Working Conference, CONFENIS 2017, Shanghai, China, October 18-20, 2017, Revised Selected Papers 11(pp. 67-77). Springer International Publishing. https://doi.org/10.1007/978-3-319-94845-4_7
  3. Ahmed, S., Basu, N., Nicholson, C. E., Rutter, S. R., Marshall, J. R., Perry, J. J., & Dean, J. R. (2024). Use of machine learning for monitoring the growth stages of an agricultural crop. Sustainable Food Technology, 2(1), 104-125. https://doi.org/10.1039/D3FB00101F
  4. Ai, Y., Sun, C., Tie, J., & Cai, X. (2020). Research on recognition model of crop diseases and insect pests based on deep learning in harsh environments. IEEE Access, 8, 171686-171693. https://doi.org/10.1109/ACCESS.2020.3025325
  5. Alam, M. Z., Haque, M. M., Islam, M. S., Hossain, E., Hasan, S. B., Hasan, S. B., & Hossain, M. S. (2016). Comparative study of integrated pest management and farmers practices on sustainable environment in the rice ecosystem. International Journal of Zoology, 2016. https://doi.org/10.1155/2016/7286040
  6. Alves, L., Silva, R. R., & Bernardino, J. (2018). System to Predict Diseases in Vineyards and Olive Groves using Data Mining and Geolocation. In ICSOFT, (pp. 713-721). https://doi.org/10.5220/0006914306790687
  7. Balikai, R., Venkatesh, H., & Sagar, D. (2021). Development of models to predict insect pest populations-an eco-friendly tactic for pest management. Journal of Farm Sciences, 32(1), 1-13.
  8. Barbedo, J. G. A. (2020). Detecting and classifying pests in crops using proximal images and machine learning: A review. Ai, 1(2), 312-328. https://doi.org/10.3390/ai1020021
  9. Battisti, A., & Larsson, S. (2015). Climate change and insect pest distribution range. In Climate change and insect pests (pp. 1-15). CABI Wallingford UK.
  10. Bebber, D. P., Ramotowski, M. A., & Gurr, S. J. (2013). Crop pests and pathogens move polewards in a warming world. Nature climate change, 3(11), 985-988. https://doi.org/10.1038/nclimate1990
  11. Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758
  12. Bestelmeyer, B. T., Marcillo, G., McCord, S. E., Mirsky, S., Moglen, G., Neven, L. G., Peters, D., Sohoulande, C., & Wakie, T. (2020). Scaling up agricultural research with artificial intelligence. IT Professional, 22(3), 33-38. https://doi.org/10.1109/MITP.2020.2986062
  13. Bhatia, A., Chug, A., & Singh, A. P. (2020). Hybrid SVM-LR classifier for powdery mildew disease prediction in tomato plant. In 2020 7th International conference on signal processing and integrated networks (SPIN) (pp. 218-223). IEEE. https://doi.org/10.1109/SPIN48934.2020.9071202
  14. Boulent, J., Foucher, S., Théau, J., & St-Charles, P.-L. (2019). Convolutional neural networks for the automatic identification of plant diseases. Frontiers in Plant Science, 10, 941. https://doi.org/10.3389/fpls.2019.00941
  15. Brahimi, M., Boukhalfa, K., & Moussaoui, A. (2017). Deep learning for tomato diseases: classification and symptoms visualization. Applied Artificial Intelligence, 31(4), 299-315. https://doi.org/10.1080/08839514.2017.1315516
  16. Brahmi, W., Jdey, I., & Drira, F. (2024). Exploring the role of Convolutional Neural Networks (CNN) in dental radiography segmentation: A comprehensive Systematic Literature Review. Engineering Applications of Artificial Intelligence, 133, 108510. https://doi.org/10.1016/j.engappai.2024.108510
  17. Brasier, C. (2008). The biosecurity threat to the UK and global environment from international trade in plants. Plant Pathology, 57(5), 792-808. https://doi.org/10.1111/j.1365-3059.2008.01886.x
  18. Chattopadhyay, C., Agrawal, R., Kumar, A., Bhar, L., Meena, P., Meena, R., Khan, S., Chattopadhyay, A., Awasthi, R., & Singh, S. (2005a). Epidemiology and forecasting of Alternaria blight of oilseed brassica in India-a case study/Epidemiologie und Prognose von Alternaria brassicae an Brassica-Ölfrüchten in Indien-Eine Fallstudie. Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz/Journal of Plant Diseases and Protection, 112(4) 351-365. http://www.jstor.org/stable/43215636
  19. Chattopadhyay, C., Agrawal, R., Kumar, A., Singh, Y., Roy, S., Khan, S., Bhar, L., Chakravarthy, N., Srivastava, A., & Patel, B. (2005b). Forecasting of Lipaphis erysimi on oilseed Brassicas in India—a case study. Crop Protection, 24(12), 1042-1053. https://doi.org/10.1016/j.cropro.2005.02.010
  20. Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61-69. https://doi.org/10.1016/j.compag.2018.05.012
  21. Cintra, M. E., Meira, C. A., Monard, M. C., Camargo, H. A., & Rodrigues, L. H. (2011). The use of fuzzy decision trees for coffee rust warning in Brazilian crops. 11th International conference on intelligent systems design and applications, Cordoba, Spain, pp. 1347-1352. https://doi.org/10.1109/ISDA.2011.6121847
  22. Collier, R. H. (2017). Pest and disease prediction models. In: Thomas, Brian and Murray, Brian G. and Murphy, Denis J., (eds.) Encyclopedia of Applied Plant Sciences: second edition. Waltham, MA: Academic Press, pp. 120-123. https://doi.org/10.1016/B978-0-12-394807-6.00058-7
  23. Corrales, D. C., Corrales, J. C., & Figueroa-Casas, A. (2015). Towards Detecting Crop Diseases and Pest by Supervised Learning. Ingeniería y Universidad, 19(13), 207-228. https://doi.org/11144/Javeriana.iyu19-1.tdcd
  24. David, D. (2023). Weather Based Prediction Models for Disease and Pest Using Machine Learning: A Review. Asian Journal of Agricultural Extension, Economics & Sociology, 41(11), 334-345. https://doi.org/10.9734/ajaees/2023/v41i112290
  25. de Souza, W. D., Remboski, T. B., de Aguiar, M. S., & Júnior, P. R. F. (2017). A model for pest infestation prediction in crops based on local meteorological monitoring stations. Sixteenth Mexican International Conference on Artificial Intelligence (MICAI), Ensenada, Mexico, pp. 39-45. https://doi.org/10.1109/MICAI-2017.2017.00015
  26. Desai, A., Chattopadhyay, C., Agrawal, R., Kumar, A., Meena, R., Meena, P., Sharma, K., Rao, M. S., Prasad, Y., & Ramakrishna, Y. (2004). Brassica juncea powdery mildew epidemiology and weatherbased forecasting models for India—a case study/Die Krankheitsentwicklung des Echten Mehltaus (Erysiphe cruciferarum) auf Brassica juncea und wetterbasierende Modelle zur Vorausschätzung seiner epidemiologischen Entwicklung in Indien—Eine Fallstudie. Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz/Journal of Plant Diseases and Protection, 111(5), 429-438. https://www.jstor.org/stable/43216277
  27. Dhawan, A. (2016). Integrated pest management in cotton. Integrated Pest Management in the Tropics, 499-575.
  28. Domingues, T., Brandão, T., & Ferreira, J. C. (2022). Machine learning for detection and prediction of crop diseases and pests: A comprehensive survey. Agriculture, 12(9), 1350. https://doi.org/10.3390/agriculture12091350
  29. Du, J. (2018). Understanding of object detection based on CNN family and YOLO. Journal of Physics: Conference Series, 2nd International Conference on Machine Vision and Information Technology (CMVIT 2018), Hong Kong, 1004, 012029. https://doi.org/10.1088/1742-6596/1004/1/012029
  30. Early, R., Bradley, B. A., Dukes, J. S., Lawler, J. J., Olden, J. D., Blumenthal, D. M., Gonzalez, P., Grosholz, E. D., Ibañez, I., & Miller, L. P. (2016). Global threats from invasive alien species in the twenty-first century and national response capacities. Nature communications, 7(1), 12485. https://doi.org/10.1038/ncomms12485
  31. Emebo, O., Fori, B., Victor, G., & Zannu, T. (2019). Development of tomato septoria leaf spot and tomato mosaic diseases detection device using raspberry Pi and deep convolutional neural networks. Journal of Physics: Conference Series, 1299, https://doi.org/1088/1742-6596/1299/1/012118
  32. Fuentes, A., Yoon, S., Kim, S. C., & Park, D. S. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9), 2022. https://doi.org/10.3390/s17092022
  33. Gauriau, O., Galárraga, L., Brun, F., Termier, A., Davadan, L., & Joudelat, F. (2024). Comparing machine-learning models of different levels of complexity for crop protection: A look into the complexity-accuracy tradeoff. Smart Agricultural Technology, 7, 100380. https://doi.org/10.1016/j.atech.2023.100380
  34. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 580-587.
  35. Gonzalez-Huitron, V., León-Borges, J. A., Rodriguez-Mata, A., Amabilis-Sosa, L. E., Ramírez-Pereda, B., & Rodriguez, H. (2021). Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4. Computers and Electronics in Agriculture, 181, 105951. https://doi.org/10.1016/j.compag.2020.105951
  36. Gurle, A. S., Barathe, S. N., Gangule, R. S., Jagtap, S. D., & Patankar, T. (2019). Survey paper on tomato crop disease detection and pest management. International Journal of Applied Evolutionary Computation (IJAEC), 10(3), 10-18. https://doi.org/10.4018/IJAEC.2019070102
  37. Halstead, M., McCool, C., Denman, S., Perez, T., & Fookes, C. (2018). Fruit quantity and ripeness estimation using a robotic vision system. IEEE robotics and automation LETTERS, 3(4), 2995-3002. https://doi.org/10.1109/LRA.2018.2849514
  38. Heeb, L., Jenner, E., & Cock, M. J. (2019). Climate-smart pest management: building resilience of farms and landscapes to changing pest threats. Journal of Pest Science, 92(3), 951-969. https://doi.org/10.1007/s10340-019-01083-y
  39. Hernandez-Espinoza, L. H., & Barrios-Masias, F. H. (2020). Physiological and anatomical changes in tomato roots in response to low water stress. Scientia Horticulturae, 265, 109208. https://doi.org/10.1016/j.scienta.2020.109208
  40. Hu, W., Hong, W., Wang, H., Liu, M., & Liu, S. (2023). A Study on Tomato Disease and Pest Detection Method. Applied Sciences, 13(18), 10063. https://doi.org/10.3390/app131810063
  41. Hughes, D., & Salathé, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv,1511.08060. https://doi.org/10.48550/arXiv.1511.08060
  42. Hunt, E. R., Jr. Daughtry, C. S. T., Walthall, C. L., McMurtrey, J. E., & Dulaney, W. P. (2004). Agricultural remote sensing using radio-controlled model aircraft. Digital Imaging and Spectral Techniques: Applications to Precision Agriculture and Crop Physiology. ASA Special Publication no. 66. https://doi.org/10.2134/asaspecpub66.c15
  43. Ifft, J., Kuhns, R., & Patrick, K. (2018). Can machine learning improve prediction–an application with farm survey data. International Food and Agribusiness Management Review, 21(8), 1083-1098. https://doi.org/10.22434/IFAMR2017.0098
  44. Ilic, M., Ilic, S., Jovic, S., & Panic, S. (2018). Early cherry fruit pathogen disease detection based on data mining prediction. Computers and electronics in agriculture, 150, 418-425. https://doi.org/10.1016/j.compag.2018.05.008
  45. Isard, S. A., Russo, J. M., Magarey, R. D., Golod, J., & VanKirk, J. R. (2015). Integrated pest information platform for extension and education (iPiPE): progress through sharing. Journal of Integrated Pest Management, 6(1), 15. https://doi.org/10.1093/jipm/pmv013
  46. Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15-30. https://doi.org/10.1016/j.aac.2022.10.001
  47. Jawade, P., Chaugule, D., Patil, D., & Shinde, H. (2020). Disease prediction of mango crop using machine learning and IoT. Advances in Decision Sciences, Image Processing, Security and Computer Vision: International Conference on Emerging Trends in Engineering (ICETE), 1, Springer, Cham. https://doi.org/10.1007/978-3-030-24322-7_33
  48. Jia, S., Gao, H., & Hang, X. (2020). Tomato Pests and Diseases Classification Model Based on Optimized Convolutional Neural Network. Journal of Physics: Conference Series, 1437. https://doi.org/10.1088/1742-6596/1437/1/012052
  49. Junjing, L., Leigang, S., & Wenjiang, H. (2019). Research Progress in Monitoring and Forecasting of Crop Diseases and Pests by Remote Sensing. Remote Sensing Technology and Application, 34(1), 21-32. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0021
  50. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). A review of the use of convolutional neural networks in agriculture. The Journal of Agricultural Science, 156(3), 312-322. https://doi.org/10.1017/S0021859618000436
  51. Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24-49. https://doi.org/10.1016/j.isprsjprs.2020.12.010
  52. Kaur, K., & Kaur, M. (2018). Prediction of plant disease from weather forecasting using data mining. International Journal on Future Revolution in Computer Science & Communication Engineering, 4, 685-688. http://www.ijfrcsce.org/index.php/ijfrcsce/article/view/1591.
  53. Kenis, M., Rabitsch, W., Auger-Rozenberg, M. A., & Roques, A. (2007). How can alien species inventories and interception data help us prevent insect invasions? Bulletin of Entomological Research, 97(5), 489-502. https://doi.org/10.1017/S0007485307005184
  54. Kim, J. A., Sung, J. Y., & Park, S. H. (2020). Comparison of Faster-RCNN, YOLO, and SSD for real-time vehicle type recognition. 2020 IEEE international conference on consumer electronics-Asia (ICCE-Asia). https://doi.org/10.1109/ICCE-Asia49877.2020.9277040
  55. Lee, Y. S., & Liu, W. Y. (2014). Forecasting value of agricultural imports using a novel two-stage hybrid model. Computers and Electronics in Agriculture, 104, 71-83. https://doi.org/10.1016/j.compag.2014.03.011
  56. Li, C., Liu, J., Bai, W., Wu, S., Zheng, P., Zhang, J., Pan, Z., & Zhai, J. (2022). Superior energy storage performance in (Bi5Na0.5) TiO3-based lead-free relaxor ferroelectrics for dielectric capacitor application via multiscale optimization design. Journal of Materials Chemistry A, 10(17), 9535-9546. https://doi.org/10.1039/D2TA00380E
  57. Liebhold, A. M., Brockerhoff, E. G., Garrett, L. J., Parke, J. L., & Britton, K. O. (2012). Live plant imports: the major pathway for forest insect and pathogen invasions of the US. Frontiers in Ecology and the Environment, 10(3), 135-143. https://doi.org/10.1890/110198
  58. Lima, M. C. F., de Almeida Leandro, M. E. D., Valero, C., Coronel, L. C. P., & Bazzo, C. O. G. (2020). Automatic detection and monitoring of insect pests—A review. Agriculture, 10(5), 161. https://doi.org/10.3390/agriculture10050161
  59. Lin, T. L., Chang, H. Y., & Chen, K. H. (2020). The pest and disease identification in the growth of sweet peppers using faster R-CNN and mask R-CNN. Journal of Internet Technology, 21(2), 605-614. https://doi.org/3966/160792642020032102027
  60. López-Morales, V., López-Ortega, O., Ramos-Fernandez, J., & Munoz, L. (2008). JAPIEST: An integral intelligent system for the diagnosis and control of tomatoes diseases and pests in hydroponic greenhouses. Expert Systems with Applications, 35(4), 1506-1512. https://doi.org/10.1016/j.eswa.2007.08.098
  61. Machekano, H., Mutamiswa, R., Mvumi, B. M., Nyabako, T., Shaw, S., & Nyamukondiwa, C. (2019). Disentangling factors limiting diamondback moth, Plutella xylostella (L.), spatio‐temporal population abundance: A tool for pest forecasting. Journal of Applied Entomology, 143(6), 670-682. https://doi.org/10.1111/jen.12636
  62. Maduranga, M., & Abeysekera, R. (2020). Machine learning applications in IoT based agriculture and smart farming: A review. International Journal of Engineering Applied Sciences and Technology, 4(12), 24-27. https://doi.org/10.33564/IJEAST.2020.v04i12.004
  63. Mahapatra, S. K., & Dash, A. (2020). ARIMA Model for Forecasting of Black Gram Productivity in Odisha. Asiatic Society for Social Science Research (ASSSR), 131-136. https://www.asssr.in/index.php/home/article/view/105
  64. Malicdem, A. R., & Fernandez, P. L. (2015). Rice blast disease forecasting for northern Philippines. WSEAS Transactions on Information Science and Applications, 12, 120-129. https://wseas.com/journals/isa/2015/a225709-430.pdf
  65. Mehta, S., Agrawal, R., & Kumar, A. (2005). Forewarning crop pests and diseases: IASRI methodologies. IASRI Publication, New Delhi.
  66. Mehta, S., Agrawal, R., Shukla, R., & Sharma, S. (2001). A Statistical model for prediction of mangofruitfly outbreak. National Seminar on Agrometeorological Research for Sustainable Agricultural Production.
  67. Mrisho, L. M., Mbilinyi, N. A., Ndalahwa, M., Ramcharan, A. M., Kehs, A. K., McCloskey, P. C., Murithi, H., Hughes, D. P., & Legg, J. P. (2020). Accuracy of a smartphone-based object detection model, PlantVillage Nuru, in identifying the foliar symptoms of the viral diseases of cassava–CMD and CBSD. Frontiers in Plant Science, 11, 590889. https://doi.org/10.3389/fpls.2020.590889
  68. Musolin, D. L. (2007). Insects in a warmer world: ecological, physiological and life‐history responses of true bugs (Heteroptera) to climate change. Global Change Biology, 13(8), 1565-1585. https://doi.org/10.1111/j.1365-2486.2007.01395.x
  69. Nagar, H., & Sharma, R. S. (2020). A comprehensive survey on pest detection techniques using image processing. 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp. 43-48. https://doi.org/1109/ICICCS48265.2020.9120889
  70. Ngugi, L. C., Abelwahab, M., & Abo-Zahhad, M. (2021). Recent advances in image processing techniques for automated leaf pest and disease recognition–A review. Information Processing in Agriculture, 8(1), 27-51. https://doi.org/10.1016/j.inpa.2020.04.004
  71. Nihar, F., Khanom, N. N., Hassan, S. S., & Das, A. K. (2021). Plant disease detection through the implementation of diversified and modified neural network algorithms. Journal of Engineering Advancements, 2(01), 48-57. https://doi.org/10.38032/jea.2021.01.007
  72. Pak, M., & Kim, S. (2017). A review of deep learning in image recognition. 4th international conference on computer applications and information processing technology (CAIPT), Kuta Bali, Indonesia, pp. 1-3. https://doi.org/10.1109/CAIPT.2017.8320684
  73. Pandit, P., Krishnamurthy, K., & Bakshi, B. (2022). Prediction of crop yield and pest-disease infestation. In AI, Edge and IoT-based Smart Agriculture, pp. 375-393. https://doi.org/10.1016/B978-0-12-823694-9.00021-9
  74. Patel, D., & Bhatt, N. (2021). Improved accuracy of pest detection using augmentation approach with Faster R-CNN. IOP Conference Series: Materials Science and Engineering, 1042, 012020. https://doi.org/10.1088/1757-899X/1042/1/012020
  75. Pawara, S., Nawale, D., Patil, K., & Mahajan, R. (2018). Early detection of pomegranate disease using machine learning and internet of things. 3rd International Conference for Convergence in Technology (I2CT). Pune, India, pp. 1-4. https://doi.org/10.1109/I2CT.2018.8529583
  76. Paymode, A. S., Magar, S. P., & Malode, V. B. (2021). Tomato leaf disease detection and classification using convolution neural network. International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, pp. 564-570. https://doi.org/1109/ESCI50559.2021.9397001
  77. Petrellis, N. (2017). A smart phone image processing application for plant disease diagnosis. 6th international conference on modern circuits and systems technologies (MOCAST), Thessaloniki, Greece, pp. 1-4. https://doi.org/10.1109/MOCAST.2017.7937683
  78. Prajwala, T. M., Pranathi, A., SaiAshritha, K., Chittaragi, N. B., & Koolagudi, S. G. (2018). Tomato leaf disease detection using convolutional neural networks. Eleventh international conference on contemporary computing (IC3), Noida, India, pp. 1-5. https://doi.org/10.1109/IC3.2018.8530532
  79. Prashar, N., & Sangal, A. (2022). Plant disease detection using deep learning (convolutional neural networks). Second International Conference on Image Processing and Capsule Networks: ICIPCN, 2021, 2. Lecture Notes in Networks and Systems, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-84760-9_54
  80. Priya, R., & Ramesh, D. (2020). ML based sustainable precision agriculture: A future generation perspective. Sustainable Computing: Informatics and Systems, 28, 100439. https://doi.org/10.1016/j.suscom.2020.100439
  81. Qi, F., Wang, Y., Tang, Z., & Chen, S. (2023). Real-time and effective detection of agricultural pest using an improved YOLOv5 network. Journal of Real-Time Image Processing, 20(2), 33. https://doi.org/10.1007/s11554-023-01264-0
  82. Qiang, Z., Shihao, S., Yulin, W., Mengying, L., Hongkai, L., & Qiang, N. (2020). Research on load distribution control technology for parallel operation of power source with different rated capacity. 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Nanjing, China, pp. 1-5. https://doi.org/10.1109/APPEEC48164.2020.9220582
  83. Rajni, J., Sonajharia, M., & Ramasubramanian, V. (2009). Machine learning for forewarning crop diseases. Journal of the Indian Society of Agricultural Statistics, 63(1), 97-107.
  84. Rawat, N., Karnatak, A. K., & Srivastava, R. M. (2020). Population dynamics of okra shoot and fruit borer (Earias vittella) of okra in agro-climatic condition of Pantnagar. International Journal of Chemical Studies, 8(1), 2131-2134. https://doi.org/10.22271/chemi.2020.v8.i1af.8584
  85. Rehman, T. U., Mahmud, M. S., Chang, Y. K., Jin, J., & Shin, J. (2019). Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and Electronics in Agriculture, 156, 585-605. https://doi.org/10.1016/j.compag.2018.12.006
  86. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28. https://doi.org/10.1109/TPAMI.2016.2577031
  87. Roques, A., Auger-Rozenberg, M.-A., Blackburn, T. M., Garnas, J., Pyšek, P., Rabitsch, W., Richardson, D. M., Wingfield, M. J., Liebhold, A. M., & Duncan, R. P. (2016). Temporal and interspecific variation in rates of spread for insect species invading Europe during the last 200 years. Biological Invasions, 18, 907-920. https://doi.org/10.1007/s10530-016-1080-y
  88. Saeed, H., Ehetisham-ul-Haq, M., Atiq, M., Kamran, M., Idrees, M., Ali, S., Burhan, M., Mohsan, M., Iqbal, M., & Nazir, S. (2018). Prediction of cotton leaf curl virus disease and its management through resistant germplasm and bio-products. Archives of Phytopathology and Plant Protection, 51(3-4), 170-186. https://doi.org/10.1080/03235408.2018.1443602
  89. Santini, A., Ghelardini, L., De Pace, C., Desprez‐Loustau, M.-L., Capretti, P., Chandelier, A., Cech, T., Chira, D., Diamandis, S., & Gaitniekis, T. (2013). Biogeographical patterns and determinants of invasion by forest pathogens in Europe. New Phytologist, 197(1), 238-250. https://doi.org/10.1111/j.1469-8137.2012.04364.x
  90. Santini, A., Liebhold, A., Migliorini, D., & Woodward, S. (2018). Tracing the role of human civilization in the globalization of plant pathogens. The ISME journal, 12(3), 647-652. https://doi.org/10.1038/s41396-017-0013-9
  91. Sarukhán, J., Whyte, A., Hassan, R., Scholes, R., Ash, N., Carpenter, S., Pingali, P., Bennett, E., Zurek, M., & Chopra, K. (2005). Millenium ecosystem assessment: Ecosystems and human well-being. http://www.millenniumassessment.org/en/products.aspx
  92. Setiyowati, S., Nugraha, R. F., & Mukhaiyar, U. (2015). Non-stationary time series modeling on caterpillars pest of palm oil for early warning system. AIP Conference Proceedings, 1692, 020011. https://doi.org/10.1063/1.4936439
  93. Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1-48. https://doi.org/10.1186/s40537-019-0197-0
  94. Siddiqua, A., Kabir, M. A., Ferdous, T., Ali, I. B., & Weston, L. A. (2022). Evaluating plant disease detection mobile applications: Quality and limitations. Agronomy, 12(8), 1869. https://doi.org/10.3390/agronomy12081869
  95. Singh, B., Singh, R., Tiwari, P., & Kumar, N. (2019). Climate based factor analysis and epidemiology prediction for potato late blight using machine learning approaches. Women Institute of Technology Conference on Electrical and Computer Engineering (WITCON ECE), Dehradun, India, pp. 113-122. https://doi.org/10.1109/WITCONECE48374.2019.9092914
  96. Singh, J., Das, D., Vennila, S., & Rawat, K. (2018). Weather based forewarning of pest and disease: An important adaptation strategies under the impact of climate change scenario: A brief review. International Journal of Advanced Multidisciplinary Scientific Research (IJAMSR), 1, 6-21. https://doi.org/10.31426/ijamsr.2018.1.10.1012
  97. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2016/3289801
  98. Srivastava, R., Gupta, C., Gupta, H., Singh, N., & Kumar, N. (2015). Mathematical Modelling of Crop Yield Forecasting and Forewarning of Pests/Diseases. In International Conference of Advance Research and Innovation,(ICARI), pp 417-19.
  99. Sudduth, K. A., Woodward-Greene, M. J., Penning, B. W., Locke, M. A., Rivers, A. R., & Veum, K. S. (2020). AI down on the farm. IT Professional, 22(3), 22-26. https://doi.org/10.1109/MITP.2020.2986104
  100. Sykes, J. R., Denby, K. J., & Franks, D. W. (2023). Computer vision for plant pathology: A review with examples from cocoa agriculture. Applications in Plant Sciences, e11559. https://doi.org/10.1002/aps3.11559
  101. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, USA, pp. 1-9. https://doi.org/10.1109/CVPR.2015.7298594
  102. Trebicki, P., & Finlay, K. (2019). Pests and diseases under climate change; its threat to food security. Food Security and Climate Change, 229-249. https://doi.org/10.1002/9781119180661.ch11
  103. Veeragandham, S., & Santhi, H. (2020). A review on the role of machine learning in agriculture. Scalable Computing: Practice and Experience, 21(4), 583-589. https://doi.org/10.12694/scpe.v21i4.1699
  104. Verma, S., & Zhang, Z. L. (2018). Graph capsule convolutional neural networks. arXiv preprint arXiv:1805.08090. https://doi.org/10.48550/arXiv.1805.08090
  105. Vishwa Dhar, V. D., Singh, S., Kumar, M., Agrawal, R., & Amrender Kumar, A. K. (2007). Prediction of pod-borer (Helicoverpa armigera) infestation in short-duration pigeonpea (Cajanus cajan) in central Uttar Pradesh. Indian Journal of Agricultural Sciences, 77(10), 701-704.
  106. Wang, X., Yan, Y., & Li, Z. (2022). Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey. Agriculture, 12(9), 1350. https://doi.org/10.3390/agriculture12091350
  107. Wiesner-Hanks, T., Stewart, E. L., Kaczmar, N., DeChant, C., Wu, H., Nelson, R. J., Lipson, H., & Gore, M. A. (2018). Image set for deep learning: field images of maize annotated with disease symptoms. BMC Research Notes, 11(1), 1-3. https://doi.org/10.1186/s13104-018-3548-6
  108. Yang, Z., Xu, C., Wang, M., Zhao, H., Zheng, Y., Huang, H., Vuguziga, F., & Umutoni, M. (2019). Enhancing the thermotolerance of tomato seedlings by heat shock treatment. Photosynthetica, 57(4). https://doi.org/10.32615/ps.2019.127
  109. Zhao, Y., Liu, L., Xie, C., Wang, R., Wang, F., Bu, Y., & Zhang, S. (2020). An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild. Applied Soft Computing, 89, 106128. https://doi.org/10.1016/j.asoc.2020.106128
  110. Zhaoxin, G., Han, L., Zhijiang, Z., & Libo, P. (2022). Design a robot system for tomato picking based on YOLO v5. IFAC-PapersOnLine, 55(3), 166-171. https://doi.org/10.1016/j.ifacol.2022.05.029
  111. Zheng, Y. Y., Kong, J. L., Jin, X. B., Wang, X. Y., Su, T. L., & Zuo, M. (2019). CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture. Sensors, 19(5), 1058. https://doi.org/10.3390/s19051058
آمار
تعداد مشاهده مقاله: 3
تعداد دریافت فایل اصل مقاله: 1


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب