Open Access
Issue
E3S Web Conf.
Volume 412, 2023
International Conference on Innovation in Modern Applied Science, Environment, Energy and Earth Studies (ICIES’11 2023)
Article Number 01083
Number of page(s) 9
DOI https://doi.org/10.1051/e3sconf/202341201083
Published online 17 August 2023
  1. Kitzes, J.; Wackernagel, M.; Loh, J.; Peller, A.; Goldfinger, S.; Cheng, D.; Tea, K. Shrink and share humanity’s present and future ecological footprint. Philos. Trans. the Roy. Soc. Lond. B Biol. Sci. 2008, 363, 467–475. [CrossRef] [PubMed] [Google Scholar]
  2. Kamilaris, A.; Prenafeta-Boldú, F.X. Deep learning in agriculture: A survey. Comput. Electron. Agric. 2018, 147, 70–90. [CrossRef] [Google Scholar]
  3. Liu, Q.; Yan, Q.; Tian, J.; Yuan, K. Key technologies and applications in intelligent agriculture. J. Phys. Conf. Ser. 2021, 1757, 012059. [CrossRef] [Google Scholar]
  4. Strange, R.N.; Scott, P.R. Plant disease: A threat to global food security. Annu. Rev. Phytopathol. 2005, 43, 83–116. [CrossRef] [PubMed] [Google Scholar]
  5. A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z. Med. Phys., vol. 29, no. 2, pp. 102–127, 2019, doi: 10.1016/j.zemedi.2018.11.002. [CrossRef] [Google Scholar]
  6. Guillén, MA; Llanes, A.; Imbernon, B.; Martínez-España, R.; Bueno-Crespo, A.; Cano, JC; Cecilia, JM Évaluation des performances des plates-formes informatiques de pointe pour la prédiction des basses températures en agriculture à l’aide de l’apprentissage en profondeur. J. Supercalculateur. 2021, 77, 818–840. [Google Scholar]
  7. Azimi, S.; Wadhawan, R.; Gandhi, TK Surveillance intelligente du stress induit par le manque d’eau chez les plantes à l’aide de l’apprentissage en profondeur. IEEE Trans. Instrument. Mes. 2021, 70, 5017113. [Google Scholar]
  8. Garibaldi-Marquez, F.; Flores, G.; Mercado-Ravell, DA; Ramírez-Pedraza, A.; Valentín-Coronado, LM Classification des mauvaises herbes à partir d’images de champs de maïs naturels multi-plantes basée sur l’apprentissage superficiel et profond. Capteurs 2022, 22, 3021. [Google Scholar]
  9. Mhango, JK; Harris, EW; Vert, R.; Monaghan, JM Cartographie de la variation de la densité des plants de pommes de terre à l’aide d’images aériennes et de techniques d’apprentissage en profondeur pour l’agriculture de précision. Remote Sens. 2021, 13, 2705. [CrossRef] [Google Scholar]
  10. Mohapatra, D.; Choudhury, B.; Sabat, B. Un système automatisé pour la gradation des fruits et la localisation des aberrations à l’aide de l’apprentissage en profondeur. Dans Actes de la 7e Conférence internationale 2021 sur les systèmes informatiques et de communication avancés (ICACCS), Coimbatore, Inde, 19-20 mars 2021. [Google Scholar]
  11. de Camargo, T.; Schirrmann, M.; Landwehr, N.; Dammer, KH; Pflanz, M. Modèle d’apprentissage en profondeur optimisé comme base pour la cartographie rapide par UAV des espèces de mauvaises herbes dans les cultures de blé d’hiver. Remote Sens. 2021, 13, 1704. [CrossRef] [Google Scholar]
  12. Kibriya, H.; Abdallah, I. ; Nasrullah, A. Identification et classification des maladies des plantes à l’aide d’un réseau de neurones convolutifs et de SVM. Dans Actes de la Conférence internationale 2021 sur les frontières des technologies de l’information (FIT), Islamabad, Pakistan, 13-14 décembre 2021 ; p. 264–268. [Google Scholar]
  13. Zhou, C.; Zhou, S.; Xing, J.; Song, J. Identification des maladies foliaires de la tomate par un réseau dense résiduel profond restructuré. Accès IEEE 2021, 9, 28822–28831. [CrossRef] [Google Scholar]
  14. Trivedi, NK; Gautam, V. ; Anand, A.; Aljahdali, HM; Villar, SG; Anand, D.; Goyal, N.; Kadry, S. Détection précoce et classification de la maladie des feuilles de tomate à l’aide d’un réseau de neurones profonds à haute performance. Capteurs 2021, 21, 7987. [Google Scholar]
  15. Vypirailenko, D.; Kiseleva, E.; Shadrin, D.; Pukalchik, M. Deep learning techniques for enhancement of weeds growth classification. In Proceedings of the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Glasgow, UK, 17–20 May 2021. [Google Scholar]
  16. M. Tian, H. Guo, H. Chen, Q. Wang, C. Long, and Y. Ma, “Automated pig counting using deep learning,” Computers and Electronics in Agriculture, vol. 163, article no. 104840, 2019. [CrossRef] [Google Scholar]
  17. Y. Tian, G. Yang, Z. Wang, H. Wang, E. Li, and Z. Liang, “Apple detection during different growth stages in orchards using the improved YOLO-V3 model,” Computers and Electronics in Agriculture, vol. 157, pp. 417-426, 2019. [CrossRef] [Google Scholar]
  18. Y. Qiao, M. Truman, and S. Sukkarieh, “Cattle segmentation and contour extraction based on Mask R-CNN for precision livestock farming,” Computers and Electronics in Agriculture, vol. 165, article no. 104958, 2019. [CrossRef] [Google Scholar]
  19. S. A. Jwade, A. Guzzomi, and A. Mian, “On farm automatic sheep breed classification using deep learning,” Computers and Electronics in Agriculture, vol. 167, Article 105055, 2019. [Google Scholar]
  20. M. K. Saggi and S. Jain, “Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning,” Computers and Electronics in Agriculture, vol. 156, pp. 387-398, 2019. [CrossRef] [Google Scholar]
  21. Fawakherji, M.; Youssef, A.; Bloisi, D.D.; Pretto, A.; Nardi, D. Crop and Weeds Classification for Precision Agriculture using Context-Independent Pixel-Wise Segmentation. In Proceedings of the Third IEEE International Conference on Robotic Computing (IRC), Naples, Italy, 25–27 February 2019. [Google Scholar]
  22. Häni, N.; Roy, P.; Isler, V. A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards. J. Field Robot. 2019, 37, 181–340. [Google Scholar]
  23. Grimm, J.; Herzog, K.; Rist, F.; Kicherer, A.; Töpfer, R.; Steinhage, V. An adaptable approach to automated visual detection of plant organs with applications in grapevine breeding. Biosyst. Eng. 2019, 183, 170–183. [CrossRef] [Google Scholar]
  24. Yu, J.; Sharpe, S.M.; Schumann, A.W.; Boyd, N.S. Deep learning for image-based weed detection in turfgrass. Eur. J. Agron. 2019, 104, 78–84. [Google Scholar]
  25. Yang, Q.; Shi, L.; Han, J.; Zha, Y.; Zhu, P. Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images. Field Crop. Res. 2019, 235, 142–153. [Google Scholar]
  26. Razfar, N., True, J., Bassiouny, R., Venkatesh, V., & Kashef, R. (2022). Weed detection in soybean crops using custom lightweight deep learning models. Journal of Agriculture and Food Research, 8, Article 100308. [CrossRef] [Google Scholar]
  27. Mostafa, A. M., Kumar, S. A., Meraj, T., Rauf, H. T., Alnuaim, A. A., & Alkhayyal, M. A. (2022). Guava Disease Detection Using Deep Convolutional Neural Networks : A Case Study of Guava Plants. Applied Sciences, 12(1), 239. [Google Scholar]
  28. Tufail, M., Iqbal, J., Tiwana, M. I., Alam, M. S., Khan, Z. A., & Khan, M. T. (2021). Identification of tobacco crop based on machine learning for a precision agricultural sprayer. IEEE access. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. [Google Scholar]
  29. L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8. [Google Scholar]
  30. L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8. [Google Scholar]
  31. M. Altalak, M. A. Uddin, A. Alajmi, and A. Rizg, “Smart Agriculture Applications Using Deep Learning Technologies: A Survey,” Appl. Sci., vol. 12, no. 12, 2022, doi: 10.3390/app12125919. [Google Scholar]
  32. L. Lipper et al., “Climate-smart agriculture for food security,” Nat. Clim. Chang., vol. 4, no. 12, pp. 1068–1072, 2014, doi: 10.1038/nclimate2437. [CrossRef] [Google Scholar]
  33. Dargan, S.; Kumar, M.; Ayyagari, M.R.; Kumar, G. A survey of deep learning and its applications: A new paradigm to machine learning. Arch. Comput. Methods Eng. 2020, 27, 1071–1092. [CrossRef] [Google Scholar]
  34. L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8. [Google Scholar]
  35. C. Ren, D. K. Kim, and D. Jeong, “A Survey of Deep Learning in Agriculture: Techniques and Their Applications,” J. Inf. Process. Syst., vol. 16, no. 5, pp. 1015–1033, 2020, doi: 10.3745/JIPS.04.0187. [Google Scholar]
  36. “A Search Interval Limitation Technique for Improved Search Performance of CNN.pdf.” [Google Scholar]
  37. T. van Klompenburg, A. Kassahun, and C. Catal, “Crop yield prediction using machine learning: A systematic literature review,” Comput. Electron. Agric., vol. 177, no. July, p. 105709, 2020, doi: 10.1016/j.compag.2020.105709. [CrossRef] [Google Scholar]
  38. T. van Klompenburg, A. Kassahun, and C. Catal, “Crop yield prediction using machine learning: A systematic literature review,” Comput. Electron. Agric., vol. 177, no. July, p. 105709, 2020, doi: 10.1016/j.compag.2020.105709. [CrossRef] [Google Scholar]
  39. J. Padarian, B. Minasny, and A. B. McBratney, “Using deep learning to predict soil properties from regional spectral data,” Geoderma Reg., vol. 16, p. e00198, 2019, doi: 10.1016/j.geodrs.2018.e00198. [CrossRef] [Google Scholar]
  40. Kassou, M. et al., Digital transformation in flow planning: The case of container terminals at a smart port, Journal of Theoretical and Applied Information Technologythis link is disabled, 2021, 99(9), pp. 1966–1976 [Google Scholar]
  41. A. Kamilaris and F. X. Prenafeta-Boldú, “A review of the use of convolutional neural networks in agriculture,” J. Agric. Sci., vol. 156, no. 3, pp. 312–322, 2018, doi: 10.1017/S0021859618000436. [CrossRef] [Google Scholar]
  42. D. Musleh, M. Alotaibi, F. Alhaidari, A. Rahman, and R. M. Mohammad, “Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT,” J. Sens. Actuator Networks, vol. 12, no. 2, 2023, doi: 10.3390/jsan12020029. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.