SavedAdd your journal

Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 14 Sayı: 1

Harnessing deep learning for multi-class weed species identification in agriculture

Tarımda çok sınıflı yabancı ot türlerinin tanımlanması için derin öğrenmenin kullanımı

Ebru Ergün(RECEP TAYYİP ERDOĞAN ÜNİVERSİTESİ)0000-0002-5371-7238
Pages
251–262
Publication Date
2025-01-15
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The experimental results demonstrated the ability of these models to identify multiple weed species with high accuracy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Effective identification of weed species is critical for efficient agricultural management, enabling targeted eradication and optimized farming practices.
📊
Key Findings & Data:In this study, ResNet, VggNet and DenseNet were used to evaluate the performance of deep learning models in accurately classifying different weed species.
💡
Conclusion & Impact:The experimental results demonstrated the ability of these models to identify multiple weed species with high accuracy.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Tarımsal yönetim · Sınıflandırma · Derin öğrenme

Abstract

Effective identification of weed species is critical for efficient agricultural management, enabling targeted eradication and optimized farming practices. In this study, ResNet, VggNet and DenseNet were used to evaluate the performance of deep learning models in accurately classifying different weed species. The dataset consisted of high-resolution images of different weed species taken under different environmental conditions. The experimental results demonstrated the ability of these models to identify multiple weed species with high accuracy. Evaluation metrics, accuracy, precision, recall and confusion matrices, validated the effectiveness of the models in discriminating between species. Of the convolutional neural network architectures tested, VggNet showed the highest classification accuracy of 99.21%. The results underscored the potential of deep learning-based classification systems in advancing scalable and efficient weed species identification and management for agricultural applications.

Özet

Yabancı ot türlerinin etkili bir şekilde tespiti, verimli tarımsal yönetim için kritik öneme sahiptir ve hedefe yönelik yok etme ve optimize edilmiş tarım uygulamalarını mümkün kılar. Bu çalışmada, ResNet, VggNet ve DenseNet derin öğrenme modellerinin farklı yabancı ot türlerini doğru bir şekilde sınıflandırmadaki performanslarını değerlendirmek için kullanılmıştır. Veri seti, farklı çevresel koşullarda çekilmiş çeşitli yabancı ot türlerine ait yüksek çözünürlüklü görüntülerden oluşmaktadır. Deneysel sonuçlar, bu modellerin birden fazla yabancı ot türünü yüksek doğrulukla tespit etme yeteneğini göstermiştir. Değerlendirme metrikleri, doğruluk, hassasiyet, hatırlama ve hata matrisi, modellerin türler arasındaki ayrımı yapmadaki etkinliğini doğrulamıştır. Test edilen evrişimli sinir ağı mimarileri arasında, VggNet %99.21’lik en yüksek sınıflandırma doğruluğunu sergilemiştir. Sonuçlar, derin öğrenmeye dayalı sınıflandırma sistemlerinin, tarımsal uygulamalar için ölçeklenebilir ve verimli yabancı ot türü tespiti ve yönetimi konusunda büyük bir potansiyele sahip olduğunu vurgulamıştır.

🔖

Cite This Article

Ergün, E. (2025). Harnessing deep learning for multi-class weed species identification in agriculture. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 14 Sayı: 1, 251–262. https://doi.org/10.28948/ngumuh.1495040

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (33)

  1. F. Ahmed, H. A. Al-Mamun, A. H. Bari, E. Hossain, & P. Kwan, Classification of crops and weeds from digital images: A support vector machine approach. Crop Protection, 40, 98-104, 2012. https://doi.org/10.1016/j.cropro.2012.04.024.
  2. T. Luo, J. Zhao, Y. Gu, S. Zhang, X. Qiao, W. Tian & Y. Han, Classification of weed seeds based on visual images and deep learning. Information Processing in Agriculture, 10(1), 40-51, 2023. https://doi.org/10.1016/j.inpa.2021.10.002.
  3. R. Bongiovanni, and J. Lowenberg-DeBoer, Precision agriculture and sustainability. Precision agriculture, 5, 359-387, 2004. https://doi.org/10.1023/B:PRAG. 0000040806.39604.aa.
  4. A. S. Baghel, A. Bhardwaj and W. Ibrahim, Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/9408535.
  5. R. Gerhards, D. Andujar Sanchez, P. Hamouz, G. G. Peteinatos, S. Christensen and C. Fernandez‐Quintanilla, Advances in site‐specific weed management in agriculture—A review. Weed Research, 62(2), 123-133, 2022. https://doi.org/10.1111/wre.12526.
  6. A. Monteiro, and S. Santos, Sustainable approach to weed management: The role of precision weed management. Agronomy, 12(1), 118, 2022. https://doi.org/10.3390/agronomy12010118.
  7. M. Z. Alom, T. M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. S. Nasrin and V. K. Asari, The history began from alexnet: A comprehensive survey on deep learning approaches. Computer Vision and Pattern Recognition, 2018. https://doi.org/10.48550/arXiv.1803.01164.
  8. S. Khan, M. Tufail, M. T. Khan, Z. A. Khan and S. Anwar, Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer. Precision Agriculture, 22(6), 1711-1727, 2021. https://doi.org/10.1007/s11119-021-09808-9.
  9. B. Espejo-Garcia, N. Mylonas, L. Athanasakos, S. Fountas and I. Vasilakoglou, Towards weeds identification assistance through transfer learning. Computers and Electronics in Agriculture, 171, 105306, 2020. https://doi.org/10.1016/j.compag.2020.105306.
  10. K. Hu, G. Coleman, S. Zeng, Z. Wang and M. Walsh, Graph weeds net: A graph-based deep learning method for weed recognition. Computers and electronics in agriculture, 174, 105520, 2020. https://doi.org/10.1016/j.compag.2020.105520.
  11. J. Tang, D. Wang, Z. Zhang, L. He, J. Xin and Y. Xu, Weed identification based on K-means feature learning combined with convolutional neural network. Computers and electronics in agriculture, 135, 63-70, 2017. https://doi.org/10.1016/j.compag.2017.01.001.
  12. V. H. Trong, Y. Gwang-hyun, D. T. Vu and K. Jin-young, Late fusion of multimodal deep neural networks for weeds classification. Computers and Electronics in Agriculture, 175, 105506, 2020. https://doi.org/10.1016/j.compag.2020.105506.
  13. A. Olsen, D. A. Konovalov, B. Philippa, P. Ridd, J. C. Wood, J. Johns and R. D. White, DeepWeeds: A multiclass weed species image dataset for deep learning. Scientific reports, 9(1), 2058, 2019. https://doi.org/10.1038/s41598-018-38343-3.
  14. X. Jin, M. Bagavathiannan, P. E. McCullough, Y. Chen and J. Yu, A deep learning‐based method for classification, detection, and localization of weeds in turfgrass. Pest Management Science, 78(11), 4809-4821, 2022. https://doi.org/10.1002/ps.7102.
  15. N. Rai, M. V. Mahecha, A. Christensen, J. Quanbeck, Y. Zhang, K. Howatt and X. Sun, Multi-format open-source weed image dataset for real-time weed identification in precision agriculture. Data in Brief, 51, 109691, 2023. https://doi.org/10.1016/j.dib.2023. 109691.
  16. TzutalinLabelImg v1.8.1 (Version 1.8.1), https://github.com/HumanSignal/labelImg.
  17. T. Tao, & X. Wei, A hybrid CNN–SVM classifier for weed recognition in winter rape field. Plant Methods, 18(1), 29, 2022. https://doi.org/10.1186/s13007-022-00869-z.
  18. M. Manikandakumar & P. Karthikeyan, Weed classification using particle swarm optimization and deep learning models. Comput. Syst. Sci. Eng., 44(1), 913-927, 2023. https://doi.org/10.32604/csse.2023.025434.
  19. W. Rawat and Z. Wang, Deep convolutional neural networks for image classification: A comprehensive review. Neural computation, 29(9), 2352-2449, 2017. https://doi.org/10.1162/neco_a_00990.
  20. J. C. Chen, T. L. Chen, H. L. Wang and P. C. Chang, Underwater abnormal classification system based on deep learning: A case study on aquaculture fish farm in Taiwan. Aquacultural Engineering, 99, 102290, 2022. https://doi.org/10.1016/j.aquaeng.2022.102290.
  21. A. Paul, R. Machavaram, D. Kumar & H. Nagar, Smart solutions for capsicum Harvesting: Unleashing the power of YOLO for Detection, Segmentation, growth stage Classification, Counting, and real-time mobile identification. Computers and Electronics inAgriculture, 219, 108832, 2024. https://doi.org/10.1016/j.compag.2024.108832.
  22. A. Sagingalieva, M. Kordzanganeh, A. Kurkin, A. Melnikov, D. Kuhmistrov, M. Perelshtein and D. V. Dollen, Hybrid quantum ResNet for car classification and its hyperparameter optimization. Quantum Machine Intelligence, 5(2), 38, 2023. https://doi.org/10.1007/s42484-023-00123-2.
  23. A. Pandey and K Jain, An intelligent system for crop identification and classification from UAV images using conjugated dense convolutional neural network. Computers and Electronics in Agriculture, 192, 106543, 2022. https://doi.org/10.1016/j.compag.2021.106543.
  24. J. Wei, Y. Ibrahim, S. Qian, H. Wang, G. Liu, Q. Yu and J. Shi, Analyzing the impact of soft errors in VGG networks implemented on GPUs. Microelectronics Reliability, 110, 113648, 2020. https://doi.org/10.1016/j.microrel.2020.113648.
  25. A. S. Paymode and V. B. Malode, Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG. Artificial Intelligence in Agriculture, 6, 23-33, 2022. https://doi.org/10.1016/j.aiia.2021.12.002.
  26. X. Zhang, Y. Qiao, Meng, F., Fan, C., & Zhang, M. (2018). Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access, 6, https://doi.org/10.1109/ACCESS.2018.2844405.
  27. Lu, T., Han, B., L. Chen, F. Yu and C. Xue, A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning. Scientific Reports, 11(1), 15824, 2021. https://doi.org/10.1038/s41598-021-95218-w.
  28. L. Shan and W. Wang, DenseNet-based land cover classification network with deep fusion. IEEE Geoscience and Remote Sensing Letters, 19, 1-5, 2021. https://doi.org/10.1109/LGRS.2020.3042199.
  29. K. Zhang, Y. Guo, X. Wang, J. Yuan and Q. Ding, Multiple feature reweight densenet for image classification. IEEE Access, 7, 9872-9880, 2019. https://doi.org/10.1109/ACCESS.2018.2890127.
  30. E. Ergün, Deep learning based multiclass classification for citrus anomaly detection in agriculture. Signal, Image and Video Processing, 18(11), 8077-8088, 2024. https://doi.org/10.1007/s11760-024-03452-2.
  31. E. Ergün, Artificial intelligence approaches for accurate assessment of insulator cleanliness in high-voltage electrical systems. Electrical Engineering, 1-14, 2024. https://doi.org/10.1007/s00202-024-02691-3.
  32. M. Heydarian, T. E. Doyle and R. Samavi, MLCM: Multi-label confusion matrix. IEEE Access, 10, 19083-19095, 2022. https://doi.org/10.1109/ACCESS.2022.3151048.
  33. M. L. Zhang and Z. H. Zhou, A review on multi-label learning algorithms. IEEE transactions on knowledge and data engineering, 26(8), 1819-1837, 2013. https://doi.org/10.1109/TKDE.2013.39.