Smart Weed-Crop Classification in Winter Wheat Fields: A Deep Learning Approach for Sustainable Agriculture

Articles in Press

Document Type : Research Article- En

Authors

S. I. Saedi ¹

H. Makarian ²

¹ Department of Water and Soil, Faculty of Agriculture, Shahrood University of Technology, Shahrood, Iran

² Department of Agronomy and Plant Breeding, Faculty of Agriculture, Shahrood University of Technology, Shahrood, Iran

https://doi.org/10.22067/jam.2026.97533.1467

Abstract

Improving wheat productivity is essential for maintaining food security in the coming years. A major challenge to wheat production is the presence of weeds, which can greatly reduce its yield. In this context, precision weed management based on weed-crop classification can be a key factor for achieving sustainable wheat production. To accomplish this goal, in the present study, we utilised colour images and deep learning techniques to distinguish winter wheat from four prevalent weeds, creating five distinct classes. Four deep learning networks–Xception, EfficientNetB0, VGG19, and InceptionResNetV2–were evaluated to serve as backbone networks to fulfil the classification requirements. These networks were pre-trained on ImageNet using transfer learning, then enhanced with additional layers to improve performance on our dataset. The improved InceptionResNetV2 model demonstrated the highest performance among the four models, achieving an accuracy of 98.17% and a loss of 3.19% on the test data. Nevertheless, all models demonstrated excellent performance in distinguishing plant classes, achieving weighted average F1-scores of 97%, 86%, 94%, and 98% for the improved models based on Xception, EfficientNetB0, VGG19, and InceptionResNetV2, respectively. Additionally, we analysed fifteen scenarios of weed presence in winter wheat fields, focusing on various weed types studied, to propose effective weed management strategies utilising the four models. The research findings provide a foundation for developing mobile apps or robotic weeders that facilitate site-specific weed management. This approach aims to reduce herbicide use and environmental impact while also improving wheat yield and quality.

Keywords

Deep learning

RGB image

Weed recognition

Winter wheat

Subjects

• Digital, Smart, and Precision Agriculture

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Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

References

1. Batchuluun, G., Nam, S. H., & Park, K. R. (2022). Deep learning-based plant-image classification using a small training dataset. Mathematics, 10, 3091. https://doi.org/10.3390/math10173091
2. Chollet, F. (2017). Deep learning with Python(1st ed.). Shelter Island, NY: Manning Publications Co.
3. Fan, X., Chai, X., Zhou, J., & Sun, T. (2023). Deep learning-based weed detection and target spraying robot system at seedling stage of cotton field. Computers and Electronics in Agriculture, 214, 108317. https://doi.org/10.1016/j.compag.2023.108317
4. Food and Agriculture Organization of the United Nations. (2021). World food and agriculture—Statistical pocketbook 2021. Rome, Italy: Food and Agriculture Organization of the United Nations. Retrieved from https://www.fao.org/documents/card/en/c/cb4592en
5. Gao, J., French, A. P., Pound, M. P., He, Y., Pridmore, T. P., & Pieters, J. G. (2020). Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields. Plant Methods, 16, 29. https://doi.org/10.1186/s13007-020-00570-z
6. GC, S., Zhang, Y., Howatt, K., Schumacher, L. G., & Sun, X. (2024). Multi-species weed and crop classification: Comparison using five different deep learning network architectures. Journal of the ASABE, 67(2), 275-287. https://doi.org/10.13031/ja.15590
7. Gyawali, A., Bhandari, R., Budhathoki, P., & Bhattarai, S. (2022). A review on effect of weeds in wheat (Triticum aestivum) and their management practices. Food and Agriculture Extension Review, 2(2), 48-54. https://doi.org/10.26480/faer.02.2022.48.54
8. Juwono, F. H., Wong, W. K., Verma, S., Shekhawat, N., Lease, B. A., & Apriono, C. (2023). Machine learning for weed–plant discrimination in agriculture 5.0: An in-depth review. Artificial Intelligence in Agriculture, 10, 13-25. https://doi.org/10.1016/j.aiia.2023.09.002
9. Khosravi, H., Saedi, S. I., & Rezaei, M. (2021). Real-time recognition of on-branch olive ripening stages by a deep convolutional neural network. Scientia Horticulturae, 287, 110252. https://doi.org/10.1016/j.scienta.2021.110252
10. Langridge, P., Alaux, M., Almeida, N., Ammar, K., Baum, M., Bekkaoui, F., Bentley, A., Beres, B., Berger, B., Braun, H.-J., Brown-Guedira, G., Burt, C., Caccamo, M., Cattivelli, L., Charmet, G., Civáň, P., Cloutier, S., Cohan, J.-P., Devaux, P., …, & Zhang, X. (2022). Meeting the challenges facing wheat production: The strategic research agenda of the Global Wheat Initiative. Agronomy, 12, 2767. https://doi.org/10.3390/agronomy12112767
11. Li, Z., Wang, D., Yan, Q., Zhao, M., Wu, X., & Liu, X. (2024). Winter wheat weed detection based on deep learning models. Computers and Electronics in Agriculture, 227, 109448. https://doi.org/10.1016/j.compag.2024.109448
12. Makarian, H., & Saedi, S. I. (2024). Automated classification of saffron and broadleaf weeds of flixweed and hoary cress using deep learning and color images. Crop Protection, 106750. https://doi.org/10.1016/j.cropro.2024.106750
13. Montazeri, M., Zand, E., & Baghestani, M. A. (2005). Weeds and their control in wheat fields of Iran(1st ed.). Agricultural Research and Education Organization Press.
14. Nasiri, A., Omid, M., Taheri-Garavand, A., & Jafari, A. (2022). Deep learning-based precision agriculture through weed recognition in sugar beet fields. Sustainable Computing: Informatics and Systems, 35, 100759. https://doi.org/10.1016/j.suscom.2022.100759
15. Quan, L., Feng, H., Lv, Y., Wang, Q., Zhang, C., Liu, J., & Yuan, Z. (2019). Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN. Biosystems Engineering, 184, 1-23. https://doi.org/10.1016/j.biosystemseng.2019.05.002
16. Rai, N., & Sun, X. (2024). WeedVision: A single-stage deep learning architecture to perform weed detection and segmentation using drone-acquired images. Computers and Electronics in Agriculture, 219, 108792. https://doi.org/10.1016/j.compag.2024.108792
17. Richter, D. J., & Kim, K. (2025). Assessing the performance of domain-specific models for plant leaf disease classification: A comprehensive benchmark of transfer-learning on open datasets. Scientific Reports, 15, 18973. https://doi.org/10.21203/rs.3.rs-5668089/v1
18. Sadeghi, S. H., Saedi, S. I., Peters, R. T., & Stöckle, C. (2022). Towards improving the global water application uniformity of centre pivots through lateral speed adjustment. Biosystems Engineering, 215, 215-227. https://doi.org/10.1016/j.biosystemseng.2022.01.012
19. Saedi, S. I., Alimardani, R., Mousazadeh, H., & Salehi, R. (2019). Development and evaluation of an energy and water efficient intensive cropping system. INMATEH – Agricultural Engineering, 58, 93-104. https://doi.org/10.35633/inmateh-58-10
20. Saedi, S. I., & Khosravi, H. (2020). A deep neural network approach towards real-time on-branch fruit recognition for precision horticulture. Expert Systems with Applications, 159, 113594. https://doi.org/10.1016/j.eswa.2020.113594
21. Saedi, S. I., & Rezaei, M. (2024). A modified Xception deep learning model for automatic sorting of olives based on ripening stages. Inventions, 9(1), 6. https://doi.org/10.3390/inventions9010006
22. Saedi, S. I., Rezaei, M., & Khosravi, H. (2024). Dual-path lightweight convolutional neural network for automatic sorting of olive fruit based on cultivar and maturity. Postharvest Biology and Technology, 216, 113054. https://doi.org/10.1016/j.postharvbio.2024.113054
23. Salim, F., Saeed, F., Basurra, S., Qasem, S. N., & Al-Hadhrami, T. (2023). DenseNet-201 and Xception pre-trained deep learning models for fruit recognition. Electronics, 12(14), 3132. https://doi.org/10.3390/electronics12143132
24. Shao, Y., Guan, X., Xuan, G., Gao, F., Feng, W., Gao, G., Wang, Q., Huang, X., & Li, J. (2023). GTCBS-YOLOv5s: A lightweight model for weed species identification in paddy fields. Computers and Electronics in Agriculture, 215, 108461. https://doi.org/10.1016/j.compag.2023.108461
25. Shewry, P. R., & Hey, S. J. (2015). The contribution of wheat to human diet and health. Food and Energy Security, 4(3), 178-202. https://doi.org/10.1002/fes3.64
26. Singh, M., Kukal, M. S., Irmak, S., & Jhala, A. J. (2021). Water use characteristics of weeds: A global review, best practices, and future directions. Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.794090
27. Speck, O., & Speck, T. (2021). Functional morphology of plants – a key to biomimetic applications. New Phytologist, 231(3), 950-956. https://doi.org/10.1111/nph.17396
28. Subeesh, A., Bhole, S., Singh, K., Chandel, N. S., Rajwade, Y. A., Rao, K. V. R., Kumar, S. P., & Jat, D. (2022). Deep convolutional neural network models for weed detection in polyhouse grown bell peppers. Artificial Intelligence in Agriculture, 6, 47-54. https://doi.org/10.1016/j.aiia.2022.01.002
29. Sultan, T., Chowdhury, M. S., Jahan, N., & Dunren, C. (2025). LeafDNet: Transforming leaf disease diagnosis through deep transfer learning. Plant Direct, 9, e70047. https://doi.org/10.1002/pld3.70047
30. Susetyarini, E., Wahyono, P., Latifa, R., & Nurrohman, E. (2020). The identification of morphological and anatomical structures of Pluchea indica. Journal of Physics: Conference Series, 1539, 012001. https://doi.org/10.1088/1742-6596/1539/1/012001
31. Xu, K., Zhao, Y., Wang, L., & Zhang, X. (2021). Multi-modal deep learning for weeds detection in wheat fields. Frontiers in Plant Science, 12, 732968. https://doi.org/10.3389/fpls.2021.732968