Journal of Agricultural Machinery
with the collaboration of Iranian Society of Mechanical Engineers (ISME)

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

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

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Journal of Agricultural Machinery

Articles in Press, Accepted Manuscript
Available Online from 23 May 2026

  • Receive Date 25 January 2026
  • Revise Date 24 February 2026
  • Accept Date 15 March 2026
  • First Publish Date 23 May 2026

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