Template-Type: ReDIF-Article 1.0
Author-Name:Urwa Bibi, Muhammad Abubakar Siddique, Muskan Maryum, Shahzaib Akbar, Soyab Sundas
Author-Email:urwatariqkhosa@gmail.com
Author-Workplace-Name:Ghazi University, D.G.Khan
Title:AI in the Field: A Review of Deep Learning Methods for Weed Identification in Wheat Crops
Abstract:Weed infestation is a major constraint in wheat production, causing yield losses and higher  herbicide  dependence.  Traditional  control  methods  often  lack  precision, highlighting  the  need  for  intelligent,  sustainable  solutions.  Deep  learning  has recently emerged as a powerful tool for automated and accurate weed detection in precision agriculture. This review summarizes the latest advances in deep learning applied to wheat weed identification, emphasizing model architectures, datasets, and imagingtechniques. Approaches such as YOLO variants, Faster R-CNN, U-Net, and transformer-based models have achieved high  accuracy  in  distinguishing  wheat  from  diverse  weed species,  even  under  complex  field conditions.  Integration  of  UAV  imagery,  multispectral  sensors,  and  spectral  indices  further enhances   detection   at   early   growth   stages.   Recent   innovations,including   attention mechanisms, feature fusion, optimized loss functions, and lightweight designs,have improved precision, speed, and generalization. Key challenges remain in dataset quality, class imbalance, and cross-field applicability. This work outlines current trends, identifies gaps, and highlights future  directions  for  scalable  and  sustainable  deep  learning-based  weed  detection  in  wheat agriculture.
Keywords:Weed  detection,  Wheat Crops,  Smart  farming,  Artificial  intelligence  (AI)  in Agriculture, Convolutional Neural Networks (CNN), YOLO Architecture, UAV-Based Weed Mapping
Journal: International Journal of Innovations in Science and Technology
Pages:2153-2170
Volume:7
Issue:3
Year: 2025
Month:August
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1568/2236
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1568
File-Format: text/html
Handle: RePEc:abq:IJIST:v:7:y:2025:i:3:p:2153-2170