Intelligent Broadleaf Weed–Wheat Crop Classification via Deep Convolutional Neural Networks

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. Therefore, precision weed management, utilizing site-specific methods, will be vital for achieving sustainable wheat production. To this goal, in the present study, we utilized color images and deep learning techniques to distinguish winter wheat from four prevalent weeds, creating five distinct classes. A total of 542 images were captured in natural field conditions, and resized to 299×299 pixels. Four deep learning networks– Xception, EfficientNetB0, VGG19, and InceptionResNetV2– were evaluated to serve as base 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 exhibited excellent performance in distinguishing plant classes, with F1-scores ranging from 93–100%, 69–98%, 82–100%, and 93–100% for models based on Xception, EfficientNetB0, VGG19, and InceptionResNetV2, respectively. Additionally, we analyzed fifteen scenarios of weed presence in winter wheat fields, focusing on various weed types studied, to propose effective weed management strategies utilizing any of the four models. The research findings provide a foundation for the development of mobile apps or robots that facilitate site-specific weed management. This approach aims to reduce herbicide use and environmental impact while also improving wheat yield and quality.