Detection of weeds in teff crops using deep learning and UAV imagery for precision herbicide application

In Ethiopia, Teff is a vital staple crop, yet its productivity is significantly challenges due to inefficient weed and fertilizer management, threatening food security. Traditional weed control methods rely on manual labor and the indiscriminate application of herbicides, resulting in inaccurate targeting, inefficient distribution, excessive labor, and reduced yields. Non-selective weed management often leads to herbicide misuse, compounded by the difficulty in distinguishing Teff from visually similar weeds, particularly on large farms. This study introduces an optimized deep learning model for weed detection in Teff fields, enabling selective and efficient herbicide application through unmanned aerial vehicles (UAVs). A dataset of 1308 high-resolution drone-captured images was collected across various growth stages and weather conditions from the University of Gondar Agricultural Research Farm and surrounding farms. Key shape-based features such as aspect ratio (AR) and solidity were utilized to enhance model performance. Further, we applied data augmentations at different ratios of the original dataset and experimented with various optimizers to enhance the model's adaptabliy to different data characteristics and minimize overfitting problems. Deep learning models, such as MobileNetV2, InceptionResNetV2, DenseNet201, VGG16, Resnet50, Fast R-CNN, and YOLOv8, were evaluated with and without fine-tuning. Among the models, fine-tuned MobileNetV2 achieved the highest accuracy (96.40%), demonstrating its potential for practical implementation in UAV-assisted precision agriculture. This work highlights the transformative role of AI-driven solutions in enhancing weed management and improving Teff crop productivity.