Evaluation of YOLOv10n for Tomato Plant and Weed Detection in Agricultural Fields Using a Lightweight Deep Learning Approach
Year: 2025
Type: article
Abstract: Deep learning techniques have emerged as powerful tools in precision agriculture, enabling automated crop monitoring and weed management. This study investigates the performance of the YOLOv10n model, a lightweight and computationally efficient deep learning architecture, for object detection in tomato crop fields. A custom dataset containing field images of tomato plants and diverse weed species was collected and annotated. The model was trained and tested using a dataset consisting of 1,091 training images, 326 validation images, and 174 testing images. A total of 2157 annotated instances across two classes; plant and weed, were collected from field conditions. The model achieved a mean Average Precision (mAP@50) of 62.5% and mAP@50–95 of 40.7%, with an inference time of 5.6 milliseconds per image. Class-wise evaluation revealed high detection accuracy for tomato plants, with a precision of 87.3% and recall of 83.5%, indicating the model’s strong potential to identify and preserve crop regions. Weed detection, however, showed relatively lower performance, primarily due to intra-class variability and class imbalance. These findings suggest that YOLOv10n can effectively detect tomato plants in complex backgrounds, providing a reliable basis for future integration into real-time precision agriculture systems. Further enhancement in weed detection may be achieved through data augmentation and improved class-specific representation.
Authors: Apoorva Sharma, Arun Kumar, Hemant Kumar Sharma, Konga Upendar
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4409833485