--- title: Object Detection with Bounding Boxes for Smart Agriculture type: templates hide_menu: true category: Computer Vision cat: computer-vision order: 1103 meta_description: Template for using Label Studio to perform object detection with rectangular bounding boxes for smart agriculture. --- ![Screenshot of labeling interface](/images/templates-misc/agriculture.png) Object Detection with Bounding Boxes labeled data is vital for AI applications in smart agriculture, as it empowers models to accurately identify and track various crops, weeds, and pests in real-time. By facilitating tasks such as crop monitoring, yield prediction, and pest control, these AI models can significantly optimize agricultural practices and enhance productivity. However, the data labeling process for this domain presents several challenges, including time-intensive annotation efforts, inconsistencies in labeling quality, and the need for domain expertise to ensure accuracy. Label Studio addresses these issues head-on through its innovative hybrid AI + human-in-the-loop approach. With AI-assisted pre-labeling, annotators can dramatically reduce their workload while maintaining high accuracy levels. The platform also includes customizable labeling templates tailored specifically to agricultural datasets, enabling efficient processing. Expert validation ensures that the final labels meet stringent quality standards, while collaboration tools streamline communication between teams, ultimately reducing labeling time and enhancing workflow scalability. By choosing Label Studio, organizations can improve model performance, save valuable resources, and confidently leverage AI to transform their agricultural practices. Open in Label Studio ## Labeling configuration ```html ``` All labeling configurations must be wrapped in View tags. Use the Image object tag to specify the aerial or satellite imagery to label: ```xml ``` Use the RectangleLabels control tag to add labels and rectangular bounding boxes to your agricultural images at the same time. Use the Label tag to control the color of the boxes: ```xml ``` If you want to add further context to object detection tasks with bounding boxes, you can add some per-region conditional labeling parameters to your labeling configuration. For example, to prompt annotators to add descriptions to detected agricultural features, you can add the following to your labeling configuration: ```html