Abstract
Weed management technologies that can identify weeds and distinguish them from crops are in need of artificial intelligence solutions based on a computer vision approach, to enable the development of precisely targeted and autonomous robotic weed management systems. A prerequisite of such systems is to create robust and reliable object detection that can unambiguously distinguish weed from food crops. One of the essential steps towards precision agriculture is using annotated images to train convolutional neural networks to distinguish weed from food crops, which can be later followed using mechanical weed removal or selected spraying of herbicides. In this data paper, we propose an open-access dataset with manually annotated images for weed detection. The dataset is composed of 1118 images in which 6 food crops and 8 weed species are identified, altogether 7853 annotations were made in total. Three RGB digital cameras were used for image capturing: Intel RealSense D435, Canon EOS 800D, and Sony W800. The images were taken on food crops and weeds grown in controlled environment and field conditions at different growth stages
Highlights
Agronomy and Crop Science, Computer Vision and Pattern Recognition
The data was acquired by capturing images with a resolution of 720 × 1280 × 3, 1000 × 750 × 3, 640 × 480 × 3, 640 × 360 × 3 and 480 × 384 × 3 pixels in a controlled and unregulated environment using the Canon EOS 800D, and Sony W800 digital cameras and the Intel RealSense D435 camera
Raw images:.jpg format, manually annotated images: .xml files Data was acquired by capturing images in field conditions and in a controlled environment
Summary
Value of the Data The dataset presents images of food crops and weed in their seedling growth stages and, respectively, their manually annotated images.
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