DOI: 10.20350/digitalcsic/17475
title: GROUNDBASED_WEED: Ground-Based Imagery for Early Weed Classification in Tomato Crops

description: [Description of methods used for collection/generation of data] A handheld Canon PowerShot SX540 HS camera was utilized, offering a spatial resolution of 5184 × 3886 pixels. To ensure optimal image quality under dynamic and variable lighting conditions, the camera operated with an automated ISO sensitivity setting and a shutter speed of 1/1000 second. A zenithal (overhead) image was captured from an elevation ranging between 1 and 1.5 meters.
description: [Methods for processing the data] The annotation of the dataset was performed using LabelImg (version 1.8.6), a free and open-source graphical image annotation tool developed in a Python environment, with the PascalVOC format employed for exporting the corresponding XML files. This format records the coordinates of bounding boxes that delineate the objects within each image. All bounding boxes associated with a single image were stored in one XML file. Each object entry includes the coordinates of the upper-left and lower-right corners—denoted as xmin, ymin, xmax, and ymax—along with the class name of the labeled object. Six plant species were categorized with the assistance of weed science experts and labeled according to the EPPO (European and Mediterranean Plant Protection Organization) code system. Another class was included as NR as unrecognized species due to insufficient image resolution or small plant size. The assigned labels are as follows: 1. SOLNI: Solanum nigrum L. 2. POROL: Portulaca oleracea L. 3. SETVE: Setaria verticillata L. 4. ECHCG: Echinochloa crus-galli L. 5. CYPRO: Cyperus rotundus L. 6. LYPES: Solanum lycopersicum L. 7. NR: Unrecognized species due to insufficient image resolution or small plant size These annotations provide a structured foundation for training and validating object detection models, facilitating accurate weed identification in early growth stages.
description: Accurate identification of weed species at early developmental stages is essential for advancing precision agriculture. Species-level classification enables the implementation of site-specific management strategies, reducing herbicide usage and fostering sustainable crop production. This study introduces a curated dataset of RGB images captured using a handheld Canon PowerShot SX540 HS camera, which provides a spatial resolution of 5184 × 3886 pixels. The images were collected from commercial tomato fields in Santa Amalia, Badajoz —an important agricultural tomato hub within the Vegas Altas region of Spain.
description: The dataset consists of images collected from various agricultural fields, with a focus on summer commercial tomato crops and their associated weed species. It includes a total of 1,216 images and 20,991 labeled instances, gathered during the growing seasons of 2021 and 2022. Data collection was conducted in May and June of each year. To reflect the temporal separation of the data, the dataset is divided into two folders: TOMATO_1 and TOMATO_2. The TOMATO_1 folder contains 938 images and 9,060 labeled instances corresponding to the 2021 season, while TOMATO_2 comprises 278 images and 11,931 labeled instances from the 2022 season. The dataset includes five agronomically significant weed species, which are classified into two botanical groups: the monocotyledonous species Cyperus rotundus L., Echinochloa crus-galli L., and Setaria verticillata L.; and the dicotyledonous species Portulaca oleracea L. and Solanum nigrum L. In addition to these weed species, the dataset also contains images of the tomato crop Solanum lycopersicum L.
description: All images are stored in JPG format and are individually annotated to indicate the corresponding weed species. Each image is accompanied by a corresponding XML file in PascalVOC format, which contains the annotation data, including bounding box coordinates and class labels for the identified species according to the EPPO (European and Mediterranean Plant Protection Organization) code. This dataset is particularly well-suited for training and evaluating advanced machine learning and deep learning models for early-stage weed detection and classification.