WheatPhenology: A Multi-Stage Field Image Dataset of Wheat Growth
Description
The image database name WheatPhenology is the field image repository directly designed to support created inquiries on crop phenology monitoring, agricultural computer vision, and precision agricultural practices. This repository contains high-resolution images of wheat crops collected at various phenological stages and, thus, can be utilized to train and test machine learning and deep-learning models to identify crop phenological stages. The farming fields within the area of 60-kilometre around the city of Sehore in Madhya Pradesh, India, a region known to have a high wheat production and a heterogeneous field environment were systematically sampled. This spatial heterogeneity makes the dataset able to reflect the differences in soil type, irrigation schedule, crop plant density, luminance and background, which reflect the real farming environments. The data has been painstakingly recorded in the form of wheat growth covering the key stages in development of the crop, including early vegetative growth, tillering, elongation of stems, booting, heading, flowering, grain filling, and maturity. The dataset, where the phenological phases are presented in the form of a visual representation, can be considered an invaluable source of training to algorithms aimed at identifying crop-stage classification or yield forecasting, growth monitoring, as well as developing intelligent agricultural decision-support systems. The photographs were taken in a real field setting using regular digital cameras and cell phone cameras, thus, guaranteeing the realistical change of illumination, perspective, and position of plants. This kind of acquisition strategy increases the extrapolation of models trained using the data to practical agricultural conditions. The dataset used to achieve robustness and application to the computer vision applications is inclusive of the images provided by different farms, different angles of the camera, and at various points in time during the wheat growing season. The use of the collection methodology focuses on the natural variability in crop structure, canopy formation and environmental background. Therefore, WheatPhenology provides an all-inclusive depiction of wheat growth in working farms. The dataset can be used to study the plant phenotyping, crop monitoring, phenological stage identification, and agricultural management systems based on AI as a benchmark. The dataset by combining spatial heterogeneity with the multi-phased growth representation can play a significant role in the development of automated crop monitoring systems and agricultural research based on data.
Files
Steps to reproduce
1. Study Area Selection Identify wheat cultivation fields within a 60 km radius of Sehore city, Madhya Pradesh, India. Select multiple farms representing different soil types, irrigation practices, and crop densities to ensure environmental diversity. 2. Define Wheat Growth Stages Determine the phenological stages of wheat to be documented. Typical stages include early vegetative stage, tillering, stem elongation, booting, heading, flowering, grain filling, and maturity. These stages should guide the timing of image collection throughout the growing season. 3. Field Visit Planning Schedule regular visits to the selected farms during the wheat growing season (approximately November to March/April in central India). Plan visits so that each phenological stage can be captured under natural field conditions. 4. Image Acquisition Capture high-resolution images using digital cameras or smartphone cameras directly in the field. Photographs should be taken from multiple angles, heights, and distances to capture variation in canopy structure, plant density, and background environment. 5. Environmental Diversity Capture Collect images at different times of the day (morning, afternoon, evening) and under varying lighting conditions. Include natural variability such as soil background, shadows, irrigation patterns, and surrounding vegetation. 6. Data Organization After collection, organize the images into folders corresponding to the identified wheat growth stages. Assign consistent file naming conventions and maintain metadata such as location, date, and growth stage if available. 7. Data Cleaning and Quality Check Remove blurred, duplicated, or low-quality images. Ensure that each growth stage contains sufficient samples and that images clearly represent the intended phenological phase. 8. Dataset Structuring Structure the final dataset into labeled categories representing each wheat growth stage. This structured format allows researchers to directly use the dataset for machine learning, deep learning, and computer vision applications such as crop stage classification and phenology monitoring. 9. Documentation Preparation Provide dataset documentation including data description, collection methodology, geographic coverage, growth stage definitions, and potential applications to enable other researchers to reproduce the dataset creation process.
Institutions
- VIT Bhopal UniversityMadhya Pradesh, Kothri Kalān