WheatPhenology: A Multi-Stage Field Image Dataset of Wheat Growth

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.