Abstract
Maize is an important food source standing first in production around the world. It has become an essential raw material in most of the food processing industries and is extensively cultivated for its post-harvest by-products. So, it is necessary to develop genotypes adapted to varied climate and soil conditions favoring its productivity. Assessing the varietal performance across fields is essential in crop development. Here, we present a dataset of phenotypical and yield attributes of maize. We cultivated maize organically in an agricultural farm of Vellore Institute of Technology, Vellore during the late kharif season of 2023. A total of eight varieties were selected for cultivation, with four recent gazette varieties from IIMR, Punjab, and four locally cultivated varieties. The crop was sown in randomized block design with four replications and eight varieties taken as treatments. We randomly selected 20 plants from each treatment in a way that 5 plants were chosen from each replication and tagged them. Thus, we obtained 160 individual crop data from the overall maize population. The dataset covers: (1) vegetative traits (2) yield traits (3) canopy temperature (CT) and (4) chlorophyll (Ch) readings. Significant difference between genotypes was determined using ANOVA (FRBD). This dataset can be incorporated in future research focused on the breeding and improvement of chosen varieties and can also be utilized for building deep learning models.