Dataset: Morphological traits, yield, and RGB images of Mung Bean (Vigna radiata L.) subjected to biofertilization

Mung bean (Vigna radiata L.) is a highly nutritious and sustainable pulse crop essential for global food security, yet its production is increasingly constrained by environmental and biotic stresses. To address these challenges through advanced crop management and precision agriculture, this paper presents a comprehensive agronomic and image dataset of mung bean plants subjected to biofertilization. The dataset encompasses detailed quantitative measurements of key morphophysiological and productive traits—including plant height, stem diameter, nodule and biomass weight, grain yield, and relative chlorophyll content—collected 60 days after planting across 18 experimental units. Crucially, these traditional biometric records are paired with high-resolution (24.2-megapixel) RGB images captured under standardized artificial night lighting and a controlled dark backdrop to perfectly isolate plant architecture. By integrating destructive agronomic evaluations with standardized visual data, this dataset provides a fundamental resource for researchers. It directly enables the development and validation of computer vision algorithms, machine learning models, and non-invasive phenotyping tools designed to assess biofertilizer efficacy and optimize crop yields in sustainable agricultural systems.