Monitoring the nutritional status of crops is crucial to assessing high productivity, optimizing cost, and minimizing environmental impact. Given that nutritional deficiencies primarily manifest through visual characteristics, artificial vision stands out as a competitive choice to assess the nutritional status of individual plants. However, in order to train a supervised artificial vision system driven by convolutional neural networks (CNNs), a high amount of data, properly formatted and labeled is necessary. This work presents a curated image database to study single-nutrient deficiencies, specifically, phosphorus deficiency in maize leaves, named Maize Phosphorus Leaf Deficiency (MPLD) Database. This database is composed of 20892 samples of maize leaves placed on a withe background. Images are 224x224 pixels size, representing three levels of phosphorus deficiency: complete absence of the nutrient (labeled -P), half dose of the required phosphorus for normal plant development (-P50), and complete supply (C).