Feeding a global population of 9.1 billion in 2050 will require food production to be increased by approximately 60%. In this context, plant breeders are demanding more effective and efficient field-based phenotyping methods to accelerate the development of more productive cultivars under contrasting environmental constraints. The leaf area index (LAI) is a dimensionless biophysical parameter of great interest to maize breeders since it is directly related to crop productivity. The LAI is defined as the one-sided photosynthetically active leaf area per unit ground area. Direct estimates of the LAI through leaf collection and subsequent leaf area determination in the laboratory are tedious and time-consuming. Hence, indirect methods based on gap fraction theory are frequently used for in situ LAI estimation. The LAI obtained from gap fraction analysis by most optical sensors available on the market is not the true LAI, but a term called the “effective LAI” that does not consider foliage clumping. Hemispherical images of the bottom-up view of crop canopies offer important advantages to maize breeders, such as a low cost compared to other commercial sensors, and it also may provide LAI estimates corrected for foliage clumping (i.e., true LAI). However, taking bottom-up hemispherical images in every single plot of a maize breeding program can take time and patience. The use of small-sized unmanned aerial vehicles (UAVs) in agriculture has allowed for crop information inference at spatial and temporal resolutions that exceed the benefits of other remote sensing technologies (e.g., airborne, satellites). We assessed the efficacy of using UAVs to collect hemispherical images for estimating the LAI. To do this, we investigated the suitability of using nadir-view hemispherical images taken from a UAV flying at a low altitude (15 m) to accurately derive LAI estimates based on gap fraction analysis in a maize breeding trial carried out near Seville, Spain. Six maize cultivars grown in a split-plot design with three blocks and two irrigation treatments (well-watered and water-stressed) were used in the experiment. LAI estimates from top-down hemispherical imaging taken from the UAV were compared with LAI estimates from both bottom-up hemispherical imaging and direct LAI estimates obtained from an allometric relationship derived in the study. The results show that hemispherical images taken from a UAV flying at a low altitude can estimate the LAI of maize breeding plots as accurately as by the classical bottom-up hemispherical imaging approach. CAN-EYE software, which includes automatic image classification and allows the processing of a series of hemispherical photographs, was used in this experiment.