Corn emergence uniformity estimation and mapping using UAV imagery and deep learning

Assessment of corn (Zea Mays L.) emergence uniformity is important to evaluate crop yield potential. Previous studies have shown the potential of unmanned aerial vehicle (UAV) imagery and deep learning (DL) models in estimating early stand count and plant spacing uniformity, but few have extended further to field-scale mapping. Additionally, estimation of plant emergence date using UAV imagery in field-scale studies has not been achieved. This study aimed to estimate and map corn emergence uniformity using UAV imagery and DL modeling. Corn emergence uniformity was quantified with plant density, plant spacing standard deviation (PSstd), and mean days to imaging after emergence (DAEmean). Corn was planted at four depths (3.8, 5.1, 6.4, and 7.6 cm). A UAV imaging system equipped with a red, green, and blue (RGB) camera was used to acquire images at 10 m above ground level at 32 days after planting (20 days after emergence at V2-V4 growth stage). A pre-trained convolutional neural network, ResNet18, was used to estimate the three emergence parameters. Results showed the estimation accuracies in the testing dataset for plant density, PSstd, and DAEmean were 0.97, 0.73, and 0.95, respectively. The developed method had higher accuracy and lower root-mean-square-error for plant density and DAEmean, indicating better performance than previous studies. A case study was conducted to assess the emergence uniformity of corn at different planting depths using the developed estimation models at the field scale. From this, field maps were produced. Results showed that the average plant density and DAEmean decreased and the average PSstd increased with increasing depths, indicating deeper planting depths caused less and later emergence and less spatial uniformity in this field. These emergence uniformity field maps could be used for future field-scale agronomic studies on temporal and spatial crop emergence uniformity and for making planting decisions in commercial production.