**3.2. Planting**

Planting crops is a costly and cumbersome endeavor that has traditionally requires a lot of manpower. UAVs have simplified crop planting for farmers, with their abilities to cover large acres of land within a short period with utmost precision and accuracy. Today's high-end UAV farming technology offers UAV-powered planting techniques that reduce planting costs by up to 85%. The reduction in planting costs is a result of the UAV's capability of performing multiple tasks at the same time.

UAVs have become increasingly popular in recent years in agricultural research applications. They have been found to have capabilities of acquiring images with high spatial and temporal resolutions in Agriculture. Reference [14] evaluated the performance of a UAV-based remote sensing system for quantification of crop growth parameters of six sorghum hybrids. Factors such as Leaf Area Index (LAI), fractional vegetation (fc) and yields were considered. The evaluation was carried out using a fixed-wing UAV, equipped with a multi spectral sensor to collect images during the 2016 growing season with flight missions carried out 50 days after planting. The flight missions provided data covering the different growth periods of the sorghum hybrids. The authors inferred that high resolution images acquired using UAV can be effectively utilized for in-season data collection from the field. The results obtained proved the relationship between Normalized Difference Vegetation Index (NDVI) and LAI, and between NDVI and fc. It was thus possible to determine/estimate LAI and fc from UAV derived NDVI values. It was shown also that imagery taken at flowing stage could be better indicator of yield, rather than NDVI obtained at earlier growth stage of sorghum crop. Furthermore, it was also established that early season NDVI measurement is useful index for estimating plant population density of sorghum.

The authors in [15] sought to develop a novel method to quantify the distance between maize plants at field scale using an UAV. The distance between roots and plants are essential in determining the final grain yield in row cops. An UAV-based image algorithm was developed to calculate maize plant distances. Knowledge of the exact number of plants per square meter is essential and helps to improve yields by deducing the fertilizer and pesticide application to match plant demand. Determining plant population is essential for several other processes such as soil-to-plant balance, nutrient recycling and resource use efficiency. The study demonstrated the possibility of quantifying the distance between maize plants and provided an innovative approach to quantify plant-to-plant variability and by extension crop yield estimates.
