Analyzing cotton development and yield with unmanned aerial vehicle data

Predicting crop yield accurately, especially during early growth stages before harvest, is important for optimizing field management and evaluating crop performance for farmers and researchers. Traditional methods are costly, time-consuming, and relatively challenging to control over a wide area. With the advancement of modern science and technology, particularly computer technology, space technology, and information technology, society has entered the age of information where agricultural production challenges are increasingly being addressed with modern high technology. Remote sensing can provide rapid coverage and data acquisition over any scale of area. Unmanned aerial vehicles (UAVs) provide a unique platform for high-resolution remote sensing, and UAV-based remote sensing systems can be used to predict crop yield cost-effectively. In this study, the analysis of cotton plant yield using the Normalized Difference Vegetation Index (NDVI) obtained from an unmanned aerial vehicle (UAV) based remote sensing system equipped with multispectral cameras is aimed. The study is based on determining the correlations of data obtained from NDVI, chlorophyll measurement (SPAD) data, cotton yield and plant growth measurements (plant height, plant fresh and dry weight, number of bolls, cocoon weight) during the cotton ripening period. At the end of the study; A strong relationship was found between plant measurements, a moderate relationship between SPAD and plant measurements, a weak relationship between NDVI and other parameters except yield and SPAD, and a weak and negative relationship between yield and all other parameters. It seems that the findings indicate that data obtained from a single period (maturation period) in cotton is not effective in predicting pre-harvest yield.