Estimating okra leaf area index using unmanned aerial vehicle imagery

Abstract

The study aimed at estimating the leaf area index of okra using vegetative indices obtained by analysing image data obtained from a low-cost Unmanned Aerial Vehicle (UAV) in Ghana. Additionally, the work also assessed which of the two indices commonly used (excess green (ExG) and normalized green-red difference index (NGRDI) ) gave a better estimation of leaf area indices. The study was conducted in Cape Coast in southern Ghana at the experimental site located on the University of Cape Coast’s Teaching and Research Farm. The experiment was arranged in a randomised complete block design (RCBD) with four treatments (2cm, 3cm, 5cm and 7cm sowing depths) and four replicate blocks. This resulted in sixteen (16) plots each measuring 3 m by 3m. Overall, it was realized that sowing okra seeds at 3cm depth gave best prediction of the best leaf area index (R²> 76 for both indices) . Also, comparing the vegetative indices, the ExG gave a better regression(R²>0.0.65) compared to NGRDI (R²> 0.43). This suggests a recommended sowing depth 3cm) for okra and a good image-based vegetative index (ExG) for estimating leaf area index.