7 páginas

Maximizing crop production efficiently and sustainably through plant health monitoring is key for global food security. Monitoring large areas with remote sensing technologies such as unmanned aerial vehicles (UAVs) with sensors deals with time and money issues; however, the usage of advanced sensors such as hyperspectral, multispectral and thermal cameras limit their usage among all the stakeholders. In this study we explore different vegetation indices (VIs) extracted from aerial RGB images acquired in different flights to differentiate the nutritional and water statuses of Hass avocado plantations. We used an image processing workflow consisting of image selection through a convolutional neural network (CNN) model, tree crown segmentation, color correction and feature extraction to automate the computation of VIs from RGB images. To compare the performance of VIs in the differentiatio...