Sugarcane drought detection through spectral indices derived modeling by remote-sensing techniques

Optimal Deep Learning Driven Smart Sugarcane Crop Monitoring on Remote Sensing Images

“…The effect of data extension and lighting conditions in distinct times on the outcomes of sugarcane stem node recognition has been deliberated, and the dominance of YOLO v4 implemented best in the study has been confirmed by relating four distinct DL approaches such as YOLO v3, Fast RCNN, SSD300, and RetinaNet. Picoli et al [14] examine the Landsat image probable for sugarcane scarcity recognition by measuring the relationships among normalized difference water index (NDWI), vegetation condition index (VCI), normalized difference infrared index (NDII)), global vegetation moisture index (GVMI), agricultural and vegetation scarcity indices (normalized difference vegetation index (NDVI). The study presented two novel indices merging short-wave infrared (SWIR) and near-infrared (NIR) bands projected for detecting sugarcane deficiency.…”

Analysis of the recent trends in vegetation dynamics and its relationship with climatological factors using remote sensing data for Caspian Sea watersheds in Iran

“…Climate change affects the Earth's ecosystems to a significant extent, but not every region is impacted by it in the same manner. It is and will be more pronounced for regions characterized by a sensitive equilibrium between the local ecosystem and climate, like parts of the Mediterranean or the Sahelian regions (Lereboullet et al, 2013;Fayech and Tarhouni, 2020;Picoli et al, 2019;Rousta et al, 2021;Shahbazi et al, 2009). Vegetation is a very sensitive part of human life and activity and is susceptible to climate change impacts on the environment.…”