Plabiany Rodrigo Acosta's research while affiliated with Universidade Federal de Mato Grosso do Sul and other places

... While lacking a reference dataset for precise accuracy assessment, these results are in close agreement with the estimation provided by the Forest Protection Department of Lam Dong province, which reported an area of about 13 ha [20], thus suggesting that the methodology used in this study is reliable and accurate, and can be useful to map burned area in near real-time. In comparison with previous work focusing on burned area mapping using deep learning techniques trained on PlanetScope observations [25,26], or the fusion of PlanetScope with other optical satellite observations (i.e., Landsat-8 and Sentinel-2) [2,27], the proposed method is faster and less complicated. This method is suitable for local managers to rapidly generate burned area maps; therefore, it is very useful for emergency response of forest fires, particularly in rural areas. ...

... Fry counting refers to the counting of the number of targets in a specific area to aid in production decisions [1], [2], [3]. Its accuracy is very important for scientific decisionmaking, including the scientific feeding, behavioral analysis, transportation, and marketing of fish, along with an assessment of fry survival and culture density control [4], [5], [6], [7]. ...

... In addition, deep learning-based approaches, single and two-stage detection techniques are used for object detection. Especially in fruit detection, there are studies in which YOLO [13][14][15] and CNN [16] architectures are frequently used. A detailed comparison of these studies is given in Table 1. ...