Previous works have shown the effectiveness of EfficientNet - a convolutional neural network built upon the concept of compound scaling - in automatically detecting smoke plumes at a distance of several kilometres in visible camera images. Building on these results, we have created enhanced EfficientNet models capable of precisely identifying the smoke location due to the introduction of a mosaic-like output and achieving extremely reduced false positive percentages due to using partial AUROC and applying class imbalance. Our EfficientNets beat InceptionV3 and MobileNetV2 in the same dataset and achieved a true detection percentage of 89.2% and a false positive percentage of only 0.306% across a test set with 17,023 images. The complete dataset used in this study contains 26,204 smoke and 51,075 non-smoke images. This makes it one of the largest, if not the most extensive, datasets reported in the scientific literature for smoke plume imagery. So, the achieved percentages are not only among the best reported for this application but are also among the most reliable due to the extent and representativeness of the dataset.