  In this paper, we propose a novel deep learning method based on a
Convolutional Neural Network (CNN) that simultaneously detects and geolocates
plantation-rows while counting its plants considering highly-dense plantation
configurations. The experimental setup was evaluated in a cornfield with
different growth stages and in a Citrus orchard. Both datasets characterize
different plant density scenarios, locations, types of crops, sensors, and
dates. A two-branch architecture was implemented in our CNN method, where the
information obtained within the plantation-row is updated into the plant
detection branch and retro-feed to the row branch; which are then refined by a
Multi-Stage Refinement method. In the corn plantation datasets (with both
growth phases, young and mature), our approach returned a mean absolute error
(MAE) of 6.224 plants per image patch, a mean relative error (MRE) of 0.1038,
precision and recall values of 0.856, and 0.905, respectively, and an F-measure
equal to 0.876. These results were superior to the results from other deep
networks (HRNet, Faster R-CNN, and RetinaNet) evaluated with the same task and
dataset. For the plantation-row detection, our approach returned precision,
recall, and F-measure scores of 0.913, 0.941, and 0.925, respectively. To test
the robustness of our model with a different type of agriculture, we performed
the same task in the citrus orchard dataset. It returned an MAE equal to 1.409
citrus-trees per patch, MRE of 0.0615, precision of 0.922, recall of 0.911, and
F-measure of 0.965. For citrus plantation-row detection, our approach resulted
in precision, recall, and F-measure scores equal to 0.965, 0.970, and 0.964,
respectively. The proposed method achieved state-of-the-art performance for
counting and geolocating plants and plant-rows in UAV images from different
types of crops.
