A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery
| Autor: | José Marcato Junior, Juliana Batistoti, Felipe David Georges Gomes, Alexandre Menezes Dias, Veraldo Liesenberg, Ana Paula Marques Ramos, Lúcio André de Castro Jorge, Lucas Prado Osco, Wesley Nunes Gonçalves, Diogo Nunes Gonçalves, Mauro dos Santos de Arruda, Lingfei Ma, Jonathan Li, Maurício de Souza |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
J.2 010504 meteorology & atmospheric sciences Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 0211 other engineering and technologies 02 engineering and technology 01 natural sciences Convolutional neural network Robustness (computer science) Approximation error Computers in Earth Sciences Engineering (miscellaneous) 021101 geological & geomatics engineering 0105 earth and related environmental sciences business.industry Deep learning Pattern recognition Atomic and Molecular Physics and Optics Object detection Computer Science Applications Precision agriculture Artificial intelligence Precision and recall business Row |
| Zdroj: | ISPRS Journal of Photogrammetry and Remote Sensing. 174:1-17 |
| ISSN: | 0924-2716 |
| DOI: | 10.1016/j.isprsjprs.2021.01.024 |
| Popis: | 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. Comment: 27 pages, 12 figures, 9 tables |
| Databáze: | OpenAIRE |
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