Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery

• Published in: Precision agriculture Vol. 22; no. 4; pp. 1171 - 1188
• Main Authors: Osco, Lucas Prado, Nogueira, Keiller, Marques Ramos, Ana Paula, Faita Pinheiro, Mayara Maezano, Furuya, Danielle Elis Garcia, Gonçalves, Wesley Nunes, de Castro Jorge, Lucio André, Marcato Junior, José, dos Santos, Jefersson Alex
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2021; Springer Nature B.V
• Subjects: Agricultural land; Agricultural practices; Agriculture; Algorithms; Artificial neural networks; Atmospheric Sciences; Biomedical and Life Sciences; Cameras; Chemistry and Earth Sciences; Computer Science; Convolution; Decision making; Deep learning; Image segmentation; Inference; Life Sciences; Machine learning; Mapping; Near infrared radiation; Neural networks; Orchards; Physics; Plantations; Precision farming; Qualitative analysis; Remote Sensing/Photogrammetry; Semantic segmentation; Semantics; Soil Science & Conservation; State-of-the-art reviews; Statistics for Engineering; Teaching methods; Unmanned aerial vehicles; Thematic map; Convolutional neural network; Remote sensing
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-020-09777-5

Accurately mapping farmlands is important for precision agriculture practices. Unmanned aerial vehicles (UAV) embedded with multispectral cameras are commonly used to map plants in agricultural landscapes. However, separating plantation fields from the remaining objects in a multispectral scene is a difficult task for traditional algorithms. In this connection, deep learning methods that perform semantic segmentation could help improve the overall outcome. In this study, state-of-the-art deep learning methods to semantic segment citrus-trees in multispectral images were evaluated. For this purpose, a multispectral camera that operates at the green (530–570 nm), red (640–680 nm), red-edge (730–740 nm) and also near-infrared (770–810 nm) spectral regions was used. The performance of the following five state-of-the-art pixelwise methods were evaluated: fully convolutional network (FCN), U-Net, SegNet, dynamic dilated convolution network (DDCN) and DeepLabV3 + . The results indicated that the evaluated methods performed similarly in the proposed task, returning F1-Scores between 94.00% (FCN and U-Net) and 94.42% (DDCN). It was also determined the inference time needed per area and, although the DDCN method was slower, based on a qualitative analysis, it performed better in highly shadow-affected areas. This study demonstrated that the semantic segmentation of citrus orchards is highly achievable with deep neural networks. The state-of-the-art deep learning methods investigated here proved to be equally suitable to solve this task, providing fast solutions with inference time varying from 0.98 to 4.36 min per hectare. This approach could be incorporated into similar research, and contribute to decision-making and accurate mapping of plantation fields.