Farmland parcel extraction and area calculation from UAV images based on semantic segmentation

In irrigation management of smart farming, the accurate and efficient calculation of farmland parcel area is regarded as a critical component, by which the optimization of water resource allocation and the enhancement of agricultural production efficiency are significantly promoted. This study proposed a method for farmland parcel extraction and area calculation from Unmanned Aerial Vehicle (UAV) images based on semantic segmentation. First, a theoretical dynamic pixel adjustment model was established based on camera imaging principles to improve the calculation method for farmland parcel area. Then, farmland parcel extraction from UAV images was performed by semantic segmentation, through which the efficiency of area calculation was enhanced. Finally, verification of the proposed method and its improved algorithm’s accuracy and applicability was conducted by utilizing self-built farmland parcel datasets and multi-altitude aerial image datasets. Experimental results indicated that the accuracy of parcel segmentation and that of area calculation methods exert a mutual influence on each other, with their relative importance varying across different stages of the workflow and the calculation accuracy and efficiency of farmland parcel area was significantly improved by the proposed method. In the task of farmland parcel extraction, the semantic segmentation model based on Deeplabv3+ resulted excellent performance, achieving a test-set F1 score, Miou, OA, precision and recall of the test set are 96.60 %, 95.67 %, 97.83 %, 97.95 % and 98.41 %. An average relative error of 1.2 % is maintained by the improved algorithm across the aerial altitude range of 22–49m. In the range of 84–121m altitude, reductions of 84.56 % in mean squared error (MSE) and 64.46 % in mean absolute error (MAE) were achieved when compared with traditional methods. Comparative analysis with measured area data demonstrated that the area calculation error of the proposed method is strictly constrained within a 4 % threshold, satisfying with the precision standards of agricultural engineering.