A computer-implemented method for estimating vegetation coverage in a real-world environment, the method comprising: receiving an RGB image of a real-world scenery with one or more plant elements of one or more plant species; providing at least one channel of the RGB image to a semantic regression neural network , wherein the semantic regression neural network has been trained with a training dataset to estimate at least a near-infrared channel (NIR) from the at least one channel of the RGB image, the semantic regression neural network having a topology based on a convolutional segmentation neural network with its last layer being substituted by a monotonic activation function and its loss function being substituted by a regression loss function, to learn a pixel-wise regression transformation that transforms any RGB channel from an RGB domain to a target domain comprising at least a near-infrared domain; obtaining an estimate of the near-infrared channel (NIR) by applying the semantic regression neural network to the RGB image ; providing a multi-channel image comprising at least one of the R-, G-, B-channels (R, G, B) of the RGB image, and the estimated near-infrared channel (NIR), as test input (TI1) to a semantic segmentation neural network , the semantic segmentation neural network trained with a training data set comprising multi-channel images of the test input type to segment the test input (TI1) into pixels associated with plant elements and pixels not associated with plant elements; and segmenting the test input (TI1) using the semantic segmentation neural network resulting in a vegetation coverage map indicating pixels of the test input associated with plant elements and indicating pixels of the test input not associated with plant elements. receiving an RGB image of a real-world scenery with one or more plant elements of one or more plant species; providing at least one channel of the RGB image to a semantic regression neural network , wherein the semantic regression neural network has been trained with a training dataset to estimate at least a near-infrared channel (NIR) from the at least one channel of the RGB image, the semantic regression neural network having a topology based on a convolutional segmentation neural network with its last layer being substituted by a monotonic activation function and its loss function being substituted by a regression loss function, to learn a pixel-wise regression transformation that transforms any RGB channel from an RGB domain to a target domain comprising at least a near-infrared domain; obtaining an estimate of the near-infrared channel (NIR) by applying the semantic regression neural network to the RGB image ; providing a multi-channel image comprising at least one of the R-, G-, B-channels (R, G, B) of the RGB image, and the estimated near-infrared channel (NIR), as test input (TI1) to a semantic segmentation neural network , the semantic segmentation neural network trained with a training data set comprising multi-channel images of the test input type to segment the test input (TI1) into pixels associated with plant elements and pixels not associated with plant elements; and segmenting the test input (TI1) using the semantic segmentation neural network resulting in a vegetation coverage map indicating pixels of the test input associated with plant elements and indicating pixels of the test input not associated with plant elements. <EOS>