A computer-implemented method for estimating damage on plants caused by herbicide treatment, the method using a regression deep learning model (RDLM1), comprising: receiving a zenithal image representing a real-world situation at an agricultural growth location with one or more non-overlapping plants, the zenithal image associated with a particular plant species identifier, wherein the one or more plants have been subject to herbicide treatment, with the zenithal image being recorded a certain number of days after the treatment; segmenting, by using a damage agnostic plant segmentation algorithm, the received image into pixels associated with plant elements and into pixels associated with non-plant elements to obtain a masked image with the pixels associated with non-plant elements being ignored for further processing; if the masked image does not match the size of an input layer of the single regression deep learning model (RDLM1), resizing the masked image to match the size of the input layer while preserving the aspect ratio; providing to the input layer a test input comprising the masked image (20- 1m), the respective number of days after treatment, and the respective plant species identifier, wherein the regression deep learning model has been trained with a plurality of plant images for multiple plant species and for multiple days-aftertreatment values, with each training image showing either one or more non-overlapping healthy plants at a control location or one or more non-overlapping damaged plants of a particular plant species at an agricultural growth training location, at a particular number of days after treatment, each training image annotated by an expert with a phytotoxicity value (PDCU*) as ground truth, wherein the phytotoxicity value reflects the expert's assessment of a damage degree of one or more plants on the respective training image in comparison to plants of the control location in the vicinity of the agricultural growth training location in accordance with predefined assessment rules; and receiving, from the regression deep learning model, a predicted phytotoxicity value (PDCU) for the test input. receiving a zenithal image representing a real-world situation at an agricultural growth location with one or more non-overlapping plants, the zenithal image associated with a particular plant species identifier, wherein the one or more plants have been subject to herbicide treatment, with the zenithal image being recorded a certain number of days after the treatment; segmenting, by using a damage agnostic plant segmentation algorithm, the received image into pixels associated with plant elements and into pixels associated with non-plant elements to obtain a masked image with the pixels associated with non-plant elements being ignored for further processing; if the masked image does not match the size of an input layer of the single regression deep learning model (RDLM1), resizing the masked image to match the size of the input layer while preserving the aspect ratio; providing to the input layer a test input comprising the masked image (20- 1m), the respective number of days after treatment, and the respective plant species identifier, wherein the regression deep learning model has been trained with a plurality of plant images for multiple plant species and for multiple days-aftertreatment values, with each training image showing either one or more non-overlapping healthy plants at a control location or one or more non-overlapping damaged plants of a particular plant species at an agricultural growth training location, at a particular number of days after treatment, each training image annotated by an expert with a phytotoxicity value (PDCU*) as ground truth, wherein the phytotoxicity value reflects the expert's assessment of a damage degree of one or more plants on the respective training image in comparison to plants of the control location in the vicinity of the agricultural growth training location in accordance with predefined assessment rules; and receiving, from the regression deep learning model, a predicted phytotoxicity value (PDCU) for the test input.