Different studies in the field of agricultural engineering have successfully related irrigation needs of plants with the percentage of green cover in crop images, by using simple allometric equations. Therefore, the problem of segmenting plants from soil in digital images becomes a key component of many water management systems. The development of automatic computer vision algorithms avoids slow and expensive procedures which require the supervision of human experts. In this sense, color analysis techniques have shown to yield the best results in accuracy and efficiency. This paper describes the design and development of a new web application with two different color segmentation techniques to estimate the percentage of green cover. The system allows a remote monitoring of crops, including functionality to upload images, analyze images, database storage, and graphical visualization of the results. An extensive experimental validation of this tool has been carried out on a lettuce crop of variety 'Little Gem'. The two segmentation methods - based on probabilistic color models using histograms, and clustering in the RGB space using the fuzzy c-means algorithm - are compared with respect to a manual segmentation technique which allows the human expert to validate the outcome of the process for each image. The experimental results demonstrate the feasibility of these two automatic methods as substitutes of the supervised process. The first method achieves a relative error below 2.4% in the obtained segmentation, while the second method has an error below 4.8%. Both techniques require less than 1 s of processing time in the server. Equations to compute the crop coefficient parameter are also included and validated for the same kind of crop.(C) 2016 Elsevier B.V. All rights reserved.