The automatic identification of crops in images plays a crucial role in the digitization of agriculture, especially related to monitoring and preserving the agroecosystem. This study presents an analysis and comparison of traditional and modern methods for identifying olive trees in RGB images. Among these approaches, we examine classical computer vision algorithms using OpenCV as well as advanced deep learning models, such as U-Net, which leverage convolutional neural networks. Identifying crops in olive groves using only RGB images relies exclusively on color information, which can lead to ambiguities when distinguishing vegetation from surrounding elements in the ecosystem. Factors such as varying lighting conditions, seasonal changes in foliage and different types of terrain further complicate the identification process. This study aims to evaluate the strengths and limitations of each approach by testing RGB images under different brightness, resolution and distance conditions and to examine its potential impact on optimizing agricultural processes. The correct selection of these segmentation techniques could represent a significant advance in agricultural automation, allowing more efficient and reliable management of olive groves through real-time integration of artificial vision tools.