Striga, a herbaceous parasitic plant, poses a significant treat to food crops in Sub-humid and Semi-arid areas. Three main species, Striga hermonthica, Striga asiatica, and Striga gesnerioides, cause major damage to crops such as maize, sorghum, millet, and groundnuts in this region. Striga infestations have a major impact on the food security and livelihoods of African farmers. The difficulty for the owner of a field of several hectares in size facilitates the rapid spread of striga in certain areas. Thus, visual detection of striga in cereal crop fields generally occurs once the plant has been completely devastated. It is therefore important to use digital tools to help farmers quickly identify signs of striga infestation in their fields. This allows early intervention to limit the damage. In addition, there are several classification algorithms, both binary and non-binary. In this paper, we have used the Support Vector Machine (SVM), a binary algorithm, to detect the presence of striga from photos or videos. This is an effective method, particularly useful for processing data such as images. Its aim is to determine the presence or absence of weeds, such as striga, in the image.