International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 10.54216/IJAACI https://www.americaspg.com/journals/show/1841 2022 2022 Management Department, Applied College, Jazan University, Jazan, KSA Abdullah Ali Salamai Management Department, Applied College, Jazan University, Jazan, KSA Nouran Ajabnoor Management Department, Applied College, Jazan University, Jazan, KSA Ali Mohammad Khawaji One of the current issues in agriculture is the lack of mechanized weed management, which is why weed detection technologies are so crucial. Detecting weeds is useful because it may lead to the elimination of pesticide usage, which in turn improves the surroundings, human health, and the sustainability of agriculture. As novel algorithms are developed and computer capacity increases, deep learning-based approaches are gradually replacing classic machine learning methods for real-time weed detection. Mixed machine learning designs, which combine the best features of existing approaches, are becoming more popular. So, the goal of this study, present the CNN model for early weed detection. The CNN model is applied to the weed dataset. The CNN model achieved 96% accuracy. 2022 2022 23 28 10.54216/IJAACI.020103 https://www.americaspg.com/articleinfo/31/show/1841