Said, Raed A.Al-Dmour, Nidal A.Ali, LiaqatAlzoubi, Haitham M.Alshurideh, MuhammadSalahat, Mohammed2023-10-132023-10-13© 20232023Said, R. A., Al-Dmour, N. A., Ali, L., Alzoubi, H. M., Alshurideh, M., & Salahat, M. (2023). Linear Discrimination Analysis Using Image Processing Optimization. In M. Alshurideh, B.H. Al Kurdi, R. Masa’deh, H.M. Alzoubi & S. Salloum (Eds.) The Effect of Information Technology on Business and Marketing Intelligence Systems, 1056, (pp. 2491-2502). Cham: Springer International Publishing.1860949Xhttps://doi.org/10.1007/978-3-031-12382-5_137https://hdl.handle.net/20.500.12519/909When we talk about Machinery Vision and Deep Learning, we often talk about algorithms. In fact, mathematical models with computer knowledge are the basis of how we deal with graphical data to process the Image and make decision. Machine learning can play an important role in determining agricultural plant type in order to optimize the harvesting steps in an automated way. How to process and introduce the products to the market often requires detailed information about the stages of planting and harvesting. In addition, by using this method, sophisticated research can be designed in plant genetics and effect of environmental variables on the end product. The ultimate goal of this work is to use Linear Discrimination Analysis for the Image Processing and classification of harvested wheat grain which are belonged to different types of grain namely Rosa, Kama and Canadian. The above discovery has proved with the statistics to have with more than 94% of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.enLicense to reuse abstract has been secured from Springer Nature and Copyright Clearance Center.Agricultural productsClusteringData scienceHarvesting expert systemBook chapterCopyright : © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.