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Abstract Near real-time and non-destructive monitoring of wheat growth using the Leaf Area Index (LAI) is a reliable and proven method for effective agricultural management. However, challenges arise when dealing with high-dimensional data and capturing nonlinear variables using conventional methods. The study utilized three models–Bidirectional Long Short-Term Memory (Bi-LSTM), Deep Neural Network (DNN), and Random Forest (RF) to handle an Here are the key variables: VIS = 22, TFs = 64, initial = 86, optimal = 26. Please ensure all information is presented within a box. Instruction A graphical abstract is required for this journal and should be a colorful, eye-catching image that captures the reader's attention. The abstract can be a figure from the manuscript or a mosaic of panels arranged horizontally in landscape format, with the horizontal axis three times longer than the vertical axis. Avoid using figure captions and keep labels inside the figures minimal and in large fonts. Refer to http://www.elsevier.com/graphicalabstracts for examples and follow the journal's guidelines for authors. Authors must provide an original image that clearly represents the paper's content and obtain permission for any third-party material included. Any generative AI tools used must comply with the journal's policies. Submit the graphical abstract as a separate file in the online system. Ensure the abstract has a clear start and end for easy browsing, preferably reading from top to bottom or left to right. Minimize distracting elements and clutter. The image should be at least 1328 x 531 pixels with a resolution of 300 dpi. U se Times, Arial, Courier, or Symbol font with a large enough size for readability. F ile types such as TIFF, EPS, PDF, or MS Office are preferred. Avoid additional text, outlines, or synopses in the image file. Any text or labels should be integrated into the image itself. Do not include unnecessary white space or a heading like "graphical abstract."