Prompt Description

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. This study utilized three models–Bidirectional Long Short-Term Memory (Bi-LSTM), Deep Neural Network (DNN), and Random Forest (RF) to handle an array of variables. Key variables include VIS = 22, TFs = 64, initial = 86, and optimal = 26. 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.