<ul><li>DeepSeqCoco is a deep learning model designed for disease identification in coconut trees to address manual and labor-intensive methods currently in use.</li><li>The model was tested with different optimizer settings like SGD, Adam, and hybrid configurations to optimize accuracy, loss minimization, and computational cost.</li><li>Results from experiments show that DeepSeqCoco can achieve up to 99.5% accuracy, surpassing existing models, with the hybrid SGD-Adam configuration demonstrating the lowest validation loss of 2.81%.</li><li>The model also offers advantages such as reduced training time by up to 18% and prediction time by up to 85%, indicating its potential to enhance precision agriculture through an AI-based disease monitoring system.</li></ul>

DeepSeqCoco: A Robust Mobile Friendly Deep Learning Model for Detection of Diseases in Cocos nucifera

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