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

TL;DR

DeepSeqCoco is a deep learning model designed for accurate, fast, and scalable detection of coconut tree diseases, significantly improving accuracy and efficiency over existing methods, thus aiding precision agriculture in developing regions.

Contribution

The paper introduces DeepSeqCoco, a novel deep learning model optimized for mobile devices, achieving higher accuracy and faster prediction times for coconut disease detection than previous models.

Findings

Achieved up to 99.5% accuracy in disease detection.

Reduced prediction time by up to 85%.

Hybrid optimizer (SGD-Adam) minimized validation loss to 2.81%.

Abstract

Coconut tree diseases are a serious risk to agricultural yield, particularly in developing countries where conventional farming practices restrict early diagnosis and intervention. Current disease identification methods are manual, labor-intensive, and non-scalable. In response to these limitations, we come up with DeepSeqCoco, a deep learning based model for accurate and automatic disease identification from coconut tree images. The model was tested under various optimizer settings, such as SGD, Adam, and hybrid configurations, to identify the optimal balance between accuracy, minimization of loss, and computational cost. Results from experiments indicate that DeepSeqCoco can achieve as much as 99.5% accuracy (achieving up to 5% higher accuracy than existing models) with the hybrid SGD-Adam showing the lowest validation loss of 2.81%. It also shows a drop of up to 18% in training time and up to 85% in prediction time for input images. The results point out the promise of the model to improve precision agriculture through an AI-based, scalable, and efficient disease monitoring system.