A Novel CNN Gradient Boosting Ensemble for Guava Disease Detection

TL;DR

This paper introduces an ensemble model combining CNN and Gradient Boosting Machine to accurately detect guava diseases, achieving nearly 100% classification accuracy for real-time agricultural monitoring in Bangladesh.

Contribution

It presents a novel CNN-ML ensemble framework that significantly improves guava disease detection accuracy over existing methods.

Findings

Achieved approximately 99.99% classification accuracy.

Demonstrated effectiveness of ensemble models for real-time disease detection.

Validated approach on dataset from Bangladeshi plantations.

Abstract

As a significant agricultural country, Bangladesh utilizes its fertile land for guava cultivation and dedicated labor to boost its economic development. In a nation like Bangladesh, enhancing guava production and agricultural practices plays a crucial role in its economy. Anthracnose and fruit fly infection can lower the quality and productivity of guava, a crucial tropical fruit. Expert systems that detect diseases early can reduce losses and safeguard the harvest. Images of guava fruits classified into the Healthy, Fruit Flies, and Anthracnose classes are included in the Guava Fruit Disease Dataset 2024 (GFDD24), which comes from plantations in Rajshahi and Pabna, Bangladesh. This study aims to create models using CNN alongside traditional machine learning techniques that can effectively identify guava diseases in locally cultivated varieties in Bangladesh. In order to achieve the highest classification accuracy of approximately 99.99% for the guava dataset, we propose utilizing ensemble models that combine CNNML with Gradient Boosting Machine. In general, the CNN-ML cascade framework exhibits strong, high-accuracy guava disease detection that is appropriate for real-time agricultural monitoring systems.