[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Article in Journal
Fusion of Remotely Sensed Data with Monitoring Well Measurements for Groundwater Level Management
Previous Article in Journal / Special Issue
Enhancing Environmental Control in Broiler Production: Retrieval-Augmented Generation for Improved Decision-Making with Large Language Models
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
AgriEngineering
Volume 7
Issue 1
10.3390/agriengineering7010013
agriengineering-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification

by
Opeyemi Adelaja
and
Bernardi Pranggono
*
School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UK
*
Author to whom correspondence should be addressed.
AgriEngineering 2025, 7(1), 13; https://doi.org/10.3390/agriengineering7010013
Submission received: 13 November 2024 / Revised: 24 December 2024 / Accepted: 3 January 2025 / Published: 8 January 2025
(This article belongs to the Special Issue The Future of Artificial Intelligence in Agriculture)
Versions Notes

Abstract

Agriculture is vital for providing food and economic benefits, but crop diseases pose significant challenges, including coffee cultivation. Traditional methods for disease identification are labor-intensive and lack real-time capabilities. This study aims to address existing methods’ limitations and provide a more efficient, reliable, and cost-effective solution for coffee leaf disease identification. It presents a novel approach to the real-time identification of coffee leaf diseases using deep learning. We implemented several transfer learning (TL) models, including ResNet101, Xception, CoffNet, and VGG16, to evaluate the feasibility and reliability of our solution. The experiment results show that the proposed models achieved high accuracy rates of 97.30%, 97.60%, 97.88%, and 99.89%, respectively. CoffNet, our proposed model, showed a notable processing speed of 125.93 frames per second (fps), making it suitable for real-time applications. Using a diverse dataset of mixed images from multiple devices, our approach reduces the workload of farmers and simplifies the disease detection process. The findings lay the groundwork for the development of practical and efficient systems that can assist coffee growers in disease management, promoting sustainable farming practices, and food security.
Keywords: convolutional neural network; deep learning; disease identification; transfer learning convolutional neural network; deep learning; disease identification; transfer learning

Share and Cite

MDPI and ACS Style

Adelaja, O.; Pranggono, B. Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification. AgriEngineering 2025, 7, 13. https://doi.org/10.3390/agriengineering7010013

AMA Style

Adelaja O, Pranggono B. Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification. AgriEngineering. 2025; 7(1):13. https://doi.org/10.3390/agriengineering7010013

Chicago/Turabian Style

Adelaja, Opeyemi, and Bernardi Pranggono. 2025. "Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification" AgriEngineering 7, no. 1: 13. https://doi.org/10.3390/agriengineering7010013

APA Style

Adelaja, O., & Pranggono, B. (2025). Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification. AgriEngineering, 7(1), 13. https://doi.org/10.3390/agriengineering7010013

Article Metrics

Back to TopTop