ISSN : 2663-2187
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Can Combination Therapy Improve Outcomes in PDE5i-Refractory Erectile Dysfunction? A Systematic Review of Low-Intensity Extracorporeal Shockwave Therapy
Volume 8 | Issue - 8
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Influence of Storage Temperature on the Bond Performance of Universal Adhesives: A Narrative Review
Volume 8 | Issue - 8
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Effects of direct acting antivirals(DAAs)on cryoglobulinemia in patients infected with hepatitis C virus
Volume 8 | Issue - 8
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Effect of Education Sessions About Plastic Usage Hazards and Its Safe Disposal on Knowledge and Reported Practice of Female Employees at Port Said University: A Pre–Post Intervention Study
Volume 8 | Issue - 7
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Soil Quality Under a Cocoa-Based Agroforestry System With Associated Indigenous Tree Species In The Department Of Divo (Cote d’Ivoire)
Volume 8 | Issue - 7
Automated Identification and Classification of Sugarcane Diseases Using a Machine Learning and Image Analysis
Main Article Content
Abstract
This research paper introduces a comprehensive system for the automatic identification and classification of sugarcane diseases (AICSD). It utilizes advanced machine learning techniques, specifically a Convolutional Neural Network (CNN) coupled with image analysis, to achieve an efficient and precise diagnosis of diseases in sugarcane crops.
