An Improved Agro Deep Learning Model for Detection of Panama Wilts Disease in Banana Leaves
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Published:2023-03-30
Issue:2
Volume:5
Page:660-679
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ISSN:2624-7402
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Container-title:AgriEngineering
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language:en
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Short-container-title:AgriEngineering
Author:
Sangeetha Ramachandran1, Logeshwaran Jaganathan2ORCID, Rocher Javier3ORCID, Lloret Jaime3ORCID
Affiliation:
1. Karunya Institute of Technology and Sciences, Coimbatore 641114, India 2. Sri Eshwar College of Engineering, Coimbatore 641202, India 3. Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politecnica de Valencia, Grao de Gandia, 46730 Valencia, Spain
Abstract
Recently, Panama wilt disease that attacks banana leaves has caused enormous economic losses to farmers. Early detection of this disease and necessary preventive measures can avoid economic damage. This paper proposes an improved method to predict Panama wilt disease based on symptoms using an agro deep learning algorithm. The proposed deep learning model for detecting Panama wilts disease is essential because it can help accurately identify infected plants in a timely manner. It can be instrumental in large-scale agricultural operations where Panama wilts disease could spread quickly and cause significant crop loss. Additionally, deep learning models can be used to monitor the effectiveness of treatments and help farmers make informed decisions about how to manage the disease best. This method is designed to predict the severity of the disease and its consequences based on the arrangement of color and shape changes in banana leaves. The present proposed method is compared with its previous methods, and it achieved 91.56% accuracy, 91.61% precision, 88.56% recall and 81.56% F1-score.
Subject
Engineering (miscellaneous),Horticulture,Food Science,Agronomy and Crop Science
Reference48 articles.
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