Hybrid Adaptive Multiple Intelligence System (HybridAMIS) for classifying cannabis leaf diseases using deep learning ensembles
•Hybrid AMIS system for deep learning in cannabis disease classification.•Combines multiple image augmentation and segmentation techniques.•Utilizes diverse CNN architectures for robust disease identification.•Enhances sustainable agricultural practices with improved disease management. Optimizing c...
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Published in | Smart agricultural technology Vol. 9; p. 100535 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.12.2024
Elsevier |
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Abstract | •Hybrid AMIS system for deep learning in cannabis disease classification.•Combines multiple image augmentation and segmentation techniques.•Utilizes diverse CNN architectures for robust disease identification.•Enhances sustainable agricultural practices with improved disease management.
Optimizing cannabis crop yield and quality necessitates accurate, automated leaf disease classi-fication systems for timely detection and intervention. Existing automated solutions, however, are insufficiently tailored to the specific challenges of cannabis disease identification, struggling with robustness across varied environmental conditions and plant growth stages. This paper introduces a novel Hybrid Adaptive Multi-Intelligence System for Deep Learning Ensembles (HyAMIS-DLE), utilizing a comprehensive dataset reflective of the diversity in cannabis leaf diseases and their progression. Our approach combines non-population-based decision fusion in image prepro-cessing with population-based decision fusion in classification, employing multiple CNN archi-tectures. This integration facilitates a significant improvement in performance metrics: Hy-AMIS-DLE achieves an accuracy of 99.58 %, outperforming conventional models by up to 4.16 %, and exhibits superior robustness and an enhanced Area Under the Curve (AUC) score, effectively distinguishing between healthy and diseased leaves. The successful deployment of HyAMIS-DLE within our Automated Cannabis Leaf Disease Classification System (A-CLDC-S) demonstrates its practical value, contributing to increased crop yields, reduced losses, and the promotion of sus-tainable agricultural practices. |
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AbstractList | •Hybrid AMIS system for deep learning in cannabis disease classification.•Combines multiple image augmentation and segmentation techniques.•Utilizes diverse CNN architectures for robust disease identification.•Enhances sustainable agricultural practices with improved disease management.
Optimizing cannabis crop yield and quality necessitates accurate, automated leaf disease classi-fication systems for timely detection and intervention. Existing automated solutions, however, are insufficiently tailored to the specific challenges of cannabis disease identification, struggling with robustness across varied environmental conditions and plant growth stages. This paper introduces a novel Hybrid Adaptive Multi-Intelligence System for Deep Learning Ensembles (HyAMIS-DLE), utilizing a comprehensive dataset reflective of the diversity in cannabis leaf diseases and their progression. Our approach combines non-population-based decision fusion in image prepro-cessing with population-based decision fusion in classification, employing multiple CNN archi-tectures. This integration facilitates a significant improvement in performance metrics: Hy-AMIS-DLE achieves an accuracy of 99.58 %, outperforming conventional models by up to 4.16 %, and exhibits superior robustness and an enhanced Area Under the Curve (AUC) score, effectively distinguishing between healthy and diseased leaves. The successful deployment of HyAMIS-DLE within our Automated Cannabis Leaf Disease Classification System (A-CLDC-S) demonstrates its practical value, contributing to increased crop yields, reduced losses, and the promotion of sus-tainable agricultural practices. Optimizing cannabis crop yield and quality necessitates accurate, automated leaf disease classi-fication systems for timely detection and intervention. Existing automated solutions, however, are insufficiently tailored to the specific challenges of cannabis disease identification, struggling with robustness across varied environmental conditions and plant growth stages. This paper introduces a novel Hybrid Adaptive Multi-Intelligence System for Deep Learning Ensembles (HyAMIS-DLE), utilizing a comprehensive dataset reflective of the diversity in cannabis leaf diseases and their progression. Our approach combines non-population-based decision fusion in image prepro-cessing with population-based decision fusion in classification, employing multiple CNN archi-tectures. This integration facilitates a significant improvement in performance metrics: Hy-AMIS-DLE achieves an accuracy of 99.58 %, outperforming conventional models by up to 4.16 %, and exhibits superior robustness and an enhanced Area Under the Curve (AUC) score, effectively distinguishing between healthy and diseased leaves. The successful deployment of HyAMIS-DLE within our Automated Cannabis Leaf Disease Classification System (A-CLDC-S) demonstrates its practical value, contributing to increased crop yields, reduced losses, and the promotion of sus-tainable agricultural practices. |
ArticleNumber | 100535 |
Author | Matitopanum, Surasak Gonwirat, Sarayut Luesak, Peerawat Kraiklang, Rungwasun Srichok, Thanatkij Chueadee, Chakat Pitakaso, Rapeepan Sala-Ngam, Sarinya Sriprateep, Keartisak Khonjun, Surajet Srithep, Yottha Kosacka-Olejnik, Monika |
Author_xml | – sequence: 1 givenname: Keartisak surname: Sriprateep fullname: Sriprateep, Keartisak organization: Manufacturing and Materials Research Unit (MMR), Department of Manufacturing Engineering, Faculty of Engineering, Maha Sarakham University, Maha Sarakham, Thailand – sequence: 2 givenname: Surajet orcidid: 0000-0002-2699-2162 surname: Khonjun fullname: Khonjun, Surajet organization: Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand – sequence: 3 givenname: Rapeepan orcidid: 0000-0002-5896-4895 surname: Pitakaso fullname: Pitakaso, Rapeepan organization: Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand – sequence: 4 givenname: Thanatkij orcidid: 0000-0001-7720-7630 surname: Srichok fullname: Srichok, Thanatkij email: thanatkij.s@ubu.ac.th organization: Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand – sequence: 5 givenname: Sarinya orcidid: 0000-0002-3575-7367 surname: Sala-Ngam fullname: Sala-Ngam, Sarinya organization: Manufacturing and Materials Research Unit (MMR), Department of Manufacturing Engineering, Faculty of Engineering, Maha Sarakham University, Maha Sarakham, Thailand – sequence: 6 givenname: Yottha orcidid: 0000-0002-7288-8219 surname: Srithep fullname: Srithep, Yottha organization: Manufacturing and Materials Research Unit (MMR), Department of Manufacturing Engineering, Faculty of Engineering, Maha Sarakham University, Maha Sarakham, Thailand – sequence: 7 givenname: Sarayut orcidid: 0000-0001-7179-7510 surname: Gonwirat fullname: Gonwirat, Sarayut organization: Department of Computer Engineering and Automation Kalasin University, Kalasin 46000, Thailand – sequence: 8 givenname: Peerawat orcidid: 0000-0002-1509-7779 surname: Luesak fullname: Luesak, Peerawat organization: Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Rai 57120, Thailand – sequence: 9 givenname: Surasak orcidid: 0009-0003-3380-6879 surname: Matitopanum fullname: Matitopanum, Surasak organization: Department of Industrial Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand – sequence: 10 givenname: Chakat surname: Chueadee fullname: Chueadee, Chakat organization: Department of Industrial Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand – sequence: 11 givenname: Rungwasun orcidid: 0000-0002-2524-1869 surname: Kraiklang fullname: Kraiklang, Rungwasun organization: Department of Industrial Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand – sequence: 12 givenname: Monika surname: Kosacka-Olejnik fullname: Kosacka-Olejnik, Monika organization: Faculty of Engineering Management, Poznan University of Technology, Poznan 60965, Poland |
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Keywords | Image segmentation Artificial multiple intelli-gence system (AMIS) Disease classification Cannabis leaf diseases Deep learning ensemble |
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SubjectTerms | Artificial multiple intelli-gence system (AMIS) Cannabis leaf diseases Deep learning ensemble Disease classification Image segmentation |
Title | Hybrid Adaptive Multiple Intelligence System (HybridAMIS) for classifying cannabis leaf diseases using deep learning ensembles |
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