Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification
Plants diseases have a detrimental effect on the quality but also on the quantity of agricultural production. However, the prediction of these diseases is proving the effect on crop quality and on reducing the risk of production losses. Indeed, the detection of plant diseases -either with a naked ey...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-01-01
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| Series: | Artificial Intelligence in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S258972172200006X |
| _version_ | 1811196710951583744 |
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| author | El Mehdi Raouhi Mohamed Lachgar Hamid Hrimech Ali Kartit |
| author_facet | El Mehdi Raouhi Mohamed Lachgar Hamid Hrimech Ali Kartit |
| author_sort | El Mehdi Raouhi |
| collection | DOAJ |
| description | Plants diseases have a detrimental effect on the quality but also on the quantity of agricultural production. However, the prediction of these diseases is proving the effect on crop quality and on reducing the risk of production losses. Indeed, the detection of plant diseases -either with a naked eye or using traditional methods- is largely a cumbersome process in terms of time, availability and results with a high-risk error. The present work introduces a depth study of various CNN architectures with different optimization algorithms carried out for olive disease detection using classification techniques that recommend the best model for constructing an effective disease detector. This study presents a dataset of 5571 olive leaf images collected manually on real conditions from different regions of Morocco, that also includes healthy class to detect olive diseases. Further, one of the goals of this research was to study the correlation effects between CNN architectures and optimization algorithms evaluated by the accuracy and other performance metrics. The highest rate in trained models was 100 %, while the highest rate in experiments without data augmentation was 92,59 %. Another subject of this study is the influence of the optimization algorithms on neuronal network performance. As a result of the experiments carried out, the MobileNet architecture using Rmsprop algorithms outperformed the others combinations in terms of performance and efficiency of disease detector. |
| first_indexed | 2024-04-12T01:03:33Z |
| format | Article |
| id | doaj.art-750ed289ba13431495879257fd8e3b9a |
| institution | Directory Open Access Journal |
| issn | 2589-7217 |
| language | English |
| last_indexed | 2024-04-12T01:03:33Z |
| publishDate | 2022-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj.art-750ed289ba13431495879257fd8e3b9a2022-12-22T03:54:21ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172022-01-0167789Optimization techniques in deep convolutional neuronal networks applied to olive diseases classificationEl Mehdi Raouhi0Mohamed Lachgar1Hamid Hrimech2Ali Kartit3LTI Laboratory, ENSA, Chouaib Doukkali University of El Jadida, El Jadida, Morocco; Corresponding author.LTI Laboratory, ENSA, Chouaib Doukkali University of El Jadida, El Jadida, MoroccoLAMSAD Laboratory, ENSA, Hassan First University, Berrechid, MoroccoLTI Laboratory, ENSA, Chouaib Doukkali University of El Jadida, El Jadida, MoroccoPlants diseases have a detrimental effect on the quality but also on the quantity of agricultural production. However, the prediction of these diseases is proving the effect on crop quality and on reducing the risk of production losses. Indeed, the detection of plant diseases -either with a naked eye or using traditional methods- is largely a cumbersome process in terms of time, availability and results with a high-risk error. The present work introduces a depth study of various CNN architectures with different optimization algorithms carried out for olive disease detection using classification techniques that recommend the best model for constructing an effective disease detector. This study presents a dataset of 5571 olive leaf images collected manually on real conditions from different regions of Morocco, that also includes healthy class to detect olive diseases. Further, one of the goals of this research was to study the correlation effects between CNN architectures and optimization algorithms evaluated by the accuracy and other performance metrics. The highest rate in trained models was 100 %, while the highest rate in experiments without data augmentation was 92,59 %. Another subject of this study is the influence of the optimization algorithms on neuronal network performance. As a result of the experiments carried out, the MobileNet architecture using Rmsprop algorithms outperformed the others combinations in terms of performance and efficiency of disease detector.http://www.sciencedirect.com/science/article/pii/S258972172200006XConvolutional neuronal networks (CNN)ClassificationOptimizationGradient descentPlant diseasesOlive dataset diseases (ODD) |
| spellingShingle | El Mehdi Raouhi Mohamed Lachgar Hamid Hrimech Ali Kartit Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification Artificial Intelligence in Agriculture Convolutional neuronal networks (CNN) Classification Optimization Gradient descent Plant diseases Olive dataset diseases (ODD) |
| title | Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification |
| title_full | Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification |
| title_fullStr | Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification |
| title_full_unstemmed | Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification |
| title_short | Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification |
| title_sort | optimization techniques in deep convolutional neuronal networks applied to olive diseases classification |
| topic | Convolutional neuronal networks (CNN) Classification Optimization Gradient descent Plant diseases Olive dataset diseases (ODD) |
| url | http://www.sciencedirect.com/science/article/pii/S258972172200006X |
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