JutePestDetect: An intelligent approach for jute pest identification using fine-tuned transfer learning
In certain Asian countries, Jute is one of the primary sources of income and Gross Domestic Product (GDP) for the agricultural sector. Like many other crops, Jute is prone to pest infestations, and its identification is typically made visually in countries like Bangladesh, India, Myanmar, and China....
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375523001089 |
| _version_ | 1797770083904258048 |
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| author | Md. Simul Hasan Talukder Mohammad Raziuddin Chowdhury Md Sakib Ullah Sourav Abdullah Al Rakin Shabbir Ahmed Shuvo Rejwan Bin Sulaiman Musarrat Saberin Nipun Muntarin Islam Mst Rumpa Islam Md Aminul Islam Zubaer Haque |
| author_facet | Md. Simul Hasan Talukder Mohammad Raziuddin Chowdhury Md Sakib Ullah Sourav Abdullah Al Rakin Shabbir Ahmed Shuvo Rejwan Bin Sulaiman Musarrat Saberin Nipun Muntarin Islam Mst Rumpa Islam Md Aminul Islam Zubaer Haque |
| author_sort | Md. Simul Hasan Talukder |
| collection | DOAJ |
| description | In certain Asian countries, Jute is one of the primary sources of income and Gross Domestic Product (GDP) for the agricultural sector. Like many other crops, Jute is prone to pest infestations, and its identification is typically made visually in countries like Bangladesh, India, Myanmar, and China. In addition, this method is time-consuming, challenging, and somewhat imprecise, which poses a substantial financial risk. To address this issue, the study proposes a high-performing and resilient transfer learning (TL) based JutePestDetect model to identify jute pests at the early stage. Firstly, we prepared jute pest dataset containing 17 classes and around 380 photos per pest class, which were evaluated after manual and automatic pre-processing and cleaning, such as background removal and resizing. Subsequently, five prominent pre-trained models—DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50—were selected from a previous study to design the JutePestDetect model. Each model was revised by replacing the classification layer with a global average pooling layer and incorporating a dropout layer for regularization. To evaluate the models' performance, various metrics such as precision, recall, F1 score, ROC curve, and confusion matrix were employed. These analyses provided additional insights for determining the efficacy of the models. Among them, the customized regularized DenseNet201-based proposed JutePestDetect model outperformed the others, achieving an impressive accuracy of 99%. As a result, our proposed method and strategy offer an enhanced approach to pest identification in the case of Jute, which can significantly benefit farmers worldwide. |
| first_indexed | 2024-03-12T21:17:20Z |
| format | Article |
| id | doaj.art-ef182bde66394d92a9b314f2ed0f619d |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-03-12T21:17:20Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-ef182bde66394d92a9b314f2ed0f619d2023-07-29T04:36:07ZengElsevierSmart Agricultural Technology2772-37552023-10-015100279JutePestDetect: An intelligent approach for jute pest identification using fine-tuned transfer learningMd. Simul Hasan Talukder0Mohammad Raziuddin Chowdhury1Md Sakib Ullah Sourav2Abdullah Al Rakin3Shabbir Ahmed Shuvo4Rejwan Bin Sulaiman5Musarrat Saberin Nipun6Muntarin Islam7Mst Rumpa Islam8Md Aminul Islam9Zubaer Haque10Electrical and Electronic Engineering, Rajshahi university of Engineering and Technology, Rajshahi, BangladeshJahangirnagar University, BangladeshShandong University of Finance and Economics, ChinaBRAC University, BangladeshUniversity of Rostock, GermanyNorthumbria University, UK; Corresponding author.Brunel University London, UKTechnical University of Denmark, DenmarkSoutheast University, BangladeshOxford Brookes University, UKBrandenburg University of Technology, GermanyIn certain Asian countries, Jute is one of the primary sources of income and Gross Domestic Product (GDP) for the agricultural sector. Like many other crops, Jute is prone to pest infestations, and its identification is typically made visually in countries like Bangladesh, India, Myanmar, and China. In addition, this method is time-consuming, challenging, and somewhat imprecise, which poses a substantial financial risk. To address this issue, the study proposes a high-performing and resilient transfer learning (TL) based JutePestDetect model to identify jute pests at the early stage. Firstly, we prepared jute pest dataset containing 17 classes and around 380 photos per pest class, which were evaluated after manual and automatic pre-processing and cleaning, such as background removal and resizing. Subsequently, five prominent pre-trained models—DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50—were selected from a previous study to design the JutePestDetect model. Each model was revised by replacing the classification layer with a global average pooling layer and incorporating a dropout layer for regularization. To evaluate the models' performance, various metrics such as precision, recall, F1 score, ROC curve, and confusion matrix were employed. These analyses provided additional insights for determining the efficacy of the models. Among them, the customized regularized DenseNet201-based proposed JutePestDetect model outperformed the others, achieving an impressive accuracy of 99%. As a result, our proposed method and strategy offer an enhanced approach to pest identification in the case of Jute, which can significantly benefit farmers worldwide.http://www.sciencedirect.com/science/article/pii/S2772375523001089JutepestdetectTransfer learningAugmentationDensenet201Inceptionv3mobilenetv2 |
| spellingShingle | Md. Simul Hasan Talukder Mohammad Raziuddin Chowdhury Md Sakib Ullah Sourav Abdullah Al Rakin Shabbir Ahmed Shuvo Rejwan Bin Sulaiman Musarrat Saberin Nipun Muntarin Islam Mst Rumpa Islam Md Aminul Islam Zubaer Haque JutePestDetect: An intelligent approach for jute pest identification using fine-tuned transfer learning Smart Agricultural Technology Jutepestdetect Transfer learning Augmentation Densenet201 Inceptionv3 mobilenetv2 |
| title | JutePestDetect: An intelligent approach for jute pest identification using fine-tuned transfer learning |
| title_full | JutePestDetect: An intelligent approach for jute pest identification using fine-tuned transfer learning |
| title_fullStr | JutePestDetect: An intelligent approach for jute pest identification using fine-tuned transfer learning |
| title_full_unstemmed | JutePestDetect: An intelligent approach for jute pest identification using fine-tuned transfer learning |
| title_short | JutePestDetect: An intelligent approach for jute pest identification using fine-tuned transfer learning |
| title_sort | jutepestdetect an intelligent approach for jute pest identification using fine tuned transfer learning |
| topic | Jutepestdetect Transfer learning Augmentation Densenet201 Inceptionv3 mobilenetv2 |
| url | http://www.sciencedirect.com/science/article/pii/S2772375523001089 |
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