Publication: Ensemble methods with deep convolutional neural networks for plant leaf recognition
| dc.contributor.author | Chompookham, Thipwimon | |
| dc.contributor.author | Surinta, Olarik | |
| dc.date.accessioned | 2023-12-16T10:56:57Z | |
| dc.date.available | 2023-12-16T10:56:57Z | |
| dc.date.issued | 2021 | |
| dc.date.issuedBE | 2564 | |
| dc.description.abstract | Recognition of plant leaves and diseases from images is a challenging task in computer vision and machine learning. This is because various problems directly affect the performance of the system; such as the leaf structure; differences of the intra-class; similarity of shape between inter-class; perspective of the image; and even recording time. In this paper; we propose the ensemble convolutional neural network (CNN) method to tackle these issues and improve plant leaf recognition performance. We trained five CNN models: MobileNetV1; MobileNetV2; NASNetMobile; DenseNet121; and Xception; accordingly to discover the best CNN based model. Ensemble methods; unweighted average; weighted average; and unweighted majority vote methods were then applied to the CNN output probabilities of each model. We have evaluated these ensemble CNN methods on a mulberry leaf dataset and two leaf disease datasets: tomato and corn leaf disease. As a result; the individual CNN model shows that MobileNetV2 outperforms every CNN model with an accuracy of 91.19% on the mulberry leaf dataset. The Xception combined with data augmentation techniques (Height Shift+Vertical Flip+Fill Mode) obtains an accuracy of 91.77%. We achieved very high accuracy above 99% from the DenseNet121 and Xception models on the leaf disease datasets. For the ensemble CNNs method; we selected the based models according to the best CNN models and predicted the output of each CNN with the weighted average ensemble method. The results showed that 3-Ensemble CNNs (3-EnsCNNs) performed better on plant leaf disease datasets; while 5-EnsCNNs outperforms on the mulberry leaf dataset. Surprisingly; the data augmentation technique did not affect the ensemble CNNs on the mulberry leaf and corn leaf disease datasets. On the other hand; application of data augmentation was slightly better than without only on the tomato leaf disease dataset. | |
| dc.identifier.doi | 10.24507/icicel.15.06.553 | |
| dc.identifier.uri | https://harrt.in.th/handle/123456789/8521 | |
| dc.language.iso | en | |
| dc.subject | การจำแนกพันธ์ไม้ | |
| dc.subject | โครงข่ายประสาทเทียมแบบคอนโวลูชันเชิงลึก | |
| dc.subject | วิธีการเรียนรู้แบบรวมกลุ่ม | |
| dc.subject | Plant Leaf Recognition | |
| dc.subject | Convolutional Neural Network | |
| dc.subject | Ensemble Method | |
| dc.subject | Ensemble Learning Method | |
| dc.subject | Ensemble Convolutional Neural Network | |
| dc.subject.isced | 0322 บรรณารักษ์, สารสนเทศ และการศึกษาจดหมายเหตุ | |
| dc.subject.oecd | 5.8 นิเทศศาสตร์และสื่อสารมวลชน | |
| dc.title | Ensemble methods with deep convolutional neural networks for plant leaf recognition | |
| dc.type | บทความวารสาร (Journal Article) | |
| dspace.entity.type | Publication | |
| harrt.researchArea | สารสนเทศศาสตร์ | |
| harrt.researchGroup | บรรณารักษศาสตร์และสารสนเทศศาสตร์ | |
| harrt.researchTheme.1 | Data Science | |
| harrt.researchTheme.2 | Machine Learning | |
| mods.location.url | https://www.researchgate.net/publication/351022816_Ensemble_Methods_with_Deep_Convolutional_Neural_Networks_for_Plant_Leaf_Recognition | |
| oaire.citation.endPage | 565 | |
| oaire.citation.issue | 6 | |
| oaire.citation.startPage | 553 | |
| oaire.citation.title | ICIC Express Letters | |
| oaire.citation.volume | 15 | |
| oairecerif.author.affiliation | มหาวิทยาลัยมหาสารคาม. คณะวิทยาการสารสนเทศ. ภาควิชาเทคโนโลยีสารสนเทศ |