Using image augmentation techniques and convolutional neural networks to identify insect infestations on tomatoes
Pest insects are a danger to both regional and global food security. In Jordan, the most productive crop is tomato. Jordan's agriculture output is threatened by insect infestations. The study intends to use a deep learning model called convolutional neural networks on a dataset that includes ei...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024175111 |
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| author | Moy'awiah Al-Shannaq Shahed N. Alkhateeb Mohammad Wedyan |
| author_facet | Moy'awiah Al-Shannaq Shahed N. Alkhateeb Mohammad Wedyan |
| author_sort | Moy'awiah Al-Shannaq |
| collection | DOAJ |
| description | Pest insects are a danger to both regional and global food security. In Jordan, the most productive crop is tomato. Jordan's agriculture output is threatened by insect infestations. The study intends to use a deep learning model called convolutional neural networks on a dataset that includes eight categories of insect pest images. A dataset was used and a group of images from reliable sources were added to it. The image collection was analyzed, and an image augmentation technique was used to increase the number of images, which reached 5894 after image augmentation. The data was split among 80 % training and 20 % validation. Convolutional Neural Networks trained on the data achieved 90 % training accuracy, 85 % testing accuracy, and 87 % validation accuracy. A high-accuracy deep learning model was developed that may be utilized on mobile applications to detect pests that affected crops to assist farmers. The original database used was small in size. When tested on deep learning and machine learning systems, the accuracy was very low, reaching 50–60 % without image augmentation, despite image enhancement techniques. |
| format | Article |
| id | doaj-art-2fcb4597edbd4a648fee41aab83b8177 |
| institution | DOAJ |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-2fcb4597edbd4a648fee41aab83b81772025-08-20T02:46:43ZengElsevierHeliyon2405-84402025-01-01111e4148010.1016/j.heliyon.2024.e41480Using image augmentation techniques and convolutional neural networks to identify insect infestations on tomatoesMoy'awiah Al-Shannaq0Shahed N. Alkhateeb1Mohammad Wedyan2Faculty of Information Technology and Computer Sciences, Yarmouk University, JordanFaculty of Information Technology and Computer Sciences, Yarmouk University, JordanCorresponding author.; Faculty of Information Technology and Computer Sciences, Yarmouk University, JordanPest insects are a danger to both regional and global food security. In Jordan, the most productive crop is tomato. Jordan's agriculture output is threatened by insect infestations. The study intends to use a deep learning model called convolutional neural networks on a dataset that includes eight categories of insect pest images. A dataset was used and a group of images from reliable sources were added to it. The image collection was analyzed, and an image augmentation technique was used to increase the number of images, which reached 5894 after image augmentation. The data was split among 80 % training and 20 % validation. Convolutional Neural Networks trained on the data achieved 90 % training accuracy, 85 % testing accuracy, and 87 % validation accuracy. A high-accuracy deep learning model was developed that may be utilized on mobile applications to detect pests that affected crops to assist farmers. The original database used was small in size. When tested on deep learning and machine learning systems, the accuracy was very low, reaching 50–60 % without image augmentation, despite image enhancement techniques.http://www.sciencedirect.com/science/article/pii/S2405844024175111AgricultureCrop diseaseNew technologiesAugmented realityDeep learningConvolutional neural networks |
| spellingShingle | Moy'awiah Al-Shannaq Shahed N. Alkhateeb Mohammad Wedyan Using image augmentation techniques and convolutional neural networks to identify insect infestations on tomatoes Heliyon Agriculture Crop disease New technologies Augmented reality Deep learning Convolutional neural networks |
| title | Using image augmentation techniques and convolutional neural networks to identify insect infestations on tomatoes |
| title_full | Using image augmentation techniques and convolutional neural networks to identify insect infestations on tomatoes |
| title_fullStr | Using image augmentation techniques and convolutional neural networks to identify insect infestations on tomatoes |
| title_full_unstemmed | Using image augmentation techniques and convolutional neural networks to identify insect infestations on tomatoes |
| title_short | Using image augmentation techniques and convolutional neural networks to identify insect infestations on tomatoes |
| title_sort | using image augmentation techniques and convolutional neural networks to identify insect infestations on tomatoes |
| topic | Agriculture Crop disease New technologies Augmented reality Deep learning Convolutional neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024175111 |
| work_keys_str_mv | AT moyawiahalshannaq usingimageaugmentationtechniquesandconvolutionalneuralnetworkstoidentifyinsectinfestationsontomatoes AT shahednalkhateeb usingimageaugmentationtechniquesandconvolutionalneuralnetworkstoidentifyinsectinfestationsontomatoes AT mohammadwedyan usingimageaugmentationtechniquesandconvolutionalneuralnetworkstoidentifyinsectinfestationsontomatoes |