A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture
Cotton is known as ‘white gold’ in the agricultural industry. Agriculture is the primary source of economic income in Bangladesh and the country's economy is heavily dependent on agriculture. The soil and water resources of our country are fertile and the climate is moderate. But numerous disea...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-11-01
|
| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305323001035 |
| _version_ | 1797526188022824960 |
|---|---|
| author | Md. Manowarul Islam Md. Alamin Talukder Md. Ruhul Amin Sarker Md Ashraf Uddin Arnisha Akhter Selina Sharmin Md. Selim Al Mamun Sumon Kumar Debnath |
| author_facet | Md. Manowarul Islam Md. Alamin Talukder Md. Ruhul Amin Sarker Md Ashraf Uddin Arnisha Akhter Selina Sharmin Md. Selim Al Mamun Sumon Kumar Debnath |
| author_sort | Md. Manowarul Islam |
| collection | DOAJ |
| description | Cotton is known as ‘white gold’ in the agricultural industry. Agriculture is the primary source of economic income in Bangladesh and the country's economy is heavily dependent on agriculture. The soil and water resources of our country are fertile and the climate is moderate. But numerous diseases affect crop production and cause enormous crop losses, endangering the lives of helpless farmers. A previous report showed that about 70–80% of cotton diseases were leaf diseases and 30–20% were pest diseases. Experts typically use bare eyes to find and identify such plant diseases and pests which may result from lower accuracy of the identification. As a result, early detection of cotton disease using AI-based systems may help to increase the production of cotton by detecting the leaf disease significantly. In this research, we proposed a DL-based cotton leaf disease detection approach using fine-tuning Transfer Learning (TL) algorithms by tuning the layers and parameters of the existing TL algorithms. We also investigated the performance of several fine-tuning TL models such as VGG-16, VGG-19, Inception-V3 and Xception on the publicly available cotton dataset for cotton disease prediction. The investigations found that the Xception model provides the highest accuracy rate of 98.70% and was selected to develop a web-based smart application for real-life cotton disease prediction in farming to increase cotton production. Hence, our model can accurately diagnose cotton leaf diseases and will provide a new window for the automatic leaf disease diagnosis of other plants. |
| first_indexed | 2024-03-10T09:25:40Z |
| format | Article |
| id | doaj.art-6c69730c461f4a9bb746cef8a576687e |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-03-10T09:25:40Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-6c69730c461f4a9bb746cef8a576687e2023-11-22T04:49:35ZengElsevierIntelligent Systems with Applications2667-30532023-11-0120200278A deep learning model for cotton disease prediction using fine-tuning with smart web application in agricultureMd. Manowarul Islam0Md. Alamin Talukder1Md. Ruhul Amin Sarker2Md Ashraf Uddin3Arnisha Akhter4Selina Sharmin5Md. Selim Al Mamun6Sumon Kumar Debnath7Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh; Corresponding authors.Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh; Corresponding authors.Department of Computer Science and Engineering, Jagannath University, Dhaka, BangladeshDepartment of Computer Science and Engineering, Jagannath University, Dhaka, BangladeshDepartment of Computer Science and Engineering, Jagannath University, Dhaka, BangladeshDepartment of Computer Science and Engineering, Jagannath University, Dhaka, BangladeshDepartment of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, BangladeshDepartment of Electrical and Electronics Engineering, Begum Rokeya University, Rangpur, BangladeshCotton is known as ‘white gold’ in the agricultural industry. Agriculture is the primary source of economic income in Bangladesh and the country's economy is heavily dependent on agriculture. The soil and water resources of our country are fertile and the climate is moderate. But numerous diseases affect crop production and cause enormous crop losses, endangering the lives of helpless farmers. A previous report showed that about 70–80% of cotton diseases were leaf diseases and 30–20% were pest diseases. Experts typically use bare eyes to find and identify such plant diseases and pests which may result from lower accuracy of the identification. As a result, early detection of cotton disease using AI-based systems may help to increase the production of cotton by detecting the leaf disease significantly. In this research, we proposed a DL-based cotton leaf disease detection approach using fine-tuning Transfer Learning (TL) algorithms by tuning the layers and parameters of the existing TL algorithms. We also investigated the performance of several fine-tuning TL models such as VGG-16, VGG-19, Inception-V3 and Xception on the publicly available cotton dataset for cotton disease prediction. The investigations found that the Xception model provides the highest accuracy rate of 98.70% and was selected to develop a web-based smart application for real-life cotton disease prediction in farming to increase cotton production. Hence, our model can accurately diagnose cotton leaf diseases and will provide a new window for the automatic leaf disease diagnosis of other plants.http://www.sciencedirect.com/science/article/pii/S2667305323001035Cotton leafDiseaseDeep learningTransfer learningDiagnosePrediction |
| spellingShingle | Md. Manowarul Islam Md. Alamin Talukder Md. Ruhul Amin Sarker Md Ashraf Uddin Arnisha Akhter Selina Sharmin Md. Selim Al Mamun Sumon Kumar Debnath A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture Intelligent Systems with Applications Cotton leaf Disease Deep learning Transfer learning Diagnose Prediction |
| title | A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture |
| title_full | A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture |
| title_fullStr | A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture |
| title_full_unstemmed | A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture |
| title_short | A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture |
| title_sort | deep learning model for cotton disease prediction using fine tuning with smart web application in agriculture |
| topic | Cotton leaf Disease Deep learning Transfer learning Diagnose Prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2667305323001035 |
| work_keys_str_mv | AT mdmanowarulislam adeeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT mdalamintalukder adeeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT mdruhulaminsarker adeeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT mdashrafuddin adeeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT arnishaakhter adeeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT selinasharmin adeeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT mdselimalmamun adeeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT sumonkumardebnath adeeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT mdmanowarulislam deeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT mdalamintalukder deeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT mdruhulaminsarker deeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT mdashrafuddin deeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT arnishaakhter deeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT selinasharmin deeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT mdselimalmamun deeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture AT sumonkumardebnath deeplearningmodelforcottondiseasepredictionusingfinetuningwithsmartwebapplicationinagriculture |