Deep Learning Methods for Disease Identification of Cotton Plants / (Record no. 607255)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01737nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.892 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Fasihi, Sajeel |
| 245 ## - TITLE STATEMENT | |
| Title | Deep Learning Methods for Disease Identification of Cotton Plants / |
| Statement of responsibility, etc. | Sajeel Fasihi |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 79p. ; |
| Other physical details | Soft Copy |
| Dimensions | 30cm. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Cotton is a vital cash crop, contributing significantly to the global textile industry and the livelihoods of millions of farmers worldwide. However, diseases such<br/>as bacterial blight, leaf curl virus, and whitefly infestations pose a severe threat<br/>to cotton production and quality. Timely detection and accurate identification of<br/>these diseases are crucial for implementing effective control measures and ensuring<br/>crop health by exploring multiple state-of-the-art deep learning models, including<br/>CNNs and transformers. The research utilizes a diverse dataset of cotton plant<br/>images, encompassing healthy and diseased leaves, to train and fine-tune the deep<br/>learning models and Vision transformers. Additionally, we will focus on evaluating the models’ capability to detect varying intensities of whitefly infestations,<br/>which is critical for assessing disease severity and implementing appropriate control strategies. The models were cross-validated and regularized to improve the<br/>models working. This study has the potential to contribute significantly to the<br/>field of computer vision, particularly for cotton disease detection. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Karam Dad Kallu |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/37932">http://10.250.8.41:8080/xmlui/handle/123456789/37932</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 12/08/2023 | 629.892 | SMME-TH-916 | Thesis |