| 000 -LEADER |
|---|
| fixed length control field |
02642nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Faisal, Shah |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Automatic Detection and Recognition of Citrus Fruits Diseases Using Deep Learning Model / |
| Statement of responsibility, etc. |
Shah Faisal |
| 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 |
2022. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
59p. |
| Other physical details |
Soft Copy |
| Dimensions |
30cm |
| 500 ## - GENERAL NOTE |
|---|
| General note |
In a country's economy, agriculture plays a very vital role. Agriculture's yield and production are<br/>reduced by plant diseases, resulting in significant economic losses and instability in the food<br/>market. In plants, the citrus fruit crop is one of the most important agricultural products in the<br/>world, produced and grown in around 140 countries. It has a lot of nutrients, such as vitamin C.<br/>However, due to pests and diseases, citrus cultivation is widely affected and suffers significant<br/>losses in both yield and quality. The majority of plant diseases exhibit visible symptoms, and the<br/>accepted method used today is for a skilled plant pathologist to detect the diseases by examining<br/>affected plant leaves under a microscope, which is a costly and time-consuming method. During<br/>the last decade, computer vision and machine learning have been widely adopted to detect and<br/>classify plant diseases, providing opportunities for early disease detection and bringing<br/>improvements to agricultural production. The early detection and accurate diagnosis of plant<br/>diseases are essential for reducing their spread and damage to crops. In this work, we presented an<br/>automatic system for early detection and recognition of citrus plant diseases based on a deep<br/>learning (DL) model to improve accuracy and reduce computational complexity. The most recent<br/>transfer learning-based models were applied to our dataset in order to increase classification<br/>accuracy. In this work, we successfully proposed a CNN-based pre-trained model (EfficientNetB3,<br/>ResNet50, MobiNetV2, (InceptionV3) for the identification and classification of citrus plant<br/>diseases using transfer learning. In order to assess the performance of the model, we found that the<br/>transfer of an EfficientNetb3 model led to the highest training, validating, and testing accuracies,<br/>which were 99.43%, 99.48%, and 99.58%, respectively. The proposed CNN model exceeds other<br/>cutting-edge CNN network architectures developed in earlier literature in the identification and<br/>categorization of citrus plant diseases. |
| 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. Kashif Javed |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="http://10.250.8.41:8080/xmlui/handle/123456789/31384">http://10.250.8.41:8080/xmlui/handle/123456789/31384</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Koha item type |
Thesis |
