| Type of Material: | Thesis |
| Title: | Precision Agricultural Early Stage Disease Detection In Plant Leaves Using Deep Learning |
| Researcher: | Nirmala Devi, S |
| Guide: | Muthukumaravel, A |
| Department: | Department of Computer Application |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2022 |
| Language: | English |
| Subject: | Computer Science | Computer Science Artificial Intelligence | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Computer Application, Bharath University, Chennai, Chennai; 2022; D16CA501 |
| Fulltext: | Shodhganga |
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| 001 | 455154 | |
| 003 | IN-AhILN | |
| 005 | 2024-09-23 16:10:31 | |
| 008 | __ | 240923t2022||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_455154 |
| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aNirmala Devi, S|eResearcher |
| 110 | __ | |aDepartment of Computer Application|bBharath University, Chennai|dChennai|ein|0U-0446 |
| 245 | __ | |aPrecision Agricultural Early Stage Disease Detection In Plant Leaves Using Deep Learning |
| 260 | __ | |aChennai|bBharath University, Chennai|c2022 |
| 300 | __ | |dDVD |
| 502 | __ | |cDepartment of Computer Application, Bharath University, Chennai, Chennai|d2022|oD16CA501|bPhD |
| 518 | __ | |d2022|oDate of Award |
| 520 | __ | |aDetecting plant disease in an earlier stage is one of the tasks where these can lead to heavy loss in cultivation. In such a way the research challenges are more likely to relate to the detection and diagnosis in plant diseases based on spots affected by many viruses that can be handled using deep learning techniques. Agricultural field relies on extracting many features from the dataset to understand the automation of identifying the diseases. Mostly machine learning is better in providing solutions for early diagnosis, even though there are many limitations such as computational cost and time. To overcome these limitations Deep learning models are introduced in identifying the diseases in plants. There is trained and tested data which are independent of classes and varieties such that image databases refer to the labeling for finding the severity. This work focuses on providing solutions by classifying, segmenting input from the images. By applying Convolutional neural network the comparison analysis with |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Artificial Intelligence |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aMuthukumaravel, A|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/456438|yShodhganga |
| 905 | __ | |afromsg |
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