An Automatic Symptom Based Citrus Plant Disease Detection Using Various Feature Descriptors
| dc.contributor.guide | Sharma, Tripti and Goyal, Bhawna | |
| dc.coverage.spatial | ||
| dc.creator.researcher | Kaur, Bobbinpreet | |
| dc.date.accessioned | 2022-04-11T06:25:30Z | |
| dc.date.available | 2022-04-11T06:25:30Z | |
| dc.date.awarded | ||
| dc.date.completed | 2020 | |
| dc.date.registered | ||
| dc.description.abstract | In recent times, the images and videos have emerged as one of the most important newlineinformation source depicting the real time scenarios. Digital images nowadays serve as newlineinput for many applications and replacing the manual methods due to their capabilities of newline3D scene representation in 2D plane. The capabilities of digital images along with newlineutilization of machine learning methodologies are showing promising accuracies in many newlineapplications of prediction and pattern recognition. One of the application fields pertains to newlinedetection of diseases occurring in the plants, which are destroying the widespread fields. newline | |
| dc.description.note | ||
| dc.format.accompanyingmaterial | DVD | |
| dc.format.dimensions | ||
| dc.format.extent | ||
| dc.identifier.uri | http://hdl.handle.net/10603/373061 | |
| dc.language | English | |
| dc.publisher.institution | Dept of Electronics and Communication Engineering | |
| dc.publisher.place | Mohali | |
| dc.publisher.university | Chandigarh University | |
| dc.relation | ||
| dc.rights | university | |
| dc.source.university | University | |
| dc.subject.keyword | Engineering | |
| dc.subject.keyword | Engineering and Technology | |
| dc.subject.keyword | Engineering Electrical and Electronic | |
| dc.title | An Automatic Symptom Based Citrus Plant Disease Detection Using Various Feature Descriptors | |
| dc.title.alternative | ||
| dc.type.degree | Ph.D. |
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