The Weed Plant Detection
V. Geetha1, C.K.Gomathy2, Y.Padmini Reddy3, V.Haripriya4
1Dr. V. Geetha, Assistant Professor in CSE Department, SCSVMV Deemed to be University, Kanchipuram, Tamil Nadu.
2Dr. CK Gomathy*, Assistant Professor in CSE Department, SCSVMV Deemed to be University, Kanchipuram, Tamil Nadu
3Ms. Y.Padmini Reddy , UG CSE Department, SCSVMV Deemed to be University, Kanchipuram, Tamil Nadu.
4Ms.V.Haripriya , UG CSE Department, SCSVMV Deemed to be University, Kanchipuram, Tamil Nadu.
Manuscript received on April 12, 2021. | Revised Manuscript received on April 26, 2021. | Manuscript published on April 30, 2021. | PP: 206-210 | Volume-10 Issue-4, April 2021. | Retrieval Number: 100.1/ijeat.D24540410421 | DOI: 10.35940/ijeat.D2454.0410421
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Knowledge about the distribution of weeds within the sector could also be prerequisite for the site-specific treatment. Optical sensors changes to detect vary weed densities and species which can have mapped using GPS data. Weeds are extracted from the pictures that are using the image processing and therefore the report by the form features. The classification supported the features reveal the type and therefore the number of weeds per the image. For the classification the sole maximum of sixteen features out of the eighty-one computed ones is employed. Which enables the optimal distinction of weed classes is used the choice is usually done using processing algorithms, which the speed discriminate of the features of prototypes. If no prototypes are available, clustering algorithms are often used to automatically generate clusters. Within the next step weed classes are often assigned to the clusters. Such procedure aids to select prototypes, which are completed manually. Classes are often identified, that are distinct within the feature space or which are overlapping, and thus not well separable. The clustering is usually utilized in some, less complex cases to work out automatic procedure for the classification. By using the system weed plants are generated. These are differentiating to the results of manual weeds sampling.
Keywords: GPS data, automatically generate clusters.
Scope of the Article: Clustering