International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Leaf Recognition Based on Color and Shape Using ADNet

Author(s) Prof. Revathy B D, Prof. Harshith Kashyap, Prof. Harsha K G
Country India
Abstract To know the details of plants and leaves around us is of great importance medicinally and economically. Traditionally, plants are categorized by taxonomists through investigation of various parts of the plant. At the same time, most of the plants can be classified and identified based on the leaf shape and associated features. in this paper we USES Image processing techniques to extract leaf color, shape and GLCM features such as mean, standard deviation, homogeneity, correlation, aspect ratio, width ratio, moment ratio, apex angle, apex ratio, base angle, centroid deviation ratio and circularity. This paper proposes an adaptive visual tracking algorithm based on key frame selection and reinforcement learning (RL) Method. At the beginning, the probability value of the RL network output is analysed, and the predicted value of output is normalized. The proposed technic uses only a fine-tuned in key frames to obtain multiple fixed prediction models. The experiment is conducted on 75 video sequences of the Object Tracking Benchmark to verify the effectiveness of key frame selection strategy, and it is Compared with the original reinforcement learning based tracking algorithm.
Keywords Keywords- Reinforcement learning, adaptive visual tracking, target appearance, ADNet algorithm, key frame selection.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-13
Cite This Leaf Recognition Based on Color and Shape Using ADNet - Prof. Revathy B D, Prof. Harshith Kashyap, Prof. Harsha K G - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32782
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.32782
Short DOI https://doi.org/g8wkn2
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