Optimized Supervised ML for Medicinal Plant Detection : An FPGA Implementation

• Autorzy: Raghukumar, Amrutha M. , Narayanan, Gayathri , Somanathan, Geethu Remadevi
• Języki publikacji: EN

Abstrakty

EN

Medicinal plants have a huge significance today as it is the root resource to treat several ailments and medical disorders that do not find a satisfactory cure using allopathy. The manual and physical identification of such plants requires experience and expertise and it can be a gradual and cumbersome task, in addition to resulting in inaccurate decisions. In an attempt to automate this decision making, a data set of leaves of 10 medicinal plant species were prepared and the Gray-level Co-occurence Matrix (GLCM) features were extracted. From our earlier implementations of the several machine learning algorithms, the k-nearest neighbor (KNN) algorithm was identified as best suited for classification using MATLAB 2019a and has been adopted here. Based on the confusion matrices for various k values, the optimum k was selected and the hardware implementation was implemented for the classifier on FPGA in this work. An accuracy of 88.3% was obtained for the classifier from the confusion chart. A custom intellectual property (IP) for the design is created and its verification is done on the ZedBoard for three classes of plants.

Słowa kluczowe

EN

machine learning; GLCM; FPGA; intellectual property; medicinal plants

• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2024
• Tom: Vol. 70, No. 3
• Strony: 537--544
• Opis fizyczny: Bibliogr. 12 poz., rys.

Twórcy

autor

Raghukumar, Amrutha M.

• DFT Engineer at Anora Semiconductor Labs Pvt Ltd, Bengaluru, India,

autor

Narayanan, Gayathri

• Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India,

autor

Somanathan, Geethu Remadevi

• Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India,

Bibliografia

• [1] Sandeep Kumar, V. T. E., Leaf feature based approach for automated identification of medicinal plants, 2014 International Conference on Communication and Signal Processing, Melmaruvathur, India, 2014, pp. 210-214, https://doi.org/10.1109/ICCSP.2014.6949830.
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• [8] Venkataraman, M. N. D ., Computer vision based feature extraction of leaves for identification of medicinal values of plants, 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India, 2016, pp. 1-5, https://doi.org/10.1109/ICCIC.2016.7919637.
• [9] Nidhis, A. D., Pardhu, C. N. V., Reddy, K. C., Deepa, K., Cluster based paddy leaf disease detection, classification and diagnosis in crop health monitoring unit, Lecture Notes in Computational Vision and Biomechanics, vol 31. Springer, Cham, 2019. https://doi.org/10.1007/978-3-030-04061-129.
• [10] Madhuri Bandara, L. R., Texture dominant approach for identifying ayurveda herbal species using flowers, 2019 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2019, pp. 117-122, https://doi.org/10.1109/MERCon.2019.8818944.
• [11] Saleem, G., Akhtar, M., Ahmed, N., Qureshi, W.S., Automated analysis of visual leaf shape features for plant classification, Computers and Electronics in Agriculture, Vol.157, 2019, pp. 270-280, https://doi.org/10.1016/j.compag.2018.12.038
• [12] Raghukumar A.M., Narayanan, G., Comparison Of Machine Learning Algorithms For Detection Of Medicinal Plants, 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2020, pp. 56-60.

Identyfikatory

DOI

10.24425/ijet.2024.149576