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Nirmala, Baddala Vijaya
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An efficient segmentation using adaptive radial basis function neural network for tomato and mango plant leaf images Smitha, Jolakula Asoka; Shadaksharappa, Bichagal; Parvathy, Sheela; Veena, Kilingar; Jenifer, Albert; Nirmala, Baddala Vijaya; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp202-213

Abstract

Agriculture has become simply to feed ever-growing populations. The tomato is arguably the most well-known vegetable in agricultural areas and plays a significant role in the growth of vegetables in our daily lives. However, because this tomato has multiple diseases, image segmentation of the diseased leaf shows a key role in classifying the disease by the leaf's symptoms. Therefore, in this paper, an efficient plant disease segmentation using an adaptive radial basis function neural network (ARBFNN) classifier. The proposed radial basis function (RBF) neural network is enhanced by using the flower pollination algorithm (FPA). Firstly, the noise is detached by an adaptive median filter and histogram equalization. Then, from every leaf image, different kind of color features is extracted. After the extraction of features, those are fed to the segmentation phase to section the disease serving from the input image. The efficiency of the suggested method is analyzed based on various metrics and our technique attained a better accuracy of 97.58%.
Co-Authors Jenifer, Albert Murugan, Subbiah Parvathy, Sheela Shadaksharappa, Bichagal Smitha, Jolakula Asoka Veena, Kilingar