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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Managuli, Manjunath
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Computer Science & IT Electrical & Electronics Engineering

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Artificial neural network-based intelligent sensor-based electronic nose for food applications Managuli, Manjunath; Bagyalakshmi, Kalimuthu; Shiny Malar, Francis Rosy; Rubia, Jebaraj Jency; Iderus, Samat
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp163-173

Abstract

Food commerce, especially for the general public, is greatly impacted by the capacity to identify and recognize chemical samples for food applications. Every chemical sample response has a unique, distinguishing smell. These advancements highlight the method of an artificial neural networks (ANN) to distinguish the distinctive fragrance from the reaction of substances. The categorization of various smell patterns has diminished confidence in ANN technology. Using an ANN technique and a sensor-based e-nose system for food applications, each chemical’s identification has been done commercially. The system comprises a 5-gas sensor selection that recognizes chemical talk while allowing for an improvement in permitting while falling gas is planned outside. To build a model of a different signal reaction, individual sensors are equally collected and merged into the innovation -favored sensor array. Demonstrates how it is related to the chemical test. The e-nose categorization has been tested with five different chemical samples and five different sensor classes. The e-nose approach, which comprises five sensors, can classify each chemical reaction model, starting with the results. With more sensors being employed, the classification accuracy of the precise chemical reaction improves. These data demonstrate that the ANN-based e-nose method promises a successful classification system for chemical sample responses for a characteristic odor sample.
Deep feature representation for automated plant species classification from leaf images Inamdar, Nikhil; Managuli, Manjunath; Patil, Uttam
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3759-3768

Abstract

Automated plant species classification using leaf images holds immense potential for advancing agricultural research, biodiversity conservation, and ecological monitoring. This study introduces a novel approach leveraging deep feature representation to achieve accurate and efficient classification based on leaf morphology. Convolutional neural networks (CNNs), including VGG16, ResNet50, DenseNet1, Inception, and Xception, are employed to extract high-level features from leaf images, capturing intricate patterns essential for species differentiation. To manage the extensive feature set extracted by these models, optimization techniques such as principal component analysis (PCA), variance thresholding, and recursive feature elimination (RFE) are applied. These methods streamline the feature set, making the classification process more efficient. The optimized features are then trained using classifiers like support vector machine (SVM), k-nearest neighbors (K-NN), decision trees (DT), and naive Bayes (NB), achieving average accuracies of 98.6%, 96.6%, 99.6%, and 99.7%, respectively, across various cross-validation methods. Experimental results on benchmark datasets demonstrate the effectiveness of this approach, achieving state-of-the-art performance in plant species classification. This work underscores the potential of deep feature representation in automated plant species classification, offering valuable insights for applications in agriculture, ecology, and environmental science.
Co-Authors Bagyalakshmi, Kalimuthu Iderus, Samat Inamdar, Nikhil Patil, Uttam Rubia, Jebaraj Jency Shiny Malar, Francis Rosy