• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 36, Pages: 2920-2928

Original Article

A Novel Algorithm for Identifying Organic Cereals by Optimal Features and Intelligent Classifiers

Received Date:17 July 2023, Accepted Date:25 August 2023, Published Date:27 September 2023


Objectives: To develop a suitable algorithm with artificial neural network, wavelet transforms for image classification problem using cereal dataset and to make a performance comparison against different features. The major objective of this study is to perform image classification on cereals dataset images. Methods: This study used Statistical classification and the fuzzy logic-based methods. Image classification is performed on the cereals dataset using morphological, color, and wavelet components with different features. 70 number of images used for testing and 30 number of images for training. The performance of the working of morphological, color, and wavelet components in classifying images from the cereals dataset is compared against different features namely major axis length, minor axis length, area, centroid, and perimeter. Findings: The study found that (Artificial Neural Network) ANN worked better with training accuracy of 95%, testing accuracy of 91% compared to MSVM (Multiclass support vector machine) and (K - Nearest neighbor) KNN algorithm. Novelty: This study presents a comparative aspect of image classification using morphological, color, and wavelet components using different features since not many studies or research articles showed the performance comparison of different classification methods along with different features. Since the real-world scenarios of today require enormous data to be processed, ANN can fit well to diversify applications since they highly reduce the number of parameters to be trained that speeds up the training process. Moreover, to be specific on image classification problems they require the best and most prominent features to be detected and uncovered. This can be achieved using ANN since it has the concept of classification using training and testing at its Core. Hence, ANN is highly recommended for such image classification applications than the traditional artificial-neural-networks because of the aforementioned reasons.

Keywords: Morphological; Wavelet transform; neural networks; Statistical classifier; Fuzzy logic


  1. Çelik Y, Başaran E, Dilay Y. Identification of durum wheat grains by using hybrid convolution neural network and deep features. Signal, Image and Video Processing. 2022;16(4):1135–1142. Available from: https://doi.org/10.1007/s11760-021-02094-y
  2. Wang L, Sun Y. Image classification using convolutional neural network with wavelet domain inputs. IET Image Processing. 2022;16(8):2037–2048. Available from: https://doi.org/10.1049/ipr2.12466
  3. Liu J, Li P, Tang X, Li J, Chen J. Research on improved convolutional wavelet neural network. Scientific Reports. 2021;11(1):1–14. Available from: https://doi.org/10.1038/s41598-021-97195-6
  4. Khatri A, Agrawal S, Chatterjee JM. Wheat Seed Classification: Utilizing Ensemble Machine Learning Approach. Scientific Programming. 2022;2022:1–9.
  5. Ibrahim S, Kamaruddin SBA, Zabidi A, Ghani NAM. Contrastive analysis of rice grain classification techniques: multi-class support vector machine vs artificial neural network. IAES International Journal of Artificial Intelligence (IJ-AI). 2020;9(4):616–622. Available from: http://doi.org/10.11591/ijai.v9.i4.pp616-622
  6. Bouguettaya A, Zarzour H, Kechida A, Taberkit AM. Deep learning techniques to classify agricultural crops through UAV imagery: a review. Neural Computing and Applications. 2022;34(12):9511–9536. Available from: https://doi.org/10.1007/s00521-022-07104-9
  7. Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors. 2021;21(11):1–55. Available from: https://doi.org/10.3390/s21113758
  8. Velesaca HO, Suárez PL, RM, ADS. Computer vision based food grain classification: A comprehensive survey. Computers and Electronics in Agriculture. 2021;187:1–13. Available from: https://doi.org/10.1016/j.compag.2021.106287


© 2023 Bhensjaliya & Dalwadi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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