A convolutional neural network with average pooling for ‎chickpea disease detection and classification

Authors

  • CH. Srilakshmi Department of Computer Engineering & Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India
  • Gudepu Sridevi Department of Mechanical Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, Andhra Pradesh,
  • Mummidi Krishnaveni Department of Computer Science and Engineering (AI&ML), Anil Neerukonda Institute of Technology and Sciences(A), Sangivalasa, ‎Visakhapatnam, Andhra Pradesh, India
  • Ch Sekhar Department of Computer Science and Engineering (AI&ML), GMR Institute of Technology(A), Rajam, Andhra Pradesh, India
  • J. Vamsinath Department of Computer Science and Engineering, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher ‎ Education, Hyderabad, Telangana, India
  • Nagamalli Arasavalli Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, KL (Deemed to be ‎ University) Vaddeswaram, Guntur District, Andhra Pradesh, India
  • Mirtipati Satish Kumar Department of Computer Science and Engineering, CENTURION University of Technology and Management, Vizianagaram, ‎ Andhra Pradesh, India

How to Cite

Srilakshmi , C. ., Sridevi, G. . ., Krishnaveni , M. ., Sekhar , C. ., Vamsinath , J. ., Arasavalli , N. ., & Kumar , M. S. . (2025). A convolutional neural network with average pooling for ‎chickpea disease detection and classification. International Journal of Basic and Applied Sciences, 14(1), 156-165. https://doi.org/10.14419/pr4acd16

Received date: April 11, 2025

Accepted date: April 30, 2025

Published date: May 2, 2025

DOI:

https://doi.org/10.14419/pr4acd16

Keywords:

Alexnet; Average Pooling; Chickpea Disease; Convolutional Neural Network; Image Denoising

Abstract

Chickpeas rank among the top legumes worldwide, yet diseases can hit them hard, slashing both quality and yield. Accurate disease classification matters a lot for managing them well. In the Proposed research work use a Convolutional Neural Network (CNN) with Average ‎Pooling to identify and categorize diseases of chickpea plants. Primary Data used in this work are Chickpea and Fusarium-22 datasets Pre‎Pre-processing was done through demonising to maintain the purity of the data. To balance the classes, Data aug-mentation is done, which boosts the dataset ‎size, with key features drawn out using the AlexNet architecture, before the CNN-Average Pooling steps in to classify the diseases. The ‎CNN automatically snatches crucial details from chickpea images, while the Average Pooling layer slices down spatial dimensions to interesting patterns that are more useful. This arrangement brings precise disease classification, making it beneficial for agriculture and crop management. Tests show the CNN-Average Pooling hitting 99.60% accuracy on the Chickpea dataset and 99.53% on Fusarium-22, exceeding ‎options like CNN-LSTM, the GLCM-Color Histogram combo, and DenseNet-201‎

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How to Cite

Srilakshmi , C. ., Sridevi, G. . ., Krishnaveni , M. ., Sekhar , C. ., Vamsinath , J. ., Arasavalli , N. ., & Kumar , M. S. . (2025). A convolutional neural network with average pooling for ‎chickpea disease detection and classification. International Journal of Basic and Applied Sciences, 14(1), 156-165. https://doi.org/10.14419/pr4acd16

Received date: April 11, 2025

Accepted date: April 30, 2025

Published date: May 2, 2025