Vol. 23 No. 3 (2024): Mapana Journal of Sciences
Research Articles

Advancing Agriculture with CNN for Timely Leaf Disease Detection and Enhance Crop Production

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  • D Divya,
  • K Ananthajothi,
  • Maya Eapen
D Divya
Jerusalem College of Engineering
DOI: https://doi.org/10.12723/mjs.70.2

Published 2024-11-09

Keywords

  • Agriculture,
  • Crop production,
  • Disease Detection,
  • Deep Learning,
  • Convolution Neural Network,
  • Modified Deer Hunting Algorithm
    • ...More
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Copyright (c) 2024

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Abstract

The Indian economy relies heavily on the agricultural sector. Improving crop and plant yields is crucial because 60% of India's labour force is involved in this industry. It took until recently for Indian farmers to see increases in both production and selling prices due to a variety of crop-related ailments.  Modern picture identification systems, such as Convolutional Neural Networks, are able to make precise and rapid diagnoses. In order to correctly detect plant diseases, this article utilizes pre-trained models that are built on convolutional neural networks (CNNs). Tuned-hyper-parameters for ResNet50, DenseNet121, VGG16, and Inception V4 in particular. We also compared our results to those of other similar, state-of-the-art investigations. We can see that DenseNet-121 gets a success rate of 99.81% from the validation data. This paves the way for artificial intelligence solutions for smallholder farmers and shows that convolutional neural networks (CNNs) can classify plant illnesses.

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