Deep Learning Based Tomato PLDD

Deep Learning Based Tomato PLDD
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Bade Ashwini Vivekanand, M. Suresh Kumar
DOI : 10.14445/22315381/IJETT-V70I7P243

How to Cite?

Bade Ashwini Vivekanand, M. Suresh Kumar, "Deep Learning Based Tomato PLDD" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 414-421, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P243

Abstract
Agriculture sector is the prime source of food and industrial raw material that satisfies the increasing population demand and industrial revolution. However, plant leaf disease detection (PLDD) degrades the quality of food and agricultural products, leading to economic loss for farmers. Recently, many deep learning frameworks have been presented for the PLDD that has shown gigantic improvement over traditional machine learning-based leaf disease detection. The performance of these deep learning frameworks is often limited due to lower feature variability, data scarcity problem, and low accuracy for multiple plant disease detection. This article presents PLDD based on a deep convolutional neural network (DCNN) to improve the feature variability and disease detection accuracy. The effectiveness of the proposed approach is evaluated on tomato plants from the PlantVillage dataset. The proposed method provides 98.83% and 96.06% accuracy in 2-class and 9-class for PLDD.

Keywords
Agricultural Automation, PLDD, Deep Learning, Precision Agriculture, Convolutional Neural Network.

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