Open Access

ARTICLE

Enhanced Disease Identification Model for Tea Plant Using Deep Learning

Santhana Krishnan Jayapal¹, Sivakumar Poruran^2,*

1 Department of Electronics and Communication Engineering, University College of Engineering, Thirukkuvalai, Nagapattinam, Tamilnadu, 610204, India
2 Department of Electronics and Communication Engineering, Dr. NGP Institute of Technology, Coimbatore, Tamilnadu, 641048, India

* Corresponding Author: Sivakumar Poruran. Email:

Intelligent Automation & Soft Computing 2023, 35(1), 1261-1275. https://doi.org/10.32604/iasc.2023.026564

Received 30 December 2021; Accepted 27 March 2022; Issue published 06 June 2022

Abstract

Tea plant cultivation plays a significant role in the Indian economy. The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant. Various climatic factors and other parameters cause these diseases. In this paper, the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is healthy. Automation in image retrieval is a hot topic in the industry as it doesn’t require any form of metadata related to the images for storing or retrieval. Deep Hashing with Integrated Autoencoders is our proposed method for image retrieval in Tea Leaf images. It is an efficient and flexible way of retrieving Tea Leaf images. It has an integrated autoencoder which makes it better than the state-of-the-art methods giving better results for the MAP (mean average precision) scores, which is used as a parameter to judge the efficiency of the model. The autoencoders used with skip connections increase the weightage of the prominent features present in the previous tensor. This constitutes a hybrid model for hashing and retrieving images from a tea leaf data set. The proposed model will examine the input tea leaf image and identify the type of tea leaf disease. The relevant image will be retrieved based on the resulting type of disease. This model is only trained on scarce data as a real-life scenario, making it practical for many applications.

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Keywords

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