Enhanced Disease Identification Model for Tea Plant Using Deep Learning
作者机构：Department of Electronics and Communication EngineeringUniversity College of EngineeringThirukkuvalaiNagapattinamTamilnadu610204India Department of Electronics and Communication EngineeringDr.NGP Institute of TechnologyCoimbatoreTamilnadu641048India
出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)
年 卷 期：2023年第35卷第1期
页 面：1261-1275页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Image retrieval autoencoders deep hashing plant disease tea leaf blister blight
摘 要：Tea plant cultivation plays a significant role in the Indian *** Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea *** climatic factors and other parameters cause these *** this paper,the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is *** 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 *** Hashing with Integrated Autoencoders is our proposed method for image retrieval in Tea Leaf *** is an efficient andflexible way of retrieving Tea Leaf *** 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 *** autoencoders used with skip connections increase the weightage of the prominent features present in the previous *** constitutes a hybrid model for hashing and retrieving images from a tea leaf data *** proposed model will examine the input tea leaf image and identify the type of tea leaf *** relevant image will be retrieved based on the resulting type of *** model is only trained on scarce data as a real-life scenario,making it practical for many applications.
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