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Enhancing Tea Plant Health Through Machine Learning: EfficientNet-B0 for Tea Sickness Detection

by Ali Raza, Muhammad Subhan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 45
Year of Publication: 2024
Authors: Ali Raza, Muhammad Subhan
10.5120/ijca2024924092

Ali Raza, Muhammad Subhan . Enhancing Tea Plant Health Through Machine Learning: EfficientNet-B0 for Tea Sickness Detection. International Journal of Computer Applications. 186, 45 ( Oct 2024), 32-43. DOI=10.5120/ijca2024924092

@article{ 10.5120/ijca2024924092,
author = { Ali Raza, Muhammad Subhan },
title = { Enhancing Tea Plant Health Through Machine Learning: EfficientNet-B0 for Tea Sickness Detection },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2024 },
volume = { 186 },
number = { 45 },
month = { Oct },
year = { 2024 },
issn = { 0975-8887 },
pages = { 32-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number45/enhancing-tea-plant-health-through-machine-learning-efficientnet-b0-for-tea-sickness-detection/ },
doi = { 10.5120/ijca2024924092 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-10-26T00:55:48.957339+05:30
%A Ali Raza
%A Muhammad Subhan
%T Enhancing Tea Plant Health Through Machine Learning: EfficientNet-B0 for Tea Sickness Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 45
%P 32-43
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Tea is a globally important crop that is prone to numerous diseases that can significantly affect its quality and production. This study proposes a novel work of improving the health of tea plants through Machine Learning (ML) for tea sickness detection from the EfficientNet-B0 convolutional neural network (CNN). By training the model on a comprehensive dataset of tea leaf images, significant improvements in disease detection accuracy were achieved. The architecture of the EfficientNet-B0 was well optimized explicitly for the use of this study and its test accuracy is 94.64%. This performance highlights the ability of the model as a classifier of the healthy and diseased tea leaves. EfficientNet-B0 is a promising solution to better manage and detect diseases in the initial stages, which confirmed the effectiveness when applied in this context. This stays in contrast to the traditional disease diagnosing approach in agriculture. This approach is an improvement by combining Deep Learning (DL) with real-life applications in farming.

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Index Terms

Computer Science
Information Sciences
Machine Learning (ML)
Neural Networks
Image Classification
Deep Learning (DL)
Computer Vision
Agriculture Technology

Keywords

Tea Sickness Detection EfficientNet-B0 Crop Disease Detection Convolutional Neural Networks (CNNs) Transfer Learning Plant Health Monitoring

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