An enhanced lightweight T-Net architecture based on convolutional neural network (CNN) for tomato plant leaf disease classification

Amreen Batool; Jisoo Kim; Sang-Joon Lee; Ji-Hyeok Yang; Yung-Cheol Byun

doi:10.7717/peerj-cs.2495

An enhanced lightweight T-Net architecture based on convolutional neural network (CNN) for tomato plant leaf disease classification

PeerJ Comput Sci. 2024 Dec 2:10:e2495. doi: 10.7717/peerj-cs.2495. eCollection 2024.

Authors

Amreen Batool¹, Jisoo Kim², Sang-Joon Lee³, Ji-Hyeok Yang⁴, Yung-Cheol Byun⁵

Affiliations

¹ Electronic Engineering, Jeju National University, Jeju, Republic of South Korea.
² Institute of Information Science & Technology, Jeju National University, Jeju, Republic of South Korea.
³ Department of Computer Engineering, Jeju National University, Jeju, Republic of South Korea.
⁴ Nanoom Energy Co. Ltd, Jeju-si, Jeju-do, Republic of South Korea.
⁵ Computer Engineering/ Electronic Engineering, Jeju National University, Jeju, Republic of South Korea.

Abstract

Tomatoes are a widely cultivated crop globally, and according to the Food and Agriculture Organization (FAO) statistics, tomatoes are the third after potatoes and sweet potatoes. Tomatoes are commonly used in kitchens worldwide. Despite their popularity, tomato crops face challenges from several diseases, which reduce their quality and quantity. Therefore, there is a significant problem with global agricultural productivity due to the development of diseases related to tomatoes. Fusarium wilt and bacterial blight are substantial challenges for tomato farming, affecting global economies and food security. Technological breakthroughs are necessary because existing disease detection methods are time-consuming and labor-intensive. We have proposed the T-Net model to find a rapid, accurate approach to tackle the challenge of automated detection of tomato disease. This novel deep learning model utilizes a unique combination of the layered architecture of convolutional neural networks (CNNs) and a transfer learning model based on VGG-16, Inception V3, and AlexNet to classify tomato leaf disease. Our suggested T-Net model outperforms earlier methods with an astounding 98.97% accuracy rate. We prove the effectiveness of our technique by extensive experimentation and comparison with current approaches. This study offers a dependable and understandable method for diagnosing tomato illnesses, marking a substantial development in agricultural technology. The proposed T-Net-based framework helps protect crops by providing farmers with practical knowledge for managing disease. The source code can be accessed from the given link.

Keywords: AlexNet; Convolutional neural networks (CNNs); Deep learning; Inception V3; T-Net architecture; Tomato Leaf; Tomato disease; Tomato plant leaf disease; Transfer learning; VGG-16.

Grants and funding

This research was supported by the “Regional Innovation Strategy (RIS)” through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE)(2023RIS-009). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.