In Bangladesh, tomatoes are a staple vegetable, prized for their versatility in various culinary applications. However, the cultivation of tomatoes is often hindered by a range of diseases that can significantly reduce crop yields and quality. Early detection of these diseases is crucial for implementing timely interventions and ensuring the sustainability of tomato production. Traditional manual inspection methods, while effective, are labor-intensive and prone to human error. To address these challenges, this research paper sought to develop an automated disease detection system using Convolutional Neural Networks (CNNs). A comprehensive dataset of tomato leaves was collected from the Brahmanbaria district, preprocessed to enhance image quality, and then applied to various deep learning models. Comparative performance analysis was conducted between YOLOv5, MobileNetV2, ResNet18, and our custom CNN model. In our study, the Custom CNN model achieved an impressive accuracy of 95.2%, significantly outperforming the other models, which achieved an accuracy of 77%, 89.38% and 71.88% respectively. While other models showed solid performance, our Custom CNN demonstrated superior results specifically tailored for the task of tomato leaf disease detection. These findings highlight the strong potential of deep learning techniques for improving early disease detection in tomato crops. By leveraging these advanced technologies, farmers can gain valuable insights to detect diseases at an early stage, allowing for more effective management practices. This approach not only promises to boost tomato yields but also contributes to the sustainability and resilience of the agricultural sector, helping to mitigate the impact of plant diseases on crop production.
Sabaragamuwa University of Sri Lanka（斯里兰卡萨伯勒格穆沃大学）
Automatic tomato disease recognition from leaf images is vital to avoid crop losses by applying control measures on time. Even though recent deep learning-based tomato disease recognition methods with classical training procedures showed promising recognition results, they demand large labelled data and involve expensive training. The traditional deep learning models proposed for tomato disease recognition also consume high memory and storage because of a high number of parameters. While lightweight networks overcome some of these issues to a certain extent, they continue to show low performan…
To ensure global food security and the overall profit of stakeholders, the importance of correctly detecting and classifying plant diseases is paramount. In this connection, the emergence of deep learning-based image classification has introduced a substantial number of solutions. However, the applicability of these solutions in low-end devices requires fast, accurate, and computationally inexpensive systems. This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with …
Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD
University of Biskra（比斯克拉大学）；University Center of Barika（巴里卡大学中心）；College of Engineering and Information Technology, University of Dubai（迪拜大学工程与信息技术学院）；University of Sharjah（沙迦大学）；Institute of Artificial Intelligence, De Montfort University（德蒙福特大学人工智能学院）
One of the most important agricultural products in Mexico is the tomato (Solanum lycopersicum), which occupies the 4th place national most produced product . Therefore, it is necessary to improve its production, building automatic detection system that detect, classify an keep tacks of the fruits is one way to archieve it. So, in this paper, we address the design of a computer vision system to detect tomatoes at different ripening stages. To solve the problem, we use a neural network-based model for tomato classification and detection. Specifically, we use the YOLOv3-tiny model because it is o…
TCLeaf-Net: a transformer-convolution framework with global-local attention for robust in-field lesion-level plant leaf disease detection
School of Optoelectronic Science and Engineering, South China Normal University（光电科学与工程学院，华南师范大学）；College of Engineering Physics, Shenzhen Technology University（工程物理学院，深圳科技大学）；Faculty of Intelligent Technology, Shanghai Institute of Technology（智能技术学院，上海理工大学）；Graduate School of Information Sciences, Tohoku University（信息科学研究生院，东北大学）
DExNet: Combining Observations of Domain Adapted Critics for Leaf Disease Classification with Limited Data
Department of CSE, Islamic University of Technology, Bangladesh（伊斯兰科技大学计算机科学与工程系，孟加拉国）；School of Computing, Queen’s University, Ontario, Canada（皇后大学计算学院，安大略省，加拿大）
The Institute for Sustainable Industries and Liveable Cities (ISILC), Victoria University（维多利亚大学可持续产业与宜居城市研究所（ISILC））；Namal Institute（纳马尔学院）；Charles Sturt University（查尔斯特大学）；Victoria University（维多利亚大学）
In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classi…
Bangladesh University of Business and Technology（孟加拉国商业技术大学）
A disease that limits a plant from its maximal capacity is defined as plant disease. From the perspective of agriculture, diagnosing plant disease is crucial, as diseases often limit plants' production capacity. However, manual approaches to recognize plant diseases are often temporal, challenging, and time-consuming. Therefore, computerized recognition of plant diseases is highly desired in the field of agricultural automation. Due to the recent improvement of computer vision, identifying diseases using leaf images of a particular plant has already been introduced. Nevertheless, the most intr…
Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat（斯瓦特工程与应用科学大学计算机系统工程系人工智能实验室）；Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran（法赫德国王石油矿产大学智能移动与物流跨学科研究中心）
An efficient plant disease detection using transfer learning approach
Mohan Babu University（莫汉巴布大学）；University of Nigeria（尼日利亚大学）；Tecnologico de Monterrey（蒙特雷理工学院）；Coal City University（煤炭城大学）；Saveetha Institute of Medical and Technical Sciences（萨维塔医学与技术科学学院）
SugarcaneNet: An Optimized Ensemble of LASSO-Regularized Pre-trained Models for Accurate Disease Classification
Dhaka University of Engineering & Technology（达卡工程技术大学）；Jashore University of Science and Technology（杰索尔科技大学）；International Islamic University Chittagong（吉大港国际伊斯兰大学）；Zarqa University（扎尔卡大学）；Northumbria University（诺森比亚大学）；Qassim University（卡西姆大学）
