Tomato Leaf Disease Research Articles

Encouraging hygiene permanence in tomato leaf and applying machine learning techniques

<div align="center"><span>Tomatoes are the major ingredient in food preparation, which leads to a huge food production rate. Most countries cultivate huge tomatoes at the same time that crop diseases affect the production rate due to many different types of diseases. The various types of diseases are bacterial spots, septoria leaf spot, left mold, late blight, early blight, arget and spot. Many research studies review these tomato leaf diseases with various statistics. The survey on disease will give a clear idea of reasons and prevention methods, also presenting how to reduce it in the early stages. In another study, tomato leaf images were taken to classify the diseased and non-diseased varieties. Few studies compare the standard model of disease prediction with the machine learning models. Therefore, this research study discusses tomato leaf disease detection and prevention methods used by various researchers in their studies and finally consolidate the observations. This study also deals with encouraging hygiene permanence in tomato leaf using machine learning algorithms. The convolutional neural network (CNN) was used to predict the early nature of the hygiene nature of leafy vegetable plants for the benefit of agriculture people and concluded with better future suggestions.</span></div>

The integration of machine learning and IoT for the early detection of tomato leaf disease in real-time

The impact of climate change, pests, and inadequate agricultural practices on crop health is becoming a growing concern. It is estimated that 20-40% of global crop yield is adversely affected by pests and diseases. This has a direct negative impact on food security and nutritional well-being, as staple cereal crops (such as rice, maize, and wheat) and tuber crops (like potato, onion, and tomato) are affected. Advancements in Artificial Intelligence (AI), Computer Vision (CV), and IoT have a significant influence on reducing crop losses by detecting crop diseases at early stages. The primary focus of this research is to develop a real-time system that can detect tomato crop diseases at an early stage by integrating Machine Learning (ML) algorithms and IoT. The most common tomato leaf diseases, such as Tomato Mosaic Virus (TMV), Tomato Bacterial Leaf Spot (TBLS), Tomato Early Blight (TEB), and Tomato Late Blight (TLB), are considered in this work. The hybrid discriminative feature space is derived from the integration of low- and high-level features via Convolution Neural Network (CNN) Layers. The 3-stage Stacked Deep Convolutional Autoencoder is used to optimize the CNN performance by reducing computation complexity. The proposed model is implemented on the Plantvillage benchmark dataset and achieves the highest recognition accuracy of 95.6% for the 5-class problem using 5-fold cross-validation.

FL-ToLeD: An Improved Lightweight Attention Convolutional Neural Network Model for Tomato Leaf Diseases Classification for Low-end Devices

FL-ToLeD: An Improved Lightweight Attention Convolutional Neural Network Model for Tomato Leaf Diseases Classification for Low-end Devices

Transfer Learning-based Object Detection Models for Improved Diagnosis of Tomato Leaf Disease

During their growth, tomatoes are impacted by several illnesses and pests. The most crucial step in effectively assisting vegetable farmers in increasing tomato yield is combating diseases by the precise and early identification of these problems. Utilizing deep learning for detecting objects saves time, effort and allows real-time judgment, thereby reducing the substantial losses in the yield. This study uses the Open-source dataset of Tomato Leaf Diseases under natural conditions and trains them with multiple YOLO models(YOLOv5, YOLOv7) for the characteristics of tomato diseases’ images. Transfer learning is subsequently applied to augment the adaptability of these YOLO models, while the integration of a YOLO-YOLO cascaded ensembling technique enhances multiscale feature detection. This approach significantly elevates the accuracy of disease detection, effectively pinpointing both the location and classification of tomato leaf diseases. The prominent YOLO v5-v7 ensemble transfer learning model, when evaluated, exhibits noteworthy performance metrics during training. It achieves a mean average precision (mAP) of 98.8% at a detection threshold of 0.5 and a mAP of 98.7% at a more stringent threshold of 0.95. Additionally, the model demonstrates high precision at 95.8% and a strong recall rate of 96.8%.

Toward Real Scenery: A Lightweight Tomato Growth Inspection Algorithm for Leaf Disease Detection and Fruit Counting

The deployment of intelligent surveillance systems to monitor tomato plant growth poses substantial challenges due to the dynamic nature of disease patterns and the complexity of environmental conditions such as background and lighting. In this study, an integrated cascade framework that synergizes detectors and trackers was introduced for the simultaneous identification of tomato leaf diseases and fruit counting. We applied an autonomous robot with smartphone camera to collect images for leaf disease and fruits in greenhouses. Further, we improved the deep learning network YOLO-TGI by incorporating Ghost and CBAM modules, which was trained and tested in conjunction with premier lightweight detection models like YOLOX and NanoDet in evaluating leaf health conditions. For the cascading with various base detectors, we integrated state-of-the-art trackers such as Byte-Track, Motpy, and FairMot to enable fruit counting in video streams. Experimental results indicated that the combination of YOLO-TGI and Byte-Track achieved the most robust performance. Particularly, YOLO-TGI-N emerged as the model with the least computational demands, registering the lowest FLOPs at 2.05 G and checkpoint weights at 3.7 M, while still maintaining a mAP of 0.72 for leaf disease detection. Regarding the fruit counting, the combination of YOLO-TGI-S and Byte-Track achieved the best R 2 of 0.93 and the lowest RMSE of 9.17, boasting an inference speed that doubles that of the YOLOX series, and is 2.5 times faster than the NanoDet series. The developed network framework is a potential solution for researchers facilitating the deployment of similar surveillance models for a broad spectrum of fruit and vegetable crops.

Deep Learning for Early Detection of Tomato Leaf Diseases: A ResNet-18 Approach for Sustainable Agriculture

Deep Learning for Early Detection of Tomato Leaf Diseases: A ResNet-18 Approach for Sustainable Agriculture

Senet Cnn Based Tomato Leaf Disease Detection

Senet Cnn Based Tomato Leaf Disease Detection

A Lightweight Tomato Leaf Disease Detection System with Strong Generalization Ability Based on TMT-YOLOv5s

A Lightweight Tomato Leaf Disease Detection System with Strong Generalization Ability Based on TMT-YOLOv5s

Detecting tomato leaf diseases with convolutional neural networks and image processing using a Sri Lankan tomato leaf dataset

Crops like tomatoes are vital to farmers' livelihoods in Sri Lanka, where agriculture is a key economic pillar. But growing tomatoes comes with a lot of difficulties, not the least of which is the possibility of certain diseases that can destroy crops. The timely implementation of interventions and reduction of losses are contingent upon the early discovery of these disorders. Using convolutional neural networks (CNNs) and image processing techniques, this study offers a novel solution to this problem by detecting tomato leaf illnesses. One unique aspect of this study is the use of a custom dataset made up of photos of Sri Lankan tomato leaves from several farms in Embilipitiya, Suriyawewa an area noted for being susceptible to several tomato illnesses. The dataset includes a variety of disease categories that are common in the local agricultural setting, such as tomato early blight, tomato Septoria leaf spot, tomato curl, and tomato leaf minor. The quality of the dataset is improved using pre-processing methods including segmentation and picture enhancement. The dataset is then used to train a CNN architecture for the purpose of classifying diseases. The efficiency of the suggested method is demonstrated by the experimental findings, which show that it can accurately identify and classify tomato leaf diseases. The system that has been built provides an automated and effective tool for early disease diagnosis, which facilitates timely intervention and efficient management approaches. Utilising a localised dataset improves the system's resilience and adaptability, which makes it ideal for implementation in Sri Lankan tomato farms.

Implementation of adaptive multiscale dilated convolution-based ResNet model with complex background removal for tomato leaf disease classification framework

Implementation of adaptive multiscale dilated convolution-based ResNet model with complex background removal for tomato leaf disease classification framework

Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

As COVID-19 spread worldwide, many major grain-producing countries have adopted measures to restrict their grain exports; food security has aroused great concern from various parties. How to improve grain production has become one of the most important issues facing all countries. However, crop diseases are a difficult problem for many farmers so it is important to master the severity of crop diseases timely and accurately to help staff take further intervention measures to minimize plants being further infected. In this paper, a restructured residual dense network was proposed for tomato leaf disease identification; this hybrid deep learning model combines the advantages of deep residual networks and dense networks, which can reduce the number of training process parameters to improve calculation accuracy as well as enhance the flow of information and gradients. The original RDN model was first used in image super resolution, so we need to restructure the network architecture for classification tasks through adjusted input image features and hyper parameters. Experimental results show that this model can achieve a top-1 average identification accuracy of 95% on the Tomato test dataset in AI Challenger 2018 datasets, which verifies its satisfactory performance. The restructured residual dense network model can obtain significant improvements over most of the state-of-the-art models in crop leaf identification, as well as requiring less computation to achieve high performance

A novel approach for tomato leaf disease classification with deep convolutional neural networks

Computer-aided automation systems that detect plant diseases are one of the challenging research areas that provide effective results in the agricultural field. Tomato crops are a major product with high commercial value worldwide and are produced in large quantities. This study proposes a new approach for the automatic detection of tomato leaf diseases, which employs classical learning methods and deep neural networks for image classification. Specifically, Local Binary Pattern (LBP) method was used for feature extraction in classical learning methods, while Extreme Learning Machines, k-Nearest Neighborhood (kNN), and Support Vector Machines (SVM) were used for classification. On the other hand, a novel Convolutional Neural Network (CNN) framework with its parameters and layers was employed for deep learning. The study shows that the accuracy values obtained from the proposed approach are better than the state-of-the-art studies. The classification process was carried out with different numbers of classes, including binary classification (healthy vs. unhealthy), 6-class, and 10-class classification for distinguishing different types of diseases. The results indicate that the CNN model outperforms classical learning methods, with accuracy values of 99.5%, 98.50%, and 97.0% obtained for the classification of 2, 6, and 10 classes, respectively. In future studies, computer-aided automated systems can be utilized to detect different diseases for various plant species.

An Optimized YOLO v5 Model for Tomato Leaf Disease Classification with Field Dataset

Deep learning has gained widespread adoption in various fields, including object recognition, classification, and precision agriculture. This study aimed to investigate the use of deep convolutional neural networks for the real-time identification of diseases in tomato plant leaves. A customized field dataset was constructed, consisting of several images of tomato leaves captured using a mobile phone from agricultural fields in the Kerala and Tamil Nadu regions and classified into two categories: healthy and diseased. A YOLO v5 deep learning model was trained to classify images of tomato leaves into the respective categories. This study aimed to determine the most effective hyperparameters for the classification and detection of healthy and sick leaves sections, using both proprietary and publicly available datasets. The YOLO v5 model demonstrated a notable accuracy rate of 93% when evaluated in the test dataset. This method can help farmers quickly recognize diseased leaves and prompt the implementation of preventive measures to curtail the spread of tomato plant diseases.

Android Application for Tomato Leaf Disease Prediction Based on MobileNet Fine-tuning

Tomato is one of the most well-known and widely cultivated plants in the world. The result of tomato production is affected by the conditions of the plants when they are grown. It may decrease due to leaf plant disease caused by climate change, pollinator decrease, microbial pets, or parasites. To prevent this, an image-based application is needed to identify tomato plant disease based on visually unique patterns or marks seen on leaves. In this paper, we proposed a CNN fine-tuned model based on MobileNet architectures to identify tomato leaf disease for mobile applications. Based on the results tested by K-fold cross-validation, the best accuracy achieved by the proposed model is 97.1%. Additionally, the best average precision, recall and F1 Score are 99.8%, 99.8%, and 99.5%, respectively. The model with the best results is also implemented into Android-based mobile applications.

Harnessing the power of diffusion models for plant disease image augmentation.

The challenges associated with data availability, class imbalance, and the need for data augmentation are well-recognized in the field of plant disease detection. The collection of large-scale datasets for plant diseases is particularly demanding due to seasonal and geographical constraints, leading to significant cost and time investments. Traditional data augmentation techniques, such as cropping, resizing, and rotation, have been largely supplanted by more advanced methods. In particular, the utilization of Generative Adversarial Networks (GANs) for the creation of realistic synthetic images has become a focal point of contemporary research, addressing issues related to data scarcity and class imbalance in the training of deep learning models. Recently, the emergence of diffusion models has captivated the scientific community, offering superior and realistic output compared to GANs. Despite these advancements, the application of diffusion models in the domain of plant science remains an unexplored frontier, presenting an opportunity for groundbreaking contributions. In this study, we delve into the principles of diffusion technology, contrasting its methodology and performance with state-of-the-art GAN solutions, specifically examining the guided inference model of GANs, named InstaGAN, and a diffusion-based model, RePaint. Both models utilize segmentation masks to guide the generation process, albeit with distinct principles. For a fair comparison, a subset of the PlantVillage dataset is used, containing two disease classes of tomato leaves and three disease classes of grape leaf diseases, as results on these classes have been published in other publications. Quantitatively, RePaint demonstrated superior performance over InstaGAN, with average Fréchet Inception Distance (FID) score of 138.28 and Kernel Inception Distance (KID) score of 0.089 ± (0.002), compared to InstaGAN's average FID and KID scores of 206.02 and 0.159 ± (0.004) respectively. Additionally, RePaint's FID scores for grape leaf diseases were 69.05, outperforming other published methods such as DCGAN (309.376), LeafGAN (178.256), and InstaGAN (114.28). For tomato leaf diseases, RePaint achieved an FID score of 161.35, surpassing other methods like WGAN (226.08), SAGAN (229.7233), and InstaGAN (236.61). This study offers valuable insights into the potential of diffusion models for data augmentation in plant disease detection, paving the way for future research in this promising field.

Heterogeneous domain adaptation method for tomato leaf disease classification base on CycleGAN

Despite the significant improvements in the detection and diagnosis of plant diseases at an early stage facilitated by deep learning technology, there are challenges associated with the generalization performance of deep learning models. These problems from the differences between in-field and in-lab data, as well as the heterogeneity of training and prediction data features. In the case of tomato leaf diseases, the PlantVillage dataset is widely used and has already demonstrated accuracy of more than 99%. However, using trained model based on this dataset to predict in-field data results in low accuracy due to domain differences and heterogeneous features. In this paper, we propose a domain adaptation method based on CycleGAN to solve this problem, followed by a preprocessing technique that utilizes both the OpenCV module and a segmentation model based on U-Net for the best generalization performance. The classification accuracy is evaluated by applying the DenseNet121 model trained on the PlantVillage dataset to the images generated by CycleGAN. Our results demonstrate, with an F1-score of 95.6%, that our domain adaptation method between the two domains is effective in mitigating the effect of domain shift.

A comparative study of Machine Learning-based classification of Tomato fungal diseases: Application of GLCM texture features

Globally, agriculture remains an important source of food and economic development. Due to various plant diseases, farmers continue to suffer huge yield losses in both quality and quantity. In this study, we explored the potential of using Artificial Neural Networks, K-Nearest Neighbors, Random Forest, and Support Vector Machine to classify tomato fungal leaf diseases: Alternaria, Curvularia, Helminthosporium, and Lasiodiplodi based on Gray Level Co-occurrence Matrix texture features. Small differences between symptoms of these diseases make it difficult to use the naked eye to obtain better results in detecting and distinguishing these diseases. The Artificial Neural Network outperformed other classifiers with an overall accuracy of 94% and average scores of 93.6% for Precision, 93.8% for Recall, and 93.8% for F1-score. Generally, the models confused samples originally belonging to Helminthosporium with Curvularia. The extracted texture features show great potential to classify the different tomato leaf fungal diseases. The results of this study show that texture characteristics of the Gray Level Co-occurrence Matrix play a critical role in the establishment of tomato leaf disease classification systems and can facilitate the implementation of preventive measures by farmers, resulting in enhanced yield quality and quantity.

A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI).

The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation's economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2's pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.

A ResNet50-DPA model for tomato leaf disease identification.

Tomato leaf disease identification is difficult owing to the variety of diseases and complex causes, for which the method based on the convolutional neural network is effective. While it is challenging to capture key features or tends to lose a large number of features when extracting image features by applying this method, resulting in low accuracy of disease identification. Therefore, the ResNet50-DPA model is proposed to identify tomato leaf diseases in the paper. Firstly, an improved ResNet50 is included in the model, which replaces the first layer of convolution in the basic ResNet50 model with the cascaded atrous convolution, facilitating to obtaining of leaf features with different scales. Secondly, in the model, a dual-path attention (DPA) mechanism is proposed to search for key features, where the stochastic pooling is employed to eliminate the influence of non-maximum values, and two convolutions with one dimension are introduced to replace the MLP layer for effectively reducing the damage to leaf information. In addition, to quickly and accurately identify the type of leaf disease, the DPA module is incorporated into the residual module of the improved ResNet50 to obtain an enhanced tomato leaf feature map, which helps to reduce economic losses. Finally, the visualization results of Grad-CAM are presented to show that the ResNet50-DPA model proposed can identify diseases more accurately and improve the interpretability of the model, meeting the need for precise identification of tomato leaf diseases.

The Use of Machine Learning in Industry 4.0 as an Educational Tool to Address Plant Diseases for Small-Scale Farmers in Developing Countries: A Review Study

This review study aims to provide an overview of the current state of research on the use of machine learning techniques for the detection of tomato leaf diseases in the context of climate change in Zambia. Plant diseases pose significant challenges to small-scale farmers in developing countries, impacting crop yields and livelihoods. The emergence of Industry 4.0 technologies, coupled with the power of machine learning, offers promising opportunities to address these challenges and empower farmers with valuable knowledge and tools This review study aims to provide an overview of the use of machine learning in Industry 4.0 as an educational tool specifically tailored to tackle plant diseases for small-scale farmers in developing countries. The study offers insights into the potential of these techniques to enhance disease detection and contribute to sustainable agricultural practices in the face of climate change. Climate change has had significant impacts on agricultural practices worldwide, leading to the emergence and spread of various plant diseases. The study examines existing literature, research articles, and practical implementations to analyze the potential applications of machine learning in plant disease management. The review focuses on four key areas: disease identification, early detection and prediction, knowledge sharing and education, and decision support systems. With further advancements in machine learning techniques and the integration of cutting-edge technologies, the agriculture sector can benefit from improved disease detection and mitigation strategies to ensure food security in the face of climate change.