Disease Of Tomato Research Articles

Enhanced Tomato Disease Classification Using Hybrid Deep Learning Approach

Tomato disease classification is important for agricultural productivity and food security, yet achieving high accuracy stays challenging due to the complexity of disease symptoms. This study proposed a model classification utilizing DenseNet121 combined with an Autoencoder for feature extraction and Simple neural network (SNN) for classifier. The dataset contains 10 classes of tomato diseases. Experimental results shows that the proposed approach achieved a high accuracy of 99%, outperforming the performance of several approaches. The integration of the Autoencoder enhances feature extraction and dimensionality reduction, important for improved classification outcomes. This study highlights the efficacy of the proposed work in achieving state-of-the-art accuracy in tomato disease detection. Future work may focus on refining the model using advanced augmentation techniques and expanding the dataset to enhance its applicability across diverse agricultural conditions.

AmiRNA Technology Enhances Tomato Disease Resistance by Suppressing Plant-Pathogen Interaction Pathways through Inhibiting TYLCV Replication.

Tomato yellow leaf curl virus disease has seriously threatened the quality and yield of tomatoes. In this study, we investigated the role of amiRNA technology in disease resistance in tomatoes (cherry tomato and large-fruited tomato) and analyzed the physiological and molecular mechanisms of disease resistance in transgenic plants. TYLCV contains six functional genes, of which the C1, C2, and V1 genes have more phosphorylation sites and glycosylation sites, and the protein structure is more complex. The virus replication was inhibited, the peroxidation of membrane lipids was reduced, and disease resistance was enhanced in all transgenic cherry tomato (J6) plants in which the C1, C2, and V1 genes were silenced, respectively. Similarly, silencing of the C1 gene enhanced disease resistance in large-fruited tomatoes. In conclusion, amiRNA technology hinders viral replication, leading to reduced activity of the tomato plant-pathogen interaction pathway and weakening tomato-virus interactions, thereby improving disease resistance.

Multi-kernel inception aggregation diffusion network for tomato disease detection.

Tomato leaf diseases significantly impact the yield and quality of tomatoes during cultivation, the main of which are septoria leaf spot, leaf curl virus, verticillium wilt, and early blight. These diseases necessitate prompt detection and management strategies to mitigate their deleterious effects on crop productivity. Due to the considerable scale variations in diseased tomato leaves, accurate and rapid detection and diagnosis remain challenging. To address the detection of tomato leaf diseases at different scales, we propose a real-time detection model incorporating a Multi-kernel Inception Aggregation Diffusion Network. In this paper, (1) We present a Multi-kernel Inception Aggregation Diffusion Network (MIADN) for the feature processing stage, which facilitates the aggregation and diffusion of multi-scale features across hierarchical levels, benefiting the detection of targets at various scales. (2) We present the Multi-kernel Inception Module (MKIM), designed to extract multi-scale object features using diverse convolution kernels, thereby enhancing the model's feature fusion and representation capabilities. (3) We incorporate the efficient FasterNet network at the feature extraction stage to preserve feature diversity and improve the model's ability to extract complex target features. (4) Extensive comparative and ablation experiments demonstrate that our method achieves the mean average precision (mAP50) of 96.6%, surpassing the baseline model by 4.1% and the advanced YOLOv9s model by 2.0%. This method provides an effective solution for high-quality tomato cultivation.

An intelligent agriculture monitoring framework for leaf disease detection using YOLOv7

Agriculture is one of the most important economic sectors on which societies have relied since ancient times. With the recent development of technology, agriculture has also been incorporating modern techniques such as the Internet of Things and Artificial Intelligence to improve productivity and monitor the farming process. One of agriculture’s most prominent issues is the spread of plant diseases and the lack of real-time monitoring. Various systems and operations have recently been developed to predict and diagnose plant diseases. However, current operations have been selective, focusing on a specific aspect without addressing other important aspects, resulting in either partial or compound application of results, rendering the desired outcomes ineffective. To deal with such challenges, we propose an intelligent framework for real-time agriculture monitoring and disease detection, namely a system for monitoring plant diseases using YOLOv7. In the proposed framework, a rule-based policy has been designed for detecting plant diseases using online plant leaf monitoring, sensors, and surveillance cameras. Images of plant leaves captured by different cameras are sent in real-time to central cloud servers for disease detection. The improved YOLOv7 technology is utilized for plant disease detection, and the proposed system has been evaluated using a dataset of diseased tomato leaves, comparing it with different models based on various performance metrics to demonstrate its effectiveness, achieving an accuracy of 96%.

Harnessing Biocontrol Potential of Streptomyces rochei Against Pythium aphanidermatum: Efficacy and Mechanisms.

Tomato (Solanum lycopersicum) and chilli (Capsicum annuum) are globally significant vegetable crops susceptible to damping-off disease caused by Pythium aphanidermatum, leading to substantial yield losses. The study aimed to document the biocontrol and plant growth promotion potential of Streptomyces rochei against damping-off disease in tomato and chilli. The actinobacterial isolates ACS18 followed by ACT30, and AOE12 were accomplished as the most effective antagonists against P. aphanidermatum in vitro. Molecular characterization confirmed these isolates as members of Streptomyces genus, with ASH 18 the top performer identified as S. rochei isolate. Analysis of biomolecule through GC-MS during ditrophic interaction between pathogen and S. rochei showed the presence of various antifungal metabolites which were directly related to suppression of the pathogen. Subsequently, S. rochei was formulated into a talc-based preparation and used as seed treatment and soil application against damping-off. In greenhouse trials, significant reductions in damping-off incidence were observed, Furthermore, seedlings treated with S. rochei displayed enhanced root and shoot lengths compared to the uninoculated controls. These benefits potentiate S. rochei as a promising biocontrol agent and demonstrating its dual benefits of disease suppression and promotion of seedling growth.

Research on the construction of a knowledge graph for tomato leaf pests and diseases based on the named entity recognition model

Research on the construction of a knowledge graph for tomato leaf pests and diseases based on the named entity recognition model

Comparative study of endophytic bacterial strains from non-host crops for enhancing plant growth and managing early blight in tomato

Bacillus pseudomycoides, Paenibacillus polymyxa, and B. velezensis are potent bacterial endophytes, which typically exhibit host-specific interactions. However, comparative studies of these endophytes in vitro and in planta in non-host crops are lacking. Therefore, in this study, we evaluated the potential of endophytes B. pseudomycoides strain HP3d, P. polymyxa strain PGSS1, B. velezensis strain A6, and P42, isolated from various crop ecosystems in promoting plant growth and inducing systemic resistance against early blight disease in tomato. In vitro, endophytes exhibited 44.44–55.56% and 37.50–87.50% inhibition of Alternaria solani in dual culture and volatilome bioassay, respectively. In the glasshouse, individual and combined applications via seed treatment (ST), seedling dip (SD), and foliar spray (FS) significantly enhanced shoot growth (23.63–57.61%), root growth (43.27–118.23%), number of leaves (77.52–93.58%), number of shoots (33.42–45.28%) and root dry matter (42.17–43.86%), reducing early blight (PDI) by 70.95–76.12% compared to uninoculated control. Enzymatic activities, including such as polyphenol oxidase (30–40 fold), peroxidase (65.00–75.00 fold), superoxide dismutase (34.20–37.20 fold) and phenylalanine ammonia-lyase (44.44–45.56 fold) were elevated post-inoculation in endophytes treated tomato plants challenged with A. solani compared to control treated only with A. solani and declined after the fifth day. The total chlorophyll content declined from the 0th to the 10th day, but endophyte treated plants exhibited lesser reductions (2.03–2.09) than uninoculated control. Field trials confirmed the glasshouse findings, showing reduced early blight and improved growth parameters in tomato where the ST + SD + FS combination emerged as the most effective treatment for all endophytes showing 1.06–1.88 fold increase in fruit yield per plant and 28.92–32.52% decrease in PDI compared to untreated control. Thus, the study highlights the broad-spectrum potential of these strains in promoting plant growth and controlling early blight in tomato, demonstrating non-host specificity. These endophytes offer eco-friendly alternatives to chemical pesticides, supporting sustainable agriculture. Their success in field trials suggests the potential for commercialization and large-scale use across diverse crops and pave the way for further interdisciplinary research to optimize their application in integrated pest management strategies.

Automatic visual recognition for leaf disease based on enhanced attention mechanism

Recognition methods have made significant strides across various domains, such as image classification, automatic segmentation, and autonomous driving. Efficient identification of leaf diseases through visual recognition is critical for mitigating economic losses. However, recognizing leaf diseases is challenging due to complex backgrounds and environmental factors. These challenges often result in confusion between lesions and backgrounds, limiting information extraction from small lesion targets. To tackle these challenges, this article proposes a visual leaf disease identification method based on an enhanced attention mechanism. By integrating multi-head attention mechanisms, this method accurately identifies small targets of tomato lesions and demonstrates robustness in complex conditions, such as varying illumination. Additionally, the method incorporates Focaler-SIoU to enhance learning capabilities for challenging classification samples. Experimental results showcase that the proposed algorithm enhances average detection accuracy by 10.3% compared to the baseline model, while maintaining a balanced identification speed. This method facilitates rapid and precise identification of tomato diseases, offering a valuable tool for disease prevention and economic loss reduction.

Mixed data augmentation and osprey search strategy for enhancing YOLO in tomato disease, pest, and weed detection

Mixed data augmentation and osprey search strategy for enhancing YOLO in tomato disease, pest, and weed detection

Research on tomato disease image recognition method based on DeiT

Tomatoes, globally cultivated and economically significant, play an essential role in both commerce and diet. However, the frequent occurrence of diseases severely affects both yield and quality, posing substantial challenges to agricultural production worldwide. In China, where tomato cultivation is carried out on a large scale, disease prevention and identification are increasingly critical for enhancing yield, ensuring food safety, and advancing sustainable agricultural practices. As agricultural production scales and the demand for efficient methodologies grows, traditional disease recognition methods no longer meet current needs. The agricultural sector's move towards more modern and scalable production methods necessitates more effective and precise disease recognition technologies to support swift decision-making and timely preventive actions. To address these challenges, this paper proposes a novel tomato disease recognition method that integrates the data-efficient image transformers (DeiT) model with strategies like exponential moving average (EMA) and self-distillation, named EMA-DeiT. By leveraging deep learning technologies, this method significantly improves the accuracy of disease recognition. The enhanced EMA-DeiT model demonstrated exemplary performance, achieving a 99.6 % accuracy rate in identifying ten types of tomato leaf diseases within the PlantVillage public dataset and 98.2 % on the Dataset of Tomato Leaves, which encompasses six disease types. In generalization tests, it achieved 97.1 % accuracy on the PlantDoc dataset and 97.6 % on the Tomato-Village dataset. Utilizing the improved DeiT model, a comprehensive tomato disease recognition system was developed, featuring modules for image collection, disease detection, and information display. This system facilitates an integrated process from image collection to intelligent disease analysis, enabling agricultural workers to promptly understand and respond to disease occurrences. This system holds significant practical value for implementing precision agriculture and enhancing the efficiency of agricultural production.

Modified Sand Cat Swarm Optimization (MSCSO) Based Feature Selection for Classification of Diseases in Tomato Leaves

Food obtained from the plants is one of the most basic requirements for the survival of humanity. However, plant diseases produce low-quality products that adversely affect the country's economy. Machine learning based detection methods for such diseases can reduce these effects. However, these methods involve plant images and usually suffer the curse of dimensionality in terms of features. Features selection improves the performance of machine learning algorithms. Feature selection method aims to find optimal number of features for a machine learning algorithm such as classification, clustering and many more. This work proposed a Modified Sand Cat Swarm Optimization (MSCSO) algorithm based feature selection to find the optimal number of features. The algorithm performs well over other standard algorithm such as GA, PSO, and GWO. The performance of different classifiers are analysed for classification of diseases in tomato leaves based on features selected by MSCSO.

Multiple Acquisitions of XopJ2 Effectors in Populations of Xanthomonas perforans.

Type III effectors (T3Es) are major determinants of Xanthomonas virulence and targets for resistance breeding. XopJ2 (synonym AvrBsT) is a highly conserved YopJ-family T3E acquired by X. perforans, the pathogen responsible for bacterial spot disease of tomato. In this study, we characterized a new variant (XopJ2b) of XopJ2, which is predicted to have a similar three-dimensional (3D) structure as the canonical XopJ2 (XopJ2a) despite sharing only 70% sequence identity. XopJ2b carries an acetyltransferase domain and the critical residues required for its activity, and the positions of these residues are predicted to be conserved in the 3D structure of the proteins. We demonstrated that XopJ2b is a functional T3E and triggers a hypersensitive response (HR) when translocated into pepper cells. Like XopJ2a, XopJ2b triggers HR in Arabidopsis that is suppressed by the deacetylase, SOBER1. We found xopJ2b in genome sequences of X. euvesicatoria, X. citri, X. guizotiae, and X. vasicola strains, suggesting widespread horizontal transfer. In X. perforans, xopJ2b was present in strains collected in North America, Africa, Asia, Australia, and Europe, whereas xopJ2a had a narrower geographic distribution. This study expands the Xanthomonas T3E repertoire, demonstrates functional conservation in T3E evolution, and further supports the importance of XopJ2 in X. perforans fitness on tomato. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Early plant disease detection by Raman spectroscopy: An open-source software designed for the automation of preprocessing and analysis of spectral dataset

This study introduces a reliable, non-coding software named qREAD-Raman, written in the JavaScript® language, for analyzing and interpreting Raman spectral information. It is designed with a focus on the early detection of diseases in tomato plants (S. lycopersicum) during the asymptomatic stage. The platform integrates a set of machine learning algorithms necessary for the preprocessing consisting of outlier removal, baseline correction, fluorescence removal, smoothing, and normalization. For classification, we applied a Consensus of five different classifiers: Multilayer Perceptron (MLP), Partial Least Squares-Discriminant Analysis (PLS-DA), Linear Discriminant Analysis (LDA), Long Short-Term Memory (LSTM), and K-nearest neighbors (kNN). The experiments were conducted on two bacterial diseases: bacterial canker of tomato induced by Clavibacter michiganesis subsp. michiganensis (Cmm), and the tomato vein-greening associated with Candidatus Liberibacter solanacearum (CLso), a non-culturable bacteria transmitted by Bactericera cockerelli insect. Binary models (Cmm-Healthy and CLso-Healthy) demonstrated excellent classification ability. Asymptomatic Cmm-infected plants were distinguished with an accuracy of 88–95 %, while CLso-infected plants showed an accuracy of 68–77 %. The three-class model (CLso-Cmm-Healthy) exhibited acceptable performance in differentiating between Cmm and CLso, with accuracy rates of 71–83% and 58–67%, respectively. The model's performance highlights differences in the relevant spectral regions associated with the biochemical changes induced by each studied disease. The qREAD-Raman software, implemented for the purpose of this research, was found to be a valuable and comprehensive tool that effectively differentiate diseased tomato plants during their asymptomatic stage.

Extracellular DNA as a Strategy to Manage Vascular Wilt Caused by Fusarium oxysporum in Tomato (Solanum lycopersicum L.) Based on Its Action as a Damage-Associated Molecular Pattern (DAMP) or Pathogen-Associated Molecular Pattern (PAMP).

Vascular wilt is an important tomato disease that affects culture yields worldwide, with Fusarium oxysporum (F.o) being the causal agent of this infection. Several management strategies have lost effectiveness due to the ability of this pathogen to persist in soil and its progress in vascular tissues. However, nowadays, research has focused on understanding the plant defense mechanisms to cope with plant diseases. One recent and promising approach is the use of extracellular DNA (eDNA) based on the ability of plants to detect their self-eDNA as damage-associated molecular patterns (DAMPs) and pathogens' (non-self) eDNA as pathogen-associated molecular patterns (PAMPs). The aim of this work was to evaluate the effect of the eDNA of F.o (as a DAMP for the fungus and a PAMP for tomato plants) applied on soil, and of tomato's eDNA (as a DAMP of tomato plants) sprayed onto tomato plants, to cope with the disease. Our results suggested that applications of the eDNA of F.o (500 ng/µL) as a DAMP for this pathogen in soil offered an alternative for the management of the disease, displaying significantly lower disease severity levels in tomato, increasing the content of some phenylpropanoids, and positively regulating the expression of some defense genes. Thus, the eDNA of F.o applied in soil was shown to be an interesting strategy to be further evaluated as a new element within the integrated management of vascular wilt in tomato.

Antifungal activity of citral against Fusarium wilt in tomatoes and induction of the upregulation of glucanase, chitinase, and thaumatin-like protein plant defense genes

Fusarium wilt of tomatoes, caused by the soil-borne fungus Fusarium oxysporum f. sp lycopersici (FOL), is one of the devastating diseases of tomatoes, causing substantial economic losses worldwide. It is primarily controlled with chemical fungicides. The present study assessed the antifungal activity of citral against FOL under greenhouse conditions and investigated its effects on three plant defense genes: glucanase, chitinase, and thaumatin-like protein (TLP) using Real-time- quantitative PCR (RT-qPCR). Greenhouse experiments demonstrated that citral oil at 1024 and 512 µg/mL significantly reduced wilt severity from 61.7 % to 28.3 and 33.3 %, respectively. RT-qPCR results revealed that citral upregulates the expression of the three defense genes to varying extents, highlighting their role in resistance induction in tomatoes against FOL. Chitinase showed the highest differential expression with a 9.54-fold increase at 12 hours post-treatment (hpt), followed by TLP with a 6.96-fold increase at 12hpt, and glucanase with a 3.68-fold increase at 24hpt. These findings suggest citral is a promising biocontrol agent against fusarium wilt in tomatoes. However, field trials are recommended to validate these results under open field conditions.

Potential of Small Molecules Piperidine and Pyrrolidine Against Copper-Resistant Xanthomonas perforans, Causal Agent of Bacterial Spot of Tomato.

Bacterial spot of tomato (BST), caused by Xanthomonas perforans, is an economically important disease of tomatoes in Florida. Due to the heavy reliance on copper-based bactericides for control of BST, copper-resistant strains of X. perforans are widely distributed in Florida, leading to reduced efficacy of copper-based bactericides for disease control. There is a need for alternative chemical control strategies to effectively manage this disease in tomato production. In this study, two small molecules, piperidine and pyrrolidine, were evaluated for their efficacy against the copper-resistant X. perforans strain GEV 485 in laboratory, greenhouse, and field experiments. In in vitro experiments, piperidine and pyrrolidine at concentrations as low as 2 mg/L and 16 mg/L, respectively, significantly (P<0.001) reduced bacterial populations within 4 h of incubation compared to the untreated control, while Kocide 3000, the grower copper-based bactericide standard, at 0.9 g/L and 2.1 g/L (full label rate) did not significantly reduce bacterial populations. When tested as foliar sprays in the greenhouse, pyrrolidine at 128 mg/L significantly (P <0.001) reduced disease severity compared to the untreated control, with an equivalent efficacy to Kocide 3000 (copper hydroxide at 2.1 g/L). Kocide 3000 at 1.0 g/L, in combination with piperidine at 64 mg/L and pyrrolidine at 64 and 128 mg/L significantly improved the efficacy in disease control compared to untreated controls and Kocide 3000 at 1.0 g/L alone. In field trials, both small molecules demonstrated equivalent or superior efficacy to ManKocide (copper hydroxide + mancozeb) against X. perforans compared to the untreated control. This study demonstrated for the first time the potential of piperidine and pyrrolidine for controlling bacterial spot of tomato.

Deep learning networks-based tomato disease and pest detection: a first review of research studies using real field datasets.

Recent advances in deep neural networks in terms of convolutional neural networks (CNNs) have enabled researchers to significantly improve the accuracy and speed of object recognition systems and their application to plant disease and pest detection and diagnosis. This paper presents the first comprehensive review and analysis of deep learning approaches for disease and pest detection in tomato plants, using self-collected field-based and benchmarking datasets extracted from real agricultural scenarios. The review shows that only a few studies available in the literature used data from real agricultural fields such as the PlantDoc dataset. The paper also reveals overoptimistic results of the huge number of studies in the literature that used the PlantVillage dataset collected under (controlled) laboratory conditions. This finding is consistent with the characteristics of the dataset, which consists of leaf images with a uniform background. The uniformity of the background images facilitates object detection and classification, resulting in higher performance-metric values for the models. However, such models are not very useful in agricultural practice, and it remains desirable to establish large datasets of plant diseases under real conditions. With some of the self-generated datasets from real agricultural fields reviewed in this paper, high performance values above 90% can be achieved by applying different (improved) CNN architectures such as Faster R-CNN and YOLO.

Solanum pimpinellifolium exhibits complex genetic resistance to Pseudomonas syringae pv. tomato.

Pseudomonas syringae pv. tomato (Pst) is the causal agent of bacterial speck disease in tomatoes. The Pto/Prf gene cluster from Solanum pimpinellifolium was introgressed into several modern tomato cultivars and provided protection against Pst race 0 strains for many decades. However, virulent Pst race 1 strains that evade Pto-mediated immunity now predominate in tomato-growing regions worldwide. Here we report the identification of resistance to a Pst race 1 strain (Pst19) in the wild tomato accession S. pimpinellifolium LA1589 (hereafter LA1589), using our rapid high-throughput seedling screen. LA1589 supports less bacterial growth than cultivars, and does not exhibit a hypersensitive response to Pst19. We tested an existing set of 87 Inbred Backcross Lines (IBLs) derived from a cross between susceptible Solanum lycopersicum E-6203 and Solanum pimpinellifolium LA1589 for resistance to Pst19. Using single-marker analysis, we identified three genomic regions associated with resistance. Bacterial growth assays on IBLs confirmed that these regions contribute to resistance in planta. We also mapped candidate genes associated with resistance in a cross between the Solanum lycopersicum var. lycopersicum cultivar Heinz BG-1706 and S. pimpinellifolium LA1589. By comparing candidates from the two mapping approaches, we were able to identify 3 QTL and 5 candidate genes in LA1589 for a role in resistance to Pst19. This work will assist in molecular marker-assisted breeding to protect tomato from bacterial speck disease.

New Method for Tomato Disease Detection Based on Image Segmentation and Cycle-GAN Enhancement

A major concern in data-driven deep learning (DL) is how to maximize the capability of a model for limited datasets. The lack of high-performance datasets limits intelligent agriculture development. Recent studies have shown that image enhancement techniques can alleviate the limitations of datasets on model performance. Existing image enhancement algorithms mainly perform in the same category and generate highly correlated samples. Directly using authentic images to expand the dataset, the environmental noise in the image will seriously affect the model’s accuracy. Hence, this paper designs an automatic leaf segmentation algorithm (AISG) based on the EISeg segmentation method, separating the leaf information with disease spot characteristics from the background noise in the picture. This algorithm enhances the network model’s ability to extract disease features. In addition, the Cycle-GAN network is used for minor sample data enhancement to realize cross-category image transformation. Then, MobileNet was trained by transfer learning on an enhanced dataset. The experimental results reveal that the proposed method achieves a classification accuracy of 98.61% for the ten types of tomato diseases, surpassing the performance of other existing methods. Our method is beneficial in solving the problems of low accuracy and insufficient training data in tomato disease detection. This method can also provide a reference for the detection of other types of plant diseases.

MobileNet-CAL: Tomato pest and disease classification method based on transfer learning and attention mechanism

Based on the MobileNetV3 model, this paper introduces the Coordinate attention (CA) attention mechanism and transfer learning to propose the MobileNet-CAL model to solve the problems of insufficient attention to model channel information, long classification training time, high training cost, and weak generalization ability. The model combines data enhancement, attention mechanism and transfer learning technology to improve the classification effect of the model, and updates the fully connected layer at the same time. Finally, the model is optimized by combining the Auxiliary loss function and the ASGD optimizer to improve the classification accuracy of the model, speed up the collection speed, and enhance the robustness of the model. Finally, the model is evaluated from multiple angles such as Accuracy, F1-score, Precision, Recall, and Specificity in the tomato pest and disease leaf dataset. Its Accuracy reaches 98.59% an accuracy of, which is improved by, and by, 4.47%respectively, compared with the original model. Experiments show that MobileNet-CAL can improve the classification accuracy to a certain extent, verifying that the model proposed 4.05%、3.43%、16.57% in 4.13%this paper is effective in the classification task of tomato pest and disease leaves.