Pest Of Tomato Research Articles

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.

RNAi targeting Nav and CPR via leaf delivery reduces adult emergence and increases the susceptibility to λ-cyholthin in Tuta absoluta (Meyrick)

The tomato leafminer, Tuta absoluta (Meyrick), one of the most economically destructive pests of tomato, causes severe yields losses of tomato production globally. Rapid evolution of insecticide resistance requires the development of alternative control strategy for this pest. RNA interference (RNAi) represents a promising, innovative control strategy against key agricultural insect pests, which has recently been licensed for Colorado Potato Beetle control. Here two essential genes, voltage-gated sodium channel (Nav) and NADPH-cytochrome P450 reductase (CPR) were evaluated as targets for RNAi using an ex vivo tomato leaf delivery system. Developmental stage-dependent expression profiles showed TaNav was most abundant in adult stages, whereas TaCPR was highly expressed in larval and adult stages. T. absoluta larvae feeding on tomato leaflets treated with dsRNA targeting TaNav and TaCPR showed significant knockdown of gene expression, leading to reduction in adult emergence. Additionally, tomato leaves treated with dsRNA targeting these two genes were significantly less damaged by larval feeding and mining. Furthermore, bioassay with LC30 doses of λ-cyholthin showed that silencing TaNav and TaCPR increased T. absoluta mortality about 32.2 and 17.4%, respectively, thus indicating that RNAi targeting TaNav and TaCPR could increase the susceptibility to λ-cyholthin in T. absoluta. This study demonstrates the potential of using RNAi targeting key genes, like TaNav and TaCPR, as an alternative technology for the control of this most destructive tomato pests in the future.

YOLO-FMDI: A Lightweight YOLOv8 Focusing on a Multi-Scale Feature Diffusion Interaction Neck for Tomato Pest and Disease Detection

At the present stage, the field of detecting vegetable pests and diseases is in dire need of the integration of computer vision technologies. However, the deployment of efficient and lightweight object-detection models on edge devices in vegetable cultivation environments is a key issue. To address the limitations of current target-detection models, we propose a novel lightweight object-detection model based on YOLOv8n while maintaining high accuracy. In this paper, (1) we propose a new neck structure, Focus Multi-scale Feature Diffusion Interaction (FMDI), and inject it into the YOLOv8n architecture, which performs multi-scale fusion across hierarchical features and improves the accuracy of pest target detection. (2) We propose a new efficient Multi-core Focused Network (MFN) for extracting features of different scales and capturing local contextual information, which optimizes the processing power of feature information. (3) We incorporate the novel and efficient Universal Inverted Bottleneck (UIB) block to replace the original bottleneck block, which effectively simplifies the structure of the block and achieves the lightweight model. Finally, the performance of YOLO-FMDI is evaluated through a large number of ablation and comparison experiments. Notably, compared with the original YOLOv8n, our model reduces the parameters, GFLOPs, and model size by 18.2%, 6.1%, and 15.9%, respectively, improving the mean average precision (mAP50) by 1.2%. These findings emphasize the excellent performance of our proposed model for tomato pest and disease detection, which provides a lightweight and high-precision solution for vegetable cultivation applications.

Evaluation of Intercropping and Tobacco Leaf Extract for Management of Fruit Borer (Helicoverpa armigera) and Some Associated Beneficial Insects on Tomato in Eastern Hararghe, Ethiopia

Tomato production is challenged by different insect pests among these, fruit borer is the most destructing pests of tomato. To tackle these problems, the study was initiated with the objective to evaluate intercropping and tobacco extract on fruit borer and some associated beneficial insects on tomato in Eastern Hararghe. Head cabbage, common bean, onion, tobacco leaf extract and karate were used as treatments. The experiment was laid out in RCBD with four replications. From these results, significant differences were observed between treatments on larval population per plant and infestation per plant for all insect pests tested and also in population reduction. Tobacco leaf extract highly reduced the population occurrence and infestation level of fruit borer next to karate and followed by onion intercropping. The number of branches, number of fruits, and number of damaged fruits, marketable yield, non-marketable yield, and total yield also showed a significant difference (P < 0.05) among the treatments. The number of fruits per plant, marketable yield, and Total yield were the highest on onion intercrops followed by karate and tobacco extracts whereas the lowest number was observed from head cabbage intercrops. Maximum yield and yield traits were recorded on tomato onion intercropping compared to the rests of intercrops while the botanical extract comparably maximized the yields with Karate. Therefore, tomato-onion intercropping and tobacco leaf extract were used as the best alternatives to karate, the synthetic insecticides in the management of tomato fruit borer.

Multistress‐tolerant Pseudomonas aeruginosa SSVP3 uses multiple strategies to control Meloidogyne incognita in tomato

AbstractTomato is the most important horticultural crop, and India is its second largest producer; however, the plant‐pathogenic nematode Meloidogyne incognita is a serious pest of tomato, causing detrimental losses in its production. This study is focused on the use of multistress‐tolerant Pseudomonas aeruginosa SSVP3 to control this devastating nematode and attempts to decipher the roles of its metabolites and volatile organic compounds (VOCs) during this interaction. The supernatant of P. aeruginosa SSVP3 caused 58% mortality in M. incognita J2 juveniles. The major nematicidal metabolites, identified using liquid chromatography‐mass spectrometry, were pyocyanin, pyoluteorin, pyochelin, benthocyanin and phenazines. The VOCs secreted by P. aeruginosa SSVP3, which were identified via gas chromatography–mass spectrometry (HS‐SPME‐GC–MS) in a mixture, caused 98% mortality in M. incognita J2 juveniles. In a pot experiment, seedling root treatment with P. aeruginosa SSVP3 activated the induced systemic response in tomato plants to M. incognita by increasing the activity of defence and antioxidant enzymes as well as the proline and phenolic contents. The malondialdehyde content in P. aeruginosa SSVP3‐treated plants decreased as compared to those in the other treatment groups. The number of galls formed and the number of endoparasitic stages of M. incognita in the untreated nematode control plants were much greater (19 and 35) than those in the P. aeruginosa SSVP3‐treated (0.33 and 1) and chemically treated groups (0.67 and 1.33). These results indicate that P. aeruginosa and its secreted metabolites and VOCs have a high potential for controlling nematodes.

Morphological characteristics and density of Bracon (Habrobracon) concolorans Marshall,1900 (Hymenoptera: Braconidae), a native Turkish parasitoid of Tuta absoluta (Meyrick, 1917) (Lepidoptera: Gelechiidae), on greenhouse-grown tomatoes in Antalya, Türkiye

Tuta absoluta (Meyrick, 1917) (Lepidoptera: Gelechiidae) is one of the most significant pests of tomato. Chemical pesticides are prioritized in pest control, and this poses risks to both the environment and human health. Although commercial biological agents are occasionally?increasingly? used for control, understanding the density of existing parasitoids and predators of the pest is crucial for the effectiveness of integrated pest management. In this study, the parasitism rate of the native parasitoid Bracon (Habrobracon) concolorans Marshall, 1900 (Hymenoptera:Braconidae) collected from T. absoluta infesting tomato plants Antalya was investigated. Tomato leaf samples were collected weekly from a greenhouse in Elmalı/Antalya in October-November 2019, and cultured. The samples were monitored daily to record the number of pests and parasitoids that reached the adult stage. According to the results, 2004 T. absoluta and 478 B. (H.) concolorans adults were obtained from 221 infedted leaves. The number of parasitoids reaching the adult stage ranged from 1.7 adults to 2.6 adults per leaf. The study also included determinationss of the morphological characteristics of the Antalya population of the parasitoid. Accordingly, the average body length of the parasitoid was 2.13 mm in males and 2.37 mm in females. The average antenna length in males and females was 2.04 mm and 1.80 mm, respectively,and the number of antenna segments was 20-23 and 18-21, respectively.The average ovipositor length was 0.59 mm.

Bioefficacy of Certain Combination Insecticides against Insect Pests in Tomato

Bioefficacy of Certain Combination Insecticides against Insect Pests in Tomato

Potential of yellow sticky trap apropos monitoring and mass trapping of insect pests of tomato (Lycopersicon esculentum Miller): a field study

Potential of yellow sticky trap apropos monitoring and mass trapping of insect pests of tomato (Lycopersicon esculentum Miller): a field study

Enhanced Tomato Pest Detection via Leaf Imagery with a New Loss Function

Pests have caused significant losses to agriculture, greatly increasing the detection of pests in the planting process and the cost of pest management in the early stages. At this time, advances in computer vision and deep learning for the detection of pests appearing in the crop open the door to the application of target detection algorithms that can greatly improve the efficiency of tomato pest detection and play an important technical role in the realization of the intelligent planting of tomatoes. However, in the natural environment, tomato leaf pests are small in size, large in similarity, and large in environmental variability, and this type of situation can lead to greater detection difficulty. Aiming at the above problems, a network target detection model based on deep learning, YOLONDD, is proposed in this paper. Designing a new loss function, NMIoU (Normalized Wasserstein Distance with Mean Pairwise Distance Intersection over Union), which improves the ability of anomaly processing, improves the model’s ability to detect and identify objects of different scales, and improves the robustness to scale changes; Adding a Dynamic head (DyHead) with an attention mechanism will improve the detection ability of targets at different scales, reduce the number of computations and parameters, improve the accuracy of target detection, enhance the overall performance of the model, and accelerate the training process. Adding decoupled head to Head can effectively reduce the number of parameters and computational complexity and enhance the model’s generalization ability and robustness. The experimental results show that the average accuracy of YOLONDD can reach 90.1%, which is 3.33% higher than the original YOLOv5 algorithm and is better than SSD, Faster R-CNN, YOLOv7, YOLOv8, RetinaNet, and other target detection networks, and it can be more efficiently and accurately utilized in tomato leaf pest detection.

Benefits of intraguild interactions between zoophytophagous predators and of bottom-up effects of fertilisation for sustainable management of tomato pests

Benefits of intraguild interactions between zoophytophagous predators and of bottom-up effects of fertilisation for sustainable management of tomato pests

Investigating the effects of species niche shifts on the potential distribution of Tuta absoluta (Lepidoptera: Gelechiidae) by using global occurrence data.

Invasive species may occupy quite different environments in their invaded areas to native ones, which may intensively interfere with predicting potential distribution through ecological niche modeling (ENM). Here, we take the tomato leafminerTuta absoluta Meyrick (Lepidoptera: Gelechiidae), a tomato pest, as an example to investigate this topic. We analyzed niche expansion, stability, unfilling, and Schoener's D by principal component analysis (PCA)ordination method to examine its realized niche shifts and to explore how ENM approaches are affected by niche shifts. We used 5 datasets: Asian, African, European, South American, and global occurrence records in this study. Results showed that high niche unfilling for the species' invaded areas in Asia (20%), Africa (12%), and Europe (37%), possibly due to T. absoluta being in the early stages of invasion. High niche expansion was observed in Asia (38%) and Europe (19%), implying that some European and Asian populations had reached new climatic areas. African niche had the most niche stability (94%) and was equivalent to the native one in climate space (PCA ordination method), but the n-dimensional climate space framework showed that they were different. When projecting the native model to Asia and Europe, the native model performed poorly, implying that the niche shifts affected the transferability of the native model. ENM based on global data outperformed than other models, and our results suggested that T. absoluta has a large potential distribution in Asia, Mexico, South Europe, the United States, and Australia. Meanwhile, we recommend updating ENMs based on the species' invasion stage.

Zoophytophagous predator sex pheromone and visual cues of opposing reflectance spectra lure predator and invasive prey.

In sub-Saharan Africa, the invasive South American leafminer Phthorimaea absoluta is the most damaging tomato pest. Females of the pest can reproduce both sexually and through parthenogenesis and lay their eggs on all tomato plant parts. The mirid predator Nesidiocoris tenuis, a biological control agent for the pest, is also a tomato pest when prey population is low. To date, however, no study has developed an eco-friendly solution that targets both the predator and its host in a tomato farming system. To develop a bio-based management system for both pest and predator based on the combined use of sexual communication in the predator and visual cues. We collected volatiles from both sexes of the Kenyan population of the predator N. tenuis and identified candidate sex pheromone components by coupled gas chromatography-mass spectrometry (GC-MS). We used electrophysiological assays to identify antennally-active odorants in the volatiles, followed by field trials with different pheromone-baited colored traps to validate the responses of both predator and prey. Thereafter, we compared the reflectance spectra of the colored traps with those of different tomato plant tissues. Our results reveal an interplay between different sensory cues which in the predator-prey interaction may favor the predator. Antennae of both sexes of predator and prey detect the predator sex pheromone identified as 1-octanol and hexyl hexanoate. Unexpectedly, our field experiments led to the discovery of a lure for P. absoluta females, which were lured distinctly into a pheromone-baited trap whose reflectance spectrum mimicked that of ripe tomato fruit (long wavelength), an egg-laying site for females. Contrastingly, N. tenuis males were lured into baited white trap (short wavelength) when the predator is actively searching for prey. Our results demonstrate the novel use of a predator sex pheromone and different visual cues to assess complex trophic interactions on tomatoes.

ОЦЕНКА ЭФФЕКТИВНОСТИ ПРИМЕНЕНИЯ МЕТОДОВ БИООР-ГАНИЧЕСКОГО ЗЕМЛЕДЕЛИЯ ДЛЯ ОБЕСПЕЧЕНИЯ КОНКУ-РЕНТНЫХ ПРЕИМУЩЕСТВ ПРЕДПРИЯТИЯ

The article provides a justification for the introduction of adaptive biotechnologies to ensure the competitive advantages of SIRIUS M LLC in Nalchik, Kabardino-Balkarian Republic. The effectiveness of soil fertility management is shown by improving the plant nutrition system by replacing chemical fertilizers with microbiological ones. Comparative characteristics of various types of biofertilizers are presented, the scope of which extends to vegetable crops, and, above all, to tomatoes grown by SIRIUS M LLC. The effect of each fertilizer is considered. Recommendations on the use of microbial fertilizers are given. The costs of pur-chasing fertilizers and the effect of their use in comparison with chemical fertilizers are calculated. The possibility of replacing chemical plant protection products with insect entomophages is considered. Entomophages that destroy the most dangerous insect pests of tomatoes have been monitored. The effectiveness of the use of biological plant protection products has been determined. The cost of advertising is calculated, reflecting the competitive advantages of SIRIUS M due to the production of environmentally friendly products due to the replacement of chemi-cal preparations with biological ones. Various types of the resulting effect are con-sidered, depending on the proposed measures.

Response of Whitefly Population to Rabbit Urine Foliar Spray on Tomato (Solanum lycopersicum L.)

Whiteflies greatly contribute to low yields and quality in tomato production. Continuous use of synthetic pesticides leads to whiteflies developing resistance to these pesticides. This research aimed at promoting the use of rabbit urine as a biopesticide against whiteflies in tomato production. Although rabbit urine is been used as a biopesticide against different pests, the best concentration to control whiteflies in tomato production is unknown. Six treatments compared in the two experiments of this study included: 2 L rabbit urine : 9 L water; 2 L rabbit urine : 6 L water; 2 L rabbit urine : 3 L water; 2 L rabbit urine : 0 L water; 0 L rabbit urine : 2 L water, and 20 mL Duduthrin 1.75EC : 20 L water. The first experiment was conducted in a randomized complete block design with six treatments and four blocks to investigate the effects of rabbit urine on the control of whitefly eggs, nymphs, and adults. The second experiment was conducted in a completely randomized design to determine the effect of rabbit urine on the bioactivity of adult whiteflies. Results indicated that plots treated with rabbit urine foliar spray registered reduced populations of whitefly eggs, nymphs, and adults, and lower LT50 values of repelling and killing compared to the negative control (0 L rabbit urine : 2 L water). However, a high ratio of rabbit urine caused significant scorching to tomato leaves. A solution of 2 L rabbit urine : 6 L water optimally controls whiteflies without scorching the plant foliage. Rabbit urine has both repelling and killing effects on whiteflies. In the future, further research can be conducted to investigate the effect of rabbit urine on populations of other important insect pests of tomato.

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

Citizen science reinforces the development of emergent tools for the surveillance, monitoring, and early detection of biological invasions, enhancing biosecurity resilience. The contribution of farmers and farm citizens is vital, as volunteers can strengthen the effectiveness and efficiency of environmental observations, improve surveillance efforts, and aid in delimiting areas affected by plant-spread diseases and pests. This study presents a robust, user-friendly, and cost-effective smart module for citizen science that incorporates a cutting-edge developed hyperspectral imaging (HI) module, integrated in a single, energy-independent device and paired with a smartphone. The proposed module can empower farmers, farming communities, and citizens to easily capture and transmit data on crop conditions, plant disease symptoms (biotic and abiotic), and pest attacks. The developed HI-based module is interconnected with a smart embedded system (SES), which allows for the capture of hyperspectral images. Simultaneously, it enables multimodal analysis using the integrated environmental sensors on the module. These data are processed at the edge using lightweight Deep Learning algorithms for the detection and identification of Tuta absoluta (Meyrick), the most important invaded alien and devastating pest of tomato. The innovative Artificial Intelligence (AI)-based module offers open interfaces to passive surveillance platforms, Decision Support Systems (DSSs), and early warning surveillance systems, establishing a seamless environment where innovation and utility converge to enhance crop health and productivity and biodiversity protection.

Efficacy of pesticides against Nesidiocoris tenuis Reuter (Hemiptera: Miridae), an emerging threat in the cultivation of tomato in Northwest Europe

As non-chemical methods are not always effective to manage pests, the search for chemical solutions that efficiently combat pest species, while safeguarding the environment and beneficial organisms, is essential for advancing effective Integrated Pest Management (IPM) programs. Here, we investigated the efficacy of commercially available pesticides in managing Nesidiocoris tenuis, an emerging threat in the cultivation of tomato in Northwest Europe, while preserving the biocontrol agent Macrolophus pygmaeus. Fourteen pesticides were assessed for their residual toxicity on adults and nymphs in laboratory experiments, after which the most promising candidate was evaluated in cages and semi-commercial greenhouse conditions. Among the pesticides tested, chlorantraniliprole, hexythiazox, tebufenozide and azadirachtin had minimal impact on both insect species, with less than 37.5% corrected mortality. Pirimicarb, sulfoxaflor, acetamiprid, flupyradifurone, thiacloprid, tebufenpyrad, spinosad, indoxacarb, and a mixture of pyrethrins and piperonyl butoxide were harmful to N. tenuis, but also affected M. pygmaeus (up to 100% mortality). Emamectin benzoate was moderately effective to N. tenuis adults (87%) and nymphs (80%), but had limited effects on M. pygmaeus. Application of emamectin benzoate at the maximum recommended concentration resulted in a significantly higher mortality of N. tenuis adults (85%) compared to M. pygmaeus (53%) in cage experiments, while it significantly reduced N. tenuis nymphs and the number of necrotic rings in a greenhouse experiment. Emamectin benzoate emerges as a promising candidate for inclusion in IPM programs. However, further research is necessary to determine its compatibility with other management strategies for controlling N. tenuis and other tomato pests.

Economic impact of pests on tomato production in southern and central Benin: The cases of Helicoverpa armigera, Tuta absoluta, Tetranychus evansi, and root-knot nematodes

Tomato (Lycopersicon esculentum Mill), the most cultivated and consumed fruit vegetable in Benin, is subjected to parasitic attacks reducing its productivity. This study assesses the economic impacts of Helicoverpa armigera, Tuta absoluta, Tetranychus evansi and root-knot nematodes. One hundred and forty-four (144) focus group sessions, supported by individual interviews with 60 farmers were conducted from August 24 to 29, 2020, and from September 7 to 12, 2020 in eighteen (18) Districts in southern and central Benin. The results indicated that growers recognize different pests affecting tomato farming, especially by symptoms on it fruit, leaves, stem and root system. Helicoverpa armigera has the highest gross output reduction rate for both severe (78.3%) and moderate (38%) attacks. This reduction corresponds to losses of 104.62 kg and 50.75 kg on a harvest of 133.62 kg per plank of 1.2m x 13m (i.e. 15.6 m²), equivalent to average economic impacts of 37,052.92 F CFA and 17,973.96 F CFA respectively for severe and moderate attacks. Helicoverpa armigera is the main pest of tomato against which the development of control technologies appears more relevant.

Tomato pest recognition using convolutional neural network in Bangladesh

The tomato is one of the most popular and well-liked veggies among Asians. It is interesting to note that in Bangladesh, it is the second most significant vegetable consumed. Moreover, tomato is served not only as a vegetable, but it is also served as sauce, jam, etc., and used in making different types of cuisines. But the fact is due to the pests, thousands of tons of tomatoes are harmed every year in Bangladesh. The production of tomatoes in Bangladesh is harmed by a number of dangerous pests. We develop a solution to recognize pests at an early stage. Five different pest types, including aphids, red spider mites, whiteflies, looper caterpillars, and thrips, have been studied in this research. To identify tomato pests, we curated image datasets from online and offline repositories and processed them using a convolutional neural network (CNN) model. We used features from CNN layers for three machine learning algorithms: Random Forest (RF), support vector machine (SVM), and K-Nearest Neighbors (K-NN). This comprehensive approach allowed a thorough comparison of these algorithms in tomato pest recognition. For recognizing tomato pests, our methods generate excellent results. The accuracy of our experiment is 95.49% which indicates the successful completion of the experiment.

Application of Real-Time Deep Learning in integrated Surveillance of Maize and Tomato Pests and Bacterial Diseases

With an emphasis on maize and tomato crops specifically, this research explores the creative fusion of computer vision (CV) and machine learning (ML) to address the enduring problem of pests and crop diseases impacting Kenya's crucial agricultural industry. This study aims to provide farmers with a reliable and accurate tool for identifying pests and diagnosing diseases by using a MobileNetV2-based model. An extensive dataset including photos of both healthy and sick crops was gathered, and preprocessing approaches, such as data augmentation, were used to improve the model's training procedure. The resulting mobile application for real-time digital imaging signifies a major advancement in agriculture technology as it provides farmers with rapid and accessible diagnostic capabilities in addition to professional advice. The transformational potential of CV and ML in transforming agricultural practices is highlighted by this research, despite faced constraints such as data limits and technical accessibility. This research proposes a future where these cutting-edge technologies serve as pillars fortifying food security, empowering farmers with actionable insights, and significantly mitigating crop losses in Kenyan agriculture. It does this by arguing for improved data collection strategies, addressing algorithmic biases, and encouraging technology adoption. This paper contributes an important step toward a more technologically advanced farming environment, where innovative solutions utilizing CV and ML methodologies promise to improve farmer livelihoods and protect Kenya's agriculture from widespread threats in addition to preserving crop health.

Associational Resistance Using Wild and Commercial Tomato Genotypes Employed in the Management of Tomato Virus Vectors

Some of the key pests of tomatoes are virus vectors, such as whiteflies, green peach aphids, and thrips, mainly because there is a lack of full resistance to the transmitted viruses. Alternatives to reduce this problem include the use of a push-and-pull strategy. Hence, this work assessed the association between Solanum habrochaites PI 1344117, used as a companion plant, and commercial tomato varieties over tomato virus vectors and the yield quality and quantity. Field and greenhouse trials were run during the 2015, 2016, 2018, and 2019 seasons. The treatments were BRS Tospodoro and BRS Tyão cultivated as monocrops and associated with PI-134417. The number of these insects was assessed by directly counting ten plants per plot and using captures on yellow sticky traps settled in the center of the plots. The yield and the number of irregularly ripening fruits (a symptom caused by whiteflies) were also measured. Both commercial cultivars gained from the protective effect of PI 134417, expressed by the significant reduction in the virus vectors on the plants. It was also noticed that there was a reduction in the number of irregularly ripening fruits, improving the fruit quality. These results encourage the use of wild and commercial tomato genotypes in association with implementing strategies to control tomato virus vectors, reducing the need to rely only on a chemical control.