GIS and Remote Sensing for Detecting Yield Loss in Cranberry Culture

The primary goal of our research is to develop key elements of a precision agriculture program applicable to high-value woody perennial crops, such as cranberries. These crop systems exhibit tremendous variability in crop yields and quality as imposed by variations in soil properties (water availability and nutrient deficiency) that lead to crop stress (disease development and weed competition). Some of the variability present in the growing environment results in persistent yield losses as well as crop-quality reductions. We are using state-of-the-art methodologies (GIS, GPS, remote sensing) to identify and map spatial variations of the crop. Through image-processing methods (NDVI and unsupervised classification), approximately 65% of the variation in yield was described using 4-m multispectral satellite data as a base image.     

DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection

Plant-leaf disease detection is one of the key problems of smart agriculture which has a significant impact on the global economy. To mitigate this, intelligent agricultural solutions are evolving that aid farmer to take preventive measures for improving crop production. With the advancement of deep learning, many convolutional neural network models have blazed their way to the identification of plant-leaf diseases. However, these models are limited to the detection of specific crops only. Therefore, this paper presents a new deeper lightweight convolutional neural network architecture (DLMC-Net) to perform plant leaf disease detection across multiple crops for real-time agricultural applications. In the proposed model, a sequence of collective blocks is introduced along with the passage layer to extract deep features. These benefits in feature propagation and feature reuse, which results in handling the vanishing gradient problem. Moreover, point-wise and separable convolution blocks are employed to reduce the number of trainable parameters. The efficacy of the proposed DLMC-Net model is validated across four publicly available datasets, namely citrus, cucumber, grapes, and tomato. Experimental results of the proposed model are compared against seven state-of-the-art models on eight parameters, namely accuracy, error, precision, recall, sensitivity, specificity, F1-score, and Matthews correlation coefficient. Experiments demonstrate that the proposed model has surpassed all the considered models, even under complex background conditions, with an accuracy of 93.56%, 92.34%, 99.50%, and 96.56% on citrus, cucumber, grapes, and tomato, respectively. Moreover, the proposed DLMC-Net requires only 6.4 million trainable parameters, which is the second best among the compared models. Therefore, it can be asserted that the proposed model is a viable alternative to perform plant leaf disease detection across multiple crops.     

Recognition method of soybean leaf diseases using residual neural network based on transfer learning

Leaf disease is an important factor restricting the high quality and high yield of the soybean plant. Insufficient control of soybean diseases will destroy the local ecological environment and break the stability of the food chain. To overcome the low accuracy in recognizing soybean leaf diseases using traditional deep learning models and complexity in chemical analysis operations, in this study, a recognition model of soybean leaf diseases was proposed based on an improved deep learning model. First, four types of soybean diseases (Septoria Glycines Hemmi, Soybean Brown Leaf Spot, Soybean Frogeye Leaf Spot, and Soybean Phyllosticta Leaf Spot) were taken as research objects. Second, image preprocessing and data expansion of original images were carried out using image registration, image segmentation, region calibration and data enhancement. The data set containing 53, 250 samples was randomly divided into the training set, verification set, and test set according to the ratio of 7:2:1. Third, the convolution layer weight of the pre-training model based on the ImageNet open data set was transferred to the convolution layer of the ResNet18 model to reconstruct the global average pooling layer and the fully connected layer for constructing recognition model of TRNet18 model. Finally, the recognition accuracy of the four leaf diseases reached 99.53%, the Macro-F1 was 99.54%, and the average recognition time was 0.047184 s. Compared with AlexNet, ResNet18, ResNet50, and TRNet50 models, the recognition accuracy and Macro-F1 of the TRNet18 model were improved by 6.03% and 5.99% respectively, and the model recognition time was saved by 16.67%, The results showed that the proposed TRNet18 model had higher classification accuracy and stronger robustness, which can not only provide a reference for accurate recognition of other crop diseases, but also be transplanted to the mobile terminal for recognition of crop leaf diseases.     

A novel multi-head CNN design to identify plant diseases using the fusion of RGB images

Plant diseases and insect pests cause a significant threat to agricultural production. Early detection and diagnosis of these diseases are critical and can reduce economic losses. The recent development of deep learning (DL) benefits various fields, such as image processing, remote sensing, medical diagnosis, and agriculture. This work proposed a novel approach based on DL for plant disease detection by fusing RGB and segmented images. A multi-headed DenseNet-based architecture was developed, considering two images as input. We evaluated the model on a public dataset, PlantVillage, consisting of 54183 images with 38 classes. The fivefold cross-validation technique achieved an average accuracy, recall, precision, and f1-score of 98.17%, 98.17%, 98.16%, and 98.12%, respectively. The proposed approach can distinguish various plant diseases with different characteristics by image fusion. The high success rate with low standard deviation proves the robustness of the model, and the model can be integrated into plant disease detection and early warning system.     

Unsupervised Retinal Vessel Segmentation Using Combined Filters

Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels’ appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi’s filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.     

Detection and classification of chilli leaf disease using a squeeze-and-excitation-based CNN model

Chilli leaf disease has a destructive effect on the chilli crop yield. Chilli leaf disease can result in a significant decrease in both the quantity and quality of the chilli crop. Early detection, perfect identification and accurately diagnosing the disease will aid in increasing the profit of the cultivator. However, after a comprehensive survey investigation, we discovered that no studies have been previously conducted to compare the classification performance of machine learning and deep learning for the chilli leaf disease classification problem. In this study, five main leaf diseases i.e. down curl of a leaf, Geminivirus, Cercospora leaf spot, yellow leaf disease, and up curl disease were identified, and images were captured using a digital camera and are labelled. These diseases were classified using 12 different pretrained deep learning networks (AlexNet, DarkNet53, DenseNet201, EfficientNetb0, InceptionV3, MobileNetV2, NasNetLarge, ResNet101, ShuffleNet, SqueezeNet, VGG19, and XceptionNet) using chilli leaf data with and without augmentation using deep learning transfer. Performance metrics such as accuracy, recall, precision, F1-score, specificity, and misclassification were calculated for each network. VGG19 had the best accuracy (83.54%) without augmentation, and DarkNet53 had the best result (98.82%) with augmentation among all pretrained deep learning networks in our self-built chilli leaf dataset. The result was enhanced by designing a squeeze-and-excitation-based convolutional neural network (SECNN) model. The model was tested on a chilli leaf dataset with different input sizes and mini-batch sizes. The proposed model produced the best accuracy of 98.63% and 99.12% without and with augmentation, respectively. The SECNN model was also tested on different datasets from the PlantVillage data, including apple, cherry, corn, grape, peach, pepper, potato, strawberry, and tomato leaves, separately and with the chilli dataset. The proposed model achieved an accuracy of 99.28% in classifying 43 different classes of plant leaf datasets.     

Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images

Accurate and timely detection of plant diseases can help mitigate the worldwide losses experienced by the horticulture and agriculture industries each year. Thermal imaging provides a fast and non-destructive way of scanning plants for diseased regions and has been used by various researchers to study the effect of disease on the thermal profile of a plant. However, thermal image of a plant affected by disease has been known to be affected by environmental conditions which include leaf angles and depth of the canopy areas accessible to the thermal imaging camera. In this paper, we combine thermal and visible light image data with depth information and develop a machine learning system to remotely detect plants infected with the tomato powdery mildew fungus Oidium neolycopersici. We extract a novel feature set from the image data using local and global statistics and show that by combining these with the depth information, we can considerably improve the accuracy of detection of the diseased plants. In addition, we show that our novel feature set is capable of identifying plants which were not originally inoculated with the fungus at the start of the experiment but which subsequently developed disease through natural transmission.     

Segmentation of parotid glands from registered CT and MR images

PurposeTo develop an automatic multimodal method for segmentation of parotid glands (PGs) from pre-registered computed tomography (CT) and magnetic resonance (MR) images and compare its results to the results of an existing state-of-the-art algorithm that segments PGs from CT images only.MethodsMagnetic resonance images of head and neck were registered to the accompanying CT images using two different state-of-the-art registration procedures. The reference domains of registered image pairs were divided on the complementary PG regions and backgrounds according to the manual delineation of PGs on CT images, provided by a physician. Patches of intensity values from both image modalities, centered around randomly sampled voxels from the reference domain, served as positive or negative samples in the training of the convolutional neural network (CNN) classifier. The trained CNN accepted a previously unseen (registered) image pair and classified its voxels according to the resemblance of its patches to the patches used for training. The final segmentation was refined using a graph-cut algorithm, followed by the dilate-erode operations.ResultsUsing the same image dataset, segmentation of PGs was performed using the proposed multimodal algorithm and an existing monomodal algorithm, which segments PGs from CT images only. The mean value of the achieved Dice overlapping coefficient for the proposed algorithm was 78.8%, while the corresponding mean value for the monomodal algorithm was 76.5%.ConclusionsAutomatic PG segmentation on the planning CT image can be augmented with the MR image modality, leading to an improved RT planning of head and neck cancer.     

Accurate Image Analysis of the Retina Using Hessian Matrix and Binarisation of Thresholded Entropy with Application of Texture Mapping

In this paper, we demonstrate a comprehensive method for segmenting the retinal vasculature in camera images of the fundus. This is of interest in the area of diagnostics for eye diseases that affect the blood vessels in the eye. In a departure from other state-of-the-art methods, vessels are first pre-grouped together with graph partitioning, using a spectral clustering technique based on morphological features. Local curvature is estimated over the whole image using eigenvalues of Hessian matrix in order to enhance the vessels, which appear as ridges in images of the retina. The result is combined with a binarized image, obtained using a threshold that maximizes entropy, to extract the retinal vessels from the background. Speckle type noise is reduced by applying a connectivity constraint on the extracted curvature based enhanced image. This constraint is varied over the image according to each region''s predominant blood vessel size. The resultant image exhibits the central light reflex of retinal arteries and veins, which prevents the segmentation of whole vessels. To address this, the earlier entropy-based binarization technique is repeated on the original image, but crucially, with a different threshold to incorporate the central reflex vessels. The final segmentation is achieved by combining the segmented vessels with and without central light reflex. We carry out our approach on DRIVE and REVIEW, two publicly available collections of retinal images for research purposes. The obtained results are compared with state-of-the-art methods in the literature using metrics such as sensitivity (true positive rate), selectivity (false positive rate) and accuracy rates for the DRIVE images and measured vessel widths for the REVIEW images. Our approach out-performs the methods in the literature.     

A recognition method of multispectral images of soybean canopies based on neural network

Multispectral images of soybean canopies can reflect plant physiological information and growth status effectively, which is of great significance for soybean high-quality breeding, scientific cultivation, and fine management. At present, it is uneven of the gray level difference of the soybean multispectral images occurred in the leaf edge, and is also small of the gray level difference between the target and the background, resulting in inaccurate recognition of the soybean canopies from the multispectral images. Thus, a multispectral images' recognition method of soybean canopies was proposed based on the neural network. First, the method of Gaussian smoothing filter was used to preprocess the raw soybean multispectral images (green light, near-infrared, red light, red edge, and visible light), which maintained the leaf edge details of the soybean multispectral image. Second, the feedforward neural network model was established to extract the canopy region in the soybean multispectral images. In addition, image morphology operation was used to improve the recognition effects of the soybean canopy. Finally, four quantitative indexes were used to evaluate the experimental results. The results showed that the average effective segmentation rate of the proposed method was 91.69%, the under-segmentation rate was reduced by 33.34%, and the over-segmentation rate was reduced by 48.43%. The difference between the pixel average entropy of the proposed method and the standard canopy image was only 0.2295. The research results can provide not only reliable data for further analysis of physiological and ecological indexes of the soybean canopy, but also technical support for multispectral image recognition of other crop canopies.     

Emergence of 13_A2 Blue Lineage of Phytophthora infestans was Responsible for Severe Outbreaks of Late Blight on Tomato in South‐West India

Prior to 2007, late blight was not reported as a serious threat to tomato cultivation in India although the disease has been known on potato since 1953. During the July–December cropping season of 2009 and 2010, severe late blight epidemics were observed in Karnataka state of India, causing crop losses up to 100%. Nineteen Phytophthora isolates, recovered from late blight affected tomato tissues from different localities in Karnataka state between 2009 and 2010, were identified as Phytophthora infestans based on morphology, a similarity search of ITS sequences at GenBank and species‐specific PCR using PINF/ITS5 primer pair. The isolates were further assessed for metalaxyl sensitivity, mating type, mitochondrial DNA (mtDNA) haplotype, DNA fingerprinting patterns based on simple sequence repeats (SSR) and RFLPs using the RG57 probe and aggressiveness on tomato. All isolates were metalaxyl resistant, A2 mating type, mtDNA haplotype Ia and had identical SSR and RG57 fingerprints and highly aggressive on tomato. The phenotypic and genotypic characters of isolates examined in this study were found to be similar to that of 13_A2 genotype of P. infestans population reported in Europe. Thus, appearance of new population similar to 13_A2 genotype was responsible for severe late blight epidemics on tomato in South‐West India.     

CoordConv-Unet: Investigating CoordConv for Organ Segmentation

ObjectivesConvolutional neural networks (CNNs) have established state-of-the-art performance in computer vision tasks such as object detection and segmentation. One of the major remaining challenges concerns their ability to capture consistent spatial and anatomically plausible attributes in medical image segmentation. To address this issue, many works advocate to integrate prior information at the level of the loss function. However, prior-based losses often suffer from local solutions and training instability. The CoordConv layers are extensions of convolutional neural network wherein convolution is conditioned on spatial coordinates. The objective of this paper is to investigate CoordConv as a proficient substitute to convolutional layers for medical image segmentation tasks when trained under prior-based losses.MethodsThis work introduces CoordConv-Unet which is a novel structure that can be used to accommodate training under anatomical prior losses. The proposed architecture demonstrates a dual role relative to prior constrained CNN learning: it either demonstrates a regularizing role that stabilizes learning while maintaining system performance, or improves system performance by allowing the learning to be more stable and to evade local minima.ResultsTo validate the performance of the proposed model, experiments are conducted on two well-known public datasets from the Decathlon challenge: a mono-modal MRI dataset dedicated to segmentation of the left atrium, and a CT image dataset whose objective is to segment the spleen, an organ characterized with varying size and mild convexity issues.ConclusionResults show that, despite the inadequacy of CoordConv when trained with the regular dice baseline loss, the proposed CoordConv-Unet structure can improve significantly model performance when trained under anatomically constrained prior losses.     

Eikonal based region growing for superpixels generation: Application to semi-supervised real time organ segmentation in CT images

We describe a semi-supervised organ segmentation method for Computed Tomography images. In a first step, a dense oversegmentation of the image is created with an Eikonal-based algorithm. The proposed superpixel algorithm ourperforms state-of-the-art algorithms on classical metrics. In a second step, the semi-supervised segmentation is performed on the underlying Region Adjacency Graph created from the oversegmentation. As the complexity is greatly reduced, the organ segmentation can be performed in real-time.     

4D Segmentation of Brain MR Images with Constrained Cortical Thickness Variation

Segmentation of brain MR images plays an important role in longitudinal investigation of developmental, aging, disease progression changes in the cerebral cortex. However, most existing brain segmentation methods consider multiple time-point images individually and thus cannot achieve longitudinal consistency. For example, cortical thickness measured from the segmented image will contain unnecessary temporal variations, which will affect the time related change pattern and eventually reduce the statistical power of analysis. In this paper, we propose a 4D segmentation framework for the adult brain MR images with the constraint of cortical thickness variations. Specifically, we utilize local intensity information to address the intensity inhomogeneity, spatial cortical thickness constraint to maintain the cortical thickness being within a reasonable range, and temporal cortical thickness variation constraint in neighboring time-points to suppress the artificial variations. The proposed method has been tested on BLSA dataset and ADNI dataset with promising results. Both qualitative and quantitative experimental results demonstrate the advantage of the proposed method, in comparison to other state-of-the-art 4D segmentation methods.     

A modified U-net-based architecture for segmentation of satellite images on a novel dataset

The remote-sensing-based satellite images have been providing a wealth of information to the scientists for study of environmental changes caused by climate changes or human activities such as destructive cyclones and earthquakes etc. This paper proposes a deep learning-based segmentation model for agriculture images captured from satellites and a novel agriculture-based satellite dataset. The segmentation has been performed on the satellite images into five categories of cultivated land, uncultivated land, residences, water, and forest. The dataset has been created using Sentinel-2 satellite data over the Panipat district in Haryana, India having diversity in crops and land usage. The dataset consists of 16,720 images and their corresponding masks over the years ranging from 2018 to 2020. The proposed model consists of a six-phase encoder-decoder network with a total of 33 convolution layers. The proposed segmentation model has been evaluated on proposed dataset and obtained an efficient metric of 72% IoU score which is better than state-of-the-art models such as U-Net, Link-Net, FPN and DeeplabV3+ score 51%, 46%, 49%, 67% IoU respectively.     

A direction‐selective local‐thresholding method,DSLT, in combination with a dye‐based method for automated three‐dimensional segmentation of cells and airspaces in developing leaves

Quantifying the anatomical data acquired from three‐dimensional (3D) images has become increasingly important in recent years. Visualization and image segmentation are essential for acquiring accurate and detailed anatomical data from images; however, plant tissues such as leaves are difficult to image by confocal or multi‐photon laser scanning microscopy because their airspaces generate optical aberrations. To overcome this problem, we established a staining method based on Nile Red in silicone‐oil solution. Our staining method enables color differentiation between lipid bilayer membranes and airspaces, while minimizing any damage to leaf development. By repeated applications of our staining method we performed time‐lapse imaging of a leaf over 5 days. To counteract the drastic decline in signal‐to‐noise ratio at greater tissue depths, we also developed a local thresholding method (direction‐selective local thresholding, DSLT) and an automated iterative segmentation algorithm. The segmentation algorithm uses the DSLT to extract the anatomical structures. Using the proposed methods, we accurately segmented 3D images of intact leaves to single‐cell resolution, and measured the airspace volumes in intact leaves.     

Deep learning-based segmentation for disease identification

Plant diseases have recently increased and exacerbated due to several factors such as climate change, chemicals’ misuse and pollution. They represent a severe threat for both economy and global food security. Recently, several researches have been proposed for plant disease identification through modern image-based recognition systems based on deep learning. However, several challenges still require further investigation. One is related to the high variety of leaf diseases/ species along with constraints related to the collection and annotation of real-world datasets. Other challenges are related to the study of leaf disease in uncontrolled environment. Compared to major existing researches, we propose in this article a new perspective to handle the problem with two main differences: First, while most approach aims to identify simultaneously a pair of species-disease, we propose to identify diseases independently of leaf species. This helps to recognize new species holding diseases that were previously learnt. Moreover, instead of using the global leaf image, we directly predict disease on the basis of the local disease symptom features. We believe that this may decrease the bias related to common context and/or background and enables to build a more generalised model for disease classification. In particular, we propose an hybrid system that combines strengths of deep learning-based semantic segmentation with classification capabilities to respectively extract infected regions and determine their identity. For that, an extensive experimentation including a comparison of different semantic segmentation and classification CNNs has been conducted on PlantVillage dataset (leaves within homogeneous background) in order to study the extent of use of local disease symptoms features to identify diseases. Specifically, a particular enhancement of disease identification accuracy has been demonstrated in IPM and BING datasets (leaves within uncontrolled background).     

Cuticular wax variation in the tomato (Solanum lycopersicum L.), related wild species and their interspecific hybrids

The tomato (Solanum lycopersicum L.) is one of the world's most important vegetable crop species. Among the many tomato accessions available, only a few are tolerant to abiotic stresses, which are responsible for the majority of the crop losses worldwide. Wild tomato species are then secondary gene pool in the breeding of more resistant tomato cultivars. In the current study, the composition of leaf cuticular waxes from fourteen tomato accessions, including S. lycopersicum, Solanum pennellii, Solanum pimpinellifolium, and their interspecific hybrids was studied in order to select the most adequate chemotaxonomic markers. Total cuticular wax load of S. pennellii plants was much higher than in the other plant species. Hydrocarbons were usually the most abundant wax components, followed by minor quantities of triterpenes and other compounds. Interspecific hybrids showed intermediate wax characteristics. The amount and composition of surface waxes were not correlated with the abiotic stress tolerance in S. lycopersicum. The composition of the hydrocarbon fraction was the least variable both within a single accession and between all the plants studied. Based on the results, cuticular hydrocarbons are proposed as potential chemotaxonomic markers in the classification of tomato and related species.     

Occurrence of whitefly-transmitted geminiviruses in crops in Burkina Faso, and their serological detection and differentiation

Whitefly-transmitted geminiviruses were found to be associated with four diseases of crop plants in Burkina Faso: cassava mosaic, okra leaf curl, tobacco leaf curl and tomato yellow leaf curl. Tomato yellow leaf curl is an economically serious disease, reaching a high incidence in March, following a peak population of the vector whitefly, Bemisia tabaci, in December. Okra leaf curl is also a problem in the small area of okra grown in the dry season but is not important in the main period of okra production in the rainy season. The geminiviruses causing these four diseases, African cassava mosaic (ACMV), okra leaf curl (OLCV), tobacco leaf curl (TobLCV) and tomato yellow leaf curl (TYLCV) viruses, were each detected in field-collected samples by triple antibody sand-wich-ELISA with cross-reacting monoclonal antibodies (MAbs) to ACMV. Epitope profiles obtained by testing each virus isolate with panels of MAbs to ACMV, OLCV and Indian cassava mosaic virus enabled four viruses to be distinguished. ACMV and OLCV had similar but distinguishable profiles. The epitope profile of TobLCV was the same as that of one form of TYLCV (which may be the same virus) and was close to the profile of TYLCV from Sardinia. The other form of TYLCV reacted with several additional MAbs and had an epitope profile close to that of TYLCV from Senegal. Only minor variations within each of these four types of epitope profile were found among geminivirus isolates from Burkina Faso. Sida acuta is a wild host of OLCV.     

Severe outbreaks of tomato yellow leaf curl Sardinia virus in Calabria, Southern Italy

During the winter 2003--2004 a serious disease was observed in protected tomato crops in Castrovillari, Reggio Calabria province, Southern Italy. Symptoms consisted in marginal leaf yellowing, leaf curling, plant stunting, flower abortion. The disease was detected in a group of greenhouses (about 10ha) where several tomato cultivars were grown hydroponically. The highest incidence of infection (60-100%) was observed in tomatoes grafted on Beaufort DRS tomato rootstock. Since the symptoms were similar to those described for Tomato yellow leaf curl Sardinia virus (TYLCSV) and Tomato yellow leaf curl virus (TYLCV), detection assays for these viruses were used. In DAS-ELISA positive results were obtained with a abroad-spectrums reagent combination (distributed by Bioreba AG) detecting TYLCV, TYLCSV, and other begomoviruses. When DNA probes were used in tissue print assays, positive reactions were obtained for TYLCSV, but not for TYLCV. The two probes consisted of digoxigenin-labelled DNAs representing the coat protein gene of either TYLCSV or TYLCV. Attempts to isolate the viral agent by mechanical inoculation failed, except in few cases where Potato virus Y and Tobacco mosaic virus were identified following transmission from symptomatic plants to herbaceous indicatorpplants. By contrast, grafting onto tomato seedlings always successfully transmitted the disease. In the Castrovillari area TYLCSV was not reported before. The rootstocks that nurseries used for grafting were obtained from Sicily, where the disease is endemic and both TYLCSV and TYLCV are widespread. Probably the grafted plantlets represented the primary source of infection from which subsequent diffusion by way of the vector Bemisia tabaci followed. In fact the vector had previously been detected in both the glasshouse-grown and open field tomato crops in Calabria region. TYLCV was previously reported in a different area of Calabria in 1991, but apparently it was an occasional outbreak, and B. tabaci was not detected. Since in the Castrovillari area surveyed in the present study tomato is grown throughtout the year in protected crops, the whitefly vector of the virus is present, and some natural hosts of the virus are found, it is feared that TYLCSV may become endemic, as already happened in Sicily, Sardinia, and Spain several years ago. In Spain and Sicily TYLCV, together with TYLCSV, was reported as the causal agent of very severe tomato crop losses. Therefore the danger exists that also TYLCV will reach this area, furthermore complicating the management of tomato crops.