Tomato Diseases, Their Impact, and Management

2021 ◽  
pp. 1-23
Author(s):  
S. S. Solankey ◽  
P. K. Ray ◽  
Meenakshi Kumari ◽  
H. K. Singh ◽  
Md. Shamim ◽  
...  
Keyword(s):  
Tomato Diseases

CHEMICAL CONTROL OF TOMATO DISEASES IN CHINA

2009 ◽  
pp. 315-316
Author(s):  
Laixin Luo ◽  
Xili Liu ◽  
Pengfei Liu ◽  
Hasan Bolkan ◽  
Jianqiang Li
Keyword(s):  
Chemical Control ◽  
Tomato Diseases

scholarly journals Fungicide Resistance Management Guidelines for the Control of Tomato Diseases in the Mid-Atlantic and Northeast Regions of the United States

2010 ◽  
Vol 11 (1) ◽  
pp. 32 ◽  
Author(s):  
Christian A. Wyenandt ◽  
Steven L. Rideout ◽  
Beth K. Gugino ◽  
Margaret T. McGrath ◽  
Kathryne L. Everts ◽  
...  
Keyword(s):  
United States ◽  
Fungicide Resistance ◽  
Resistance Management ◽  
The United States ◽  
Management Tool ◽  
Fresh Market ◽  
Management Guidelines ◽  
Control Programs ◽  
Action Committee ◽  
Tomato Diseases

Foliar diseases and fruit rots occur routinely on tomato, an important crop grown throughout the Mid-Atlantic and Northeast regions of the United States where it is produced for both fresh-market and processing. To enable these tomato growers to more effectively manage economically important diseases, a fungicide resistance management table has been developed which promotes the importance of understanding FRAC (Fungicide Resistance Action Committee) codes and provides an integrated pest management tool for tomato growers which will allow them to develop season-long disease control programs with an emphasis on fungicide resistance management. Accepted for publication 19 July 2010. Published 27 August 2010.

scholarly journals Plant Disease Identification Using Shallow Convolutional Neural Network

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2388
Author(s):  
Sk Mahmudul Hassan ◽  
Michal Jasinski ◽  
Zbigniew Leonowicz ◽  
Elzbieta Jasinska ◽  
Arnab Kumar Maji
Keyword(s):  
Deep Learning ◽  
Late Blight ◽  
Early Blight ◽  
Gray Leaf Spot ◽  
Plant Diseases ◽  
Automated Identification ◽  
Average Accuracy ◽  
Proposed Model ◽  
Effective Manner ◽  
Tomato Diseases

Various plant diseases are major threats to agriculture. For timely control of different plant diseases in effective manner, automated identification of diseases are highly beneficial. So far, different techniques have been used to identify the diseases in plants. Deep learning is among the most widely used techniques in recent times due to its impressive results. In this work, we have proposed two methods namely shallow VGG with RF and shallow VGG with Xgboost to identify the diseases. The proposed model is compared with other hand-crafted and deep learning-based approaches. The experiments are carried on three different plants namely corn, potato, and tomato. The considered diseases in corns are Blight, Common rust, and Gray leaf spot, diseases in potatoes are early blight and late blight, and tomato diseases are bacterial spot, early blight, and late blight. The result shows that our implemented shallow VGG with Xgboost model outperforms different deep learning models in terms of accuracy, precision, recall, f1-score, and specificity. Shallow Visual Geometric Group (VGG) with Xgboost gives the highest accuracy rate of 94.47% in corn, 98.74% in potato, and 93.91% in the tomato dataset. The models are also tested with field images of potato, corn, and tomato. Even in field image the average accuracy obtained using shallow VGG with Xgboost are 94.22%, 97.36%, and 93.14%, respectively.

scholarly journals Efficiency of phylloplane bacteria in controlling aerial tomato diseases under field conditions

2008 ◽  
Vol 34 (1) ◽  
pp. 86-87 ◽  
Author(s):  
Bernardo A. Halfeld-Vieira ◽  
Reginaldo S. Romeiro ◽  
Ann Mounteer ◽  
Eduardo S.G. Mizubuti
Keyword(s):  
Late Blight ◽  
Bacillus Cereus ◽  
Phytophthora Infestans ◽  
Field Conditions ◽  
Water Control ◽  
Tomato Plants ◽  
Tomato Diseases

The capacity of two bacteria isolated from the tomato phylloplane to control late blight (Phytophthora infestans) was investigated in the field, and compared against the effectiveness of spraying with the fungicide chlorothalonil (1.5 g a.i. L-1) or water (control). A 55% reduction in late blight intensity was observed in the leaves of the middle of the plant and 62% in those of the upper leaves when using the antagonist UFV-STB 6 (Novosphingobium capsulatum) as compared to the control. Isolate UFV-IEA 6 (Bacillus cereus) was able to reduce disease intensity by 55%, but only in the upper leaves of the tomato plants. Treatment with isolate UFV-STB 6 also led to a significant reduction in the percentage of fruits with late blight symptoms. The results demonstrate the potential of these two bacteria in controlling this disease.

scholarly journals Deep Learning Based Classification for Tomato Diseases Recognition

2020 ◽  
Vol 474 ◽  
pp. 032014
Author(s):  
Tao Zhang ◽  
Xiankun Zhu ◽  
Yiqing Liu ◽  
Kun Zhang ◽  
Azhar Imran
Keyword(s):  
Deep Learning ◽  
Tomato Diseases

A molecular linkage map of tomato displaying chromosomal locations of resistance gene analogs based on a Lycopersicon esculentum × Lycopersicon hirsutum cross

Genome ◽  
2002 ◽  
Vol 45 (1) ◽  
pp. 133-146 ◽  
Author(s):  
L P Zhang ◽  
A Khan ◽  
D Niño-Liu ◽  
M R Foolad
Keyword(s):  
Disease Resistance ◽  
Restriction Fragment Length Polymorphism ◽  
Fragment Length Polymorphism ◽  
Rflp Markers ◽  
Resistance Gene Analogs ◽  
Lycopersicon Hirsutum ◽  
R Genes ◽  
Molecular Linkage Map ◽  
Tomato Diseases ◽  
Restriction Fragment Length

A molecular linkage map of tomato was constructed based on a BC1 population (N = 145) of a cross between Lycopersicon esculentum Mill. line NC84173 (maternal and recurrent parent) and Lycopersicon hirsutum Humb. and Bonpl. accession PI126445. NC84173 is an advanced breeding line that is resistant to several tomato diseases, not including early blight (EB) and late blight (LB). PI126445 is a self-incompatible accession that is resistant to many tomato diseases, including EB and LB. The map included 142 restriction fragment length polymorphism (RFLP) markers and 29 resistance gene analogs (RGAs). RGA loci were identified by PCR amplification of genomic DNA from the BC1 population, using ten pairs of degenerate oligonucleotide primers designed based on conserved leucine-rich repeat (LRR), nucleotide binding site (NBS), and serine (threonine) protein kinase (PtoKin) domains of known resistance genes (R genes). The PCR-amplified DNAs were separated by denaturing polyacrylamide gel electrophoresis (PAGE), which allowed separation of heterogeneous products and identification and mapping of individual RGA loci. The map spanned 1469 cM of the 12 tomato chromosomes with an average marker distance of 8.6 cM. The RGA loci were mapped to 9 of the 12 tomato chromosomes. Locations of some RGAs coincided with locations of several known tomato R genes or quantitative resistance loci (QRLs), including Cf-1, Cf-4, Cf-9, Cf-ECP2, rx-1, and Cm1.1 (chromosome 1); Tm-1 (chromosome 2); Asc (chrromosme 3); Pto, Fen, and Prf (chromosome 5); OI-1, Mi, Ty-1, Cm6.1, Cf-2, CF-5, Bw-5, and Bw-1 (chromosome 6); I-1, I-3, and Ph-1 (chromosome 7); Tm-2a and Fr1 (chromosome 9); and Lv (chromosome 12). These co-localizations indicate that the RGA loci were either linked to or part of the known R genes. Furthermore, similar to that for many R gene families, several RGA loci were found in clusters, suggesting their potential evolutionary relationship with R genes. Comparisons of the present map with other molecular linkage maps of tomato, including the high density L. esculentum × Lycopersicon pennellii map, indicated that the lengths of the maps and linear order of RFLP markers were in good agreement, though certain chromosomal regions were less consistent than others in terms of the frequency of recombination. The present map provides a basis for identification and mapping of genes and QTLs for disease resistance and other desirable traits in PI126445 and other L. hirsutum accessions, and will be useful for marker-assisted selection and map-based gene cloning in tomato.Key words: disease resistance, genetic marker, molecular map, quantitative trait locus (QTL), restriction fragment length polymorphism (RFLP), RGAs.

scholarly journals Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Qimei Wang ◽  
Feng Qi ◽  
Minghe Sun ◽  
Jianhua Qu ◽  
Jie Xue
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Detection Methods ◽  
Disease Detection ◽  
Deep Convolutional Neural Networks ◽  
Detection Techniques ◽  
Tomato Diseases ◽  
Validation Set ◽  
Tomato Disease

This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.

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