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Figure 6 in Hydrogen peroxide is involved in drought stress long-distance signaling controlling early stomatal closure in tomato plants
Figure 6. Water relations analysis of tomato BS II0020 cultivated under irrigated or drought conditions. (a) relative water content; (b) leaf temperature; (c) total transpiration of plants throughout the evaluation period; (d) average daily transpiration; (e) transpiration per cm2 of leaf area; (f) water use efficiency (shoot dry mass/total transpiration). Control plants received full irrigation throughout the experiment. The values are the means of each treatment (n= 4), followed by the standard error. The letters over the bars represent the differences in the means between biochemical treatments within each condition, and the asterisks the differences of the same biochemical treatment between the conditions, calculated by Scott-knott test at 5% probability.
Figure 11. A in Morphological and molecular identification of Cladosporium sphaerospermum isolates collected from tomato plant residues
Figure 11. A phylogenetic tree constructed using the neighbor-joining method depending on the comparison of the obtained nucleotide sequence of C. sphaerospermum isolate 10 as indicated by red dote (●) with those of C. sphaerospermum isolates deposited in NCBI.
Figure 9. A phylogenetic tree shows the genetic relationship between the C. sphaerospermum isolate 9 in Morphological and molecular identification of Cladosporium sphaerospermum isolates collected from tomato plant residues
Figure 9. A phylogenetic tree shows the genetic relationship between the C. sphaerospermum isolate 9 investigated in this study as indicated by red dote (●), and the C. sphaerospermum isolates available in NCBI.
Figure 7 in Morphological and molecular identification of Cladosporium sphaerospermum isolates collected from tomato plant residues
Figure 7. The Neighbor-Joining tree analysis shows the genetic relationship of the C. sphaerospermum isolate 6, investigated in this study, and the other isolates already recorded in NCBI.
Figure 6 in Morphological and molecular identification of Cladosporium sphaerospermum isolates collected from tomato plant residues
Figure 6. Nucleotide sequence alignment of the ITS region of the C. sphaerospermum isolate 6 identified in the current study and the other isolates already recorded in NCBI.
Figure 4 in Morphological and molecular identification of Cladosporium sphaerospermum isolates collected from tomato plant residues
Figure 4. The similarity and difference of nucleotide sequences in the C. sphaerospermum isolate 2, identified in this study, with those C. sphaerospermum isolates previously registered in NCBI. Identical nucleotides are given in dots. Table 2. Similarity rates among the C. sphaerospermum isolates identified in this study.
Fig. 6 in Effect of Bombus terrestris L. (Hymenoptera, Apidae) pollinating on flowering and fruiting trends of greenhouse tomato (Lycopersicon esculentum)
Fig. 6: Compare external diameter in greenhouse tomato bushes with and without bumblebees pollination treatments.
Fig. 2 in Effect of Bombus terrestris L. (Hymenoptera, Apidae) pollinating on flowering and fruiting trends of greenhouse tomato (Lycopersicon esculentum)
Fig. 2: Establishment of colonies in the middle of net (A), a box of Bombus terrestris colony bought from Koppert Co., representing in Turkey (B).
Figure 4 from: McDonnell AJ, Wetreich HB, Cantley JT, Jobson P, Martine CT (2019) Solanum plastisexum, an enigmatic new bush tomato from the Australian Monsoon Tropics exhibiting breeding system fluidity. PhytoKeys 124: 39-55. https://doi.org/10.3897/phytokeys.124.33526
Figure 4 Map showing geographic distribution of all taxa compared in this study. red points = S.jobsonii, blue points = S.diversiflorum, black points = S.eburneum, pink points = S.watneyi, yellow points = S.succosum and purple asterisk = S.plastisexum. All points are based on specimens databased in the Australasian Virtual Herbarium (https://avh.chah.org.au/) and specimens held at BUPL.
Figure 1 from: McDonnell AJ, Wetreich HB, Cantley JT, Jobson P, Martine CT (2019) Solanum plastisexum, an enigmatic new bush tomato from the Australian Monsoon Tropics exhibiting breeding system fluidity. PhytoKeys 124: 39-55. https://doi.org/10.3897/phytokeys.124.33526
Figure 1 Morphology and the earliest-known herbarium specimen of Solanumplastisexum. A Flowering stem with a single staminate flower in 2016 B Mature fruit C Erect inflorescences bearing staminate flowers in 2018 and D Specimen collected by P. Latz in 1974, held at DNA and annotated by D. Symon with an annotation indicating his confusion about the reproductive morphology of the specimen (male rachis visible above fruit on far left).
Figure 3 from: McDonnell AJ, Wetreich HB, Cantley JT, Jobson P, Martine CT (2019) Solanum plastisexum, an enigmatic new bush tomato from the Australian Monsoon Tropics exhibiting breeding system fluidity. PhytoKeys 124: 39-55. https://doi.org/10.3897/phytokeys.124.33526
Figure 3 Principal components analysis score plot with eigenvalues and the contribution of each PC displayed (left) and loading plot (right) of characters and species in Table 1. Left, red triangles = S.jobsonii, blue circles = S.diversiflorum, black crosses = S.eburneum, pink triangles = S.watneyi, yellow squares = S.succosum and purple asterisks = S.plastisexum. Right, weighted characters labelled and indicated with red arrows. A Seed length B Fruit width C Number of seeds per fruit D Fruit length E Depth of lobing on apical leaves F Depth of lobing on basal leaves G Fruit wall width H Width of basal leaves I Surface area of basal leaves J Calyx lobe length, hermaphrodite flowers K Internode length L Width of apical leaves, M Surface area of apical leaves N Petiole length O Pedicel length in fruit P Corolla diameter, hermaphrodite flowers Q Length of basal leaves R Corolla diameter, male flowers S Length of apical leaves T Plant height U Stem prickle length V Trichome density, abaxial surface of apical leaves W Trichome density, adaxial surface of apical leaves X Calyx lobe length, male flowers. Colours associated with each taxon also used in Figs 2, 3.
Figure 2 from: McDonnell AJ, Wetreich HB, Cantley JT, Jobson P, Martine CT (2019) Solanum plastisexum, an enigmatic new bush tomato from the Australian Monsoon Tropics exhibiting breeding system fluidity. PhytoKeys 124: 39-55. https://doi.org/10.3897/phytokeys.124.33526
Figure 2 Closely related species of andromonoecious bush tomatoes included in this study. ASolanumjobsoniiBS.watneyiCS.succosumDS.plastisexumES.diversiflorum and FS.eburneum. Colours associated with each taxon also used in Figs 3, 4.
Fig. 3 in Molecular cloning of the trypsin inhibitor from the skin secretion of the Madagascan Tomato Frog, Dyscophus guineti (Microhylidae), and insights into its potential defensive role
Fig. 3 MALDI-TOF mass spectrum of the protease inhibitor-containing fraction
Fig. 1 in High-level phylogeographic structuring of Neoleucinodes elegantalis Guenée (Lepidoptera, Crambridae) in Brazil: an important tomato pest
Fig. 1. Distribution patterns of haplotypes for N. elegantalis. (A) Frequency of haplotypes for the cytochrome c oxidase subunit 1 (CO1) region by study sites and the haplotype network obtained by median-joining. The study sites are indicated by letters, and the size of the graphics is proportional to the sample size. The colors correspond to the haplotypes as described by the legend: Garanhuns (GA), Petrolina (PT), Coimbra (CO), Camocim (CM), Encruzilhada de São João (ES), São José do R. Pardo (SP), Bezerros (BE) and São João (SJ). (B) Phylogenetic analysis for the haplotypes using the Bayesian approach. This analysis shows the time of divergence between the haplotypes in millions of years (Ma) (95% confidences are indicated by bold horizontal bars) and the groups defined by Spatial Analysis of Molecular Variance (SAMOVA). The support values are estimated with posterior probabilities BY (here in percentages).
Fig. 2 in High-level phylogeographic structuring of Neoleucinodes elegantalis Guenée (Lepidoptera, Crambridae) in Brazil: an important tomato pest
Fig. 2. The cluster analysis was built using the "Bayesian approach to phylogeographic clustering." The colors represent the clusters, and the background or shaded colors indicate uncertainty about the respective clusters. The points mark the sampling sites.
Fig. 3 in Acaricide efficacy and resistance in South Carolina tomato populations of twospotted spider mite
Fig. 3. Median plot damage ratings in a 2016 acaricide efficacy trial conducted in South Carolina, USA. The dashed line indicates the overall median across treatments. Plots were rated on a 1 to 10 scale, with "1" indicating no damage and "10" indicating complete leaf necrosis.
Tomato-leaf-disease-detection
Open the record for dataset details and reuse information.
Temperature differentially influences the capacity of Trichoderma species to induce plant defense responses in tomato against insect pests
<p>Species of the ecological opportunistic, avirulent fungus, <em>Trichoderma</em> are widely used in agriculture for their ability to protect crops from the attack of pathogenic fungi and for plant growth promotion activity. Recently, it has been shown that they may also have complementary properties that enhance plant defense barriers against insects. However, the use of these fungi is somewhat undermined by their variable level of biocontrol activity, which is influenced by environmental conditions. Understanding the source of this variability is essential for its profitable and wide use in plant protection. Here, we focus on the impact of temperature on <em>Trichoderma afroharzianum</em> T22, <em>Trichoderma atroviride</em> P1, and the defense response induced in tomato by insects. The <em>in vitro</em> development of these two strains was differentially influenced by temperature, and the observed pattern was consistent with temperature-dependent levels of resistance induced by them in tomato plants against the aphid, <em>Macrosiphum euphorbiae</em>, and the noctuid moth, <em>Spodoptera littoralis</em>. Tomato plants treated with <em>T. afroharzianum</em> T22 exhibited enhanced resistance toward both insect pests at 25°C, while <em>T. atroviride</em> P1 proved to be more effective at 20°C. The comparison of plant transcriptomic profiles generated by the two <em>Trichoderma</em> species allowed the identification of specific defense genes involved in the observed response, and a selected group was used to assess, by real-time quantitative reverse transcription PCR (qRT-PCR), the differential gene expression in <em>Trichoderma</em>-treated tomato plants subjected to the two temperature regimens that significantly affected fungal biological performance. These results will help pave the way toward a rational selection of the most suitable <em>Trichoderma</em> isolates for field applications, in order to best face the challenges imposed by local environmental conditions and by extreme climatic shifts due to global warming.</p>
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
<p>This is an open dataset.</p>
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
<p>In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as 'Cascaded Object Detector' (COD) and the other is a composition of traditional customised methods, individually known as 'Colour Transformation': 'Colour Segmentation' and 'Circular Hough Transformation'. The (Viola-Jones) COD generates 'histogram of oriented gradient' (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of <i>O</i>(<i>n</i><sup>3</sup>) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.</p>
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