Artificial neural networks and genetic dissimilarity among saladette type dwarf tomato plant populations

Oliveira, Camila Soares de; Maciel, Gabriel Mascarenhas; Siquieroli, Ana Carolina Silva; Gomes, Danilo; Diniz, Nádia Mendes; Luz, José Magno Queiróz; Yada, Rickey Y.

doi:10.1016/j.fochms.2021.100056

Cited by 6 publications

(4 citation statements)

References 45 publications

(65 reference statements)

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“…Finzi et al (2020) identified some tomato populations with the highest divergence and the most promising for development of inbred lines with improved fruit traits among the 12 BC1F2 populations of dwarf round tomatoes. Genetic dissimilarity was also reported in BC1F3 populations of saladette tomato (De Oliveira et al 2021).…”

Section: Comparative Analysis Of Variation Among the Selfed And Backc...supporting

confidence: 54%

Selfing revealed positive values than backcrossing for yield and yield enhancing traits among tomato segregating populations generated from Solanum lycopersicum × S. pimpinellifolium crosses under tropical humid climate

Ene

Abtew

Oselebe

et al. 2023

Preprint

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The objectives of this study were to assess phenotypic variability among F3 and BC1F2 tomato populations, and apply genotype by yield*trait (GYT) biplots for population and line selection based on multiple traits. Four diverse cultivated parents (‘CLN2498D’ and ‘CLN2417H’ from Ethiopia; ‘UC Dan INDIA’ and ‘Tima’ from Nigeria), and wild parent ‘LA2093’ were used to generate 276 potential breeding lines. The lines were categorized into eight populations (‘Pop_1_W/H1’, ‘Pop_2_W/H2’, ‘Pop_3_W/D1’, ‘Pop_4_W/D2’, ‘Pop_5_W/T1’, ‘Pop_6_W/T2’, ‘Pop_7_W/U1’, and ‘Pop_8_W/U2’), and evaluated twice in the field using 19 × 15 alpha-lattice design with two replicates. Significant differences were observed among lines and populations for all yield enhancing traits. ‘Pop_1_W/H1’, ‘pop_4_W/D2’ and ‘pop_6_W/T2’ expressed the highest genetic divergence for plant height, number of leaves, total flower and fruit number, and fruit weight. GYT biplots revealed that all yield*trait interactions had a positive correlation with each other. F3 populations, ‘Pop_5_W/T1’ and ‘pop_1_W/H1’ exhibited the best performance for majority of the yield*trait combinations. Hierarchical clustering on principal components (HCPC) revealed overlapping lines (70.58% of Cluster D lines) and (54.05% of Cluster U lines) from the two F3 populations. In BC1F2 population, 32.35% of the 34 original lines of Cluster D and 48.48% of Cluster T lines overlapped between Clusters D and T, while 18.18% of Cluster T lines and 8.82% of Cluster H lines were transgressive between Clusters T and H. Transgressive segregants ‘0210U1’, ‘0211U1’, and ‘0171T1’ of selfed population using multivariate analysis were believed to represent potential sources of novel genetic variation for future tomato breeding.

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Section: Comparative Analysis Of Variation Among the Selfed And Backc...supporting

confidence: 54%

Selfing revealed positive values than backcrossing for yield and yield enhancing traits among tomato segregating populations generated from Solanum lycopersicum × S. pimpinellifolium crosses under tropical humid climate

Ene

Abtew

Oselebe

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Internode length plays an important role for the improvement of plant architecture in tomato plants [21]. In studies with tomato culture, the distance between the tomato internodes is a parameter that can indicate the potential culture productivity, since it is directly related to yield [22,23].…”

Section: Discussionmentioning

confidence: 99%

Productive and Physico-Chemical Parameters of Tomato Fruits Submitted to Fertigation Doses with Water Treated with Very Low-Frequency Electromagnetic Resonance Fields

Putti

Nogueira

Souza

et al. 2022

Plants

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It is known that poorly performed fertigation directly impacts on tomato production and biometric components. In addition, consumers are also affected by interrelated characteristics that interfere with the acceptability of the fruit, such as the physicochemical parameters and nutrients in the fruit. Thus, eco-friendly technologies, such as irrigation with ultra-low frequency electromagnetic treated-water, which attenuates the inadequate management of fertigation, are essential to improve marketable fruit yields. Thus, the objective of the present work was to investigate the impact of treated water with very low-frequency electromagnetic resonance fields in physical, chemical and nutritional parameters at different nutrient solution strengths in tomato fruits. In this study, experiments were carried out in randomized blocks and five doses of fertigation were used (1.5; 2.5; 4.0; 5.5; and 7.0 dS m−1), employing two types of water: electromagnetically treated and untreated. It can be seen that the fertigation affected some parameters, mainly the number of fruits with blossom-end rot, fruit size, and weight. Variance analysis (ANOVA) was performed with the subsequent use of the Tukey test. In all statistical tests, a confidence level of 95% was considered. The soluble solids content increased by 28% as a function of the fertigation doses. The electromagnetically treated water reduced the number of fruits with blossom-end rot by 35% (p < 0.05). Overall, electromagnetic water improved the physicochemical quality parameters and the nutritional status of tomato fruits. Thus, this study demonstrated that green technology could leverage tomato fruit production and quality.

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“…Deep Learning has also been used to detect pests on strawberry plants. In experiments showing multivariate nonlinear models achieving an identification accuracy rate of more than 94% [52], [53]. Deep Learning is also used to classify tomato plant pests with digital images as input.…”

Section: The Use Of Artificial Neural Network In Agricultural Plant P...mentioning

confidence: 99%

The Use of Artificial Neural Networks in Agricultural Plants

Susanti

Nofendra

Zaini

et al. 2023

AJEEET

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Artificial Neural Networks use high-performance computing and big data technology, opportunities for science to create new opportunities in agriculture. The purpose of writing this article is to analyze the use of artificial neural networks on (a) plant diseases based on plant leaf diseases, (b) plant pests, (c) growth or quality, and (d) agricultural products. The writing method used is a literature study of the research that has been done. The keywords used in the search for references include ANN, plant, diseases, pests, growth or quality, and agricultural products. Publishers for the reference in this article are ScienceDirect and IEEE. The years of publication of the references are restricted from 2015 to 2022. Based on the literature study results, it was concluded that Artificial Neural Networks' deep learning models are accurate for detecting and classifying leaf diseases and pests, detecting growth, and application to agricultural plant products.

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Artificial neural networks and genetic dissimilarity among saladette type dwarf tomato plant populations

Cited by 6 publications

References 45 publications

Selfing revealed positive values than backcrossing for yield and yield enhancing traits among tomato segregating populations generated from Solanum lycopersicum × S. pimpinellifolium crosses under tropical humid climate

Selfing revealed positive values than backcrossing for yield and yield enhancing traits among tomato segregating populations generated from Solanum lycopersicum × S. pimpinellifolium crosses under tropical humid climate

Productive and Physico-Chemical Parameters of Tomato Fruits Submitted to Fertigation Doses with Water Treated with Very Low-Frequency Electromagnetic Resonance Fields

The Use of Artificial Neural Networks in Agricultural Plants

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