Combination of Biochemical, Molecular, and Synchrotron-Radiation-Based Techniques to Study the Effects of Silicon in Tomato (Solanum Lycopersicum L.)

Marmiroli, Marta; Mussi, Francesca; Gallo, Valentina; Gianoncelli, Alessandra; Hartley, W. G.; Marmiroli, Nelson

doi:10.3390/ijms232415837

Cited by 5 publications

(8 citation statements)

References 120 publications

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“…A typical application field of STXM coupled with XRF is environmental science and specifically plant biology, where it may be important to determine the distribution of a specific chemical element in leaves, flowers and/or roots. Such element may be of interest to understand plant optical properties [19], or for a better understanding of plant uptake from the soil [20] or may cause toxicity and damage to the plants itselves [21][22][23] or even for phytoremediation purposes, where plants are used to reclaim contaminated lands, as they can absorb toxic compounds from the soil and transform them in non dangerous ones [24,25]. Such plant tissues, that is leaves, roots, flowers or roots sections, have usually irregular shapes and are highly porous, thus rectangular or square scans acquire signals also on empty or on non-interesting areas.…”

Section: Absorption Threshold Methodsmentioning

confidence: 99%

Advances on sparse Dynamic Scanning in Spectromicroscopy through Compressive Sensing

Kourousias

Billè

Ippoliti

et al. 2023

Preprint

Self Cite

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Scanning microscopies and spectroscopies like X-ray Fluorescence (XRF), Scanning Transmission X-ray Microscopy (STXM), and Ptychography are of very high scientific importance as they can be employed in several research fields. Methodology and technology advances aim at analysing larger samples at better resolution, improved sensitivities and higher acquisition speeds. The frontiers of those advances are on detectors, radiation sources, motors, but also on acquisition and analysis software together with general methodology improvements. We've e recently introduced and fully implemented on a soft X-ray microscopy beamline an intelligent scanning methodology based on compressive sensing. This demonstrated sparse low energy XRF scanning of dynamically chosen regions of interest in combination with STXM. It resulted in a sparse megapixel-rage low energy XRF spectroimaging through dynamic scanning that was previously not feasible. This research has been further developed and has been applied in scientific applications in biology. The developments are mostly on the dynamic triggering decisional mechanism in order to incorporate modern Machine Learning and real-time XRF fitting but also on the suitable integration of the method in the control system making it available for other beamlines and imaging techniques. On the applications front, the method was successfully used on different samples, from lung and ovarian human tissues to plant root sections. This manuscript introduces the latest advances of the method and demonstrates applications in life and environmental sciences. Finally it highlights an auxiliary development of a mobile application that assists the selection of specific regions of interest in an easy way.

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Section: Absorption Threshold Methodsmentioning

confidence: 99%

Advances on sparse Dynamic Scanning in Spectromicroscopy through Compressive Sensing

Kourousias

Billè

Ippoliti

et al. 2023

Preprint

Self Cite

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“…The diversity in Si concentration in leaves and shoots is caused by different plants' Si absorption and passing mechanisms (Bhardwaj & Kapoor, 2021). Higher plant Si adsorption is characterized as active uptake (Si uptake > water uptake), passive uptake (Si uptake = water uptake), and rejective uptake (Si uptake water uptake) based on water uptake capacity (Kaur & Greger, 2019) (Marmiroli et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Edaphic silicon nutrition of tomato biostimulates their growth, yield and antioxidant composition under greenhouse conditions

Reyes-Pérez,

Murillo-Noboa,

Murillo-Amador

et al. 2024

Hortic. Bras.

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Silicon (Si) has multiple benefits in crops. Most of the studies on Si have been carried out by applying some type of stress. It has even been suggested that the positive response of Si is determined by the degree of stress in the plant, and there is little information on Si and its effect on the plant when there is no induced stress factor. The objective of the study was to determine the effect of edaphic Si on the growth, production and concentration of antioxidants in tomato under greenhouse conditions without induced stress. The treatments were three doses of Si (0.06, 0.12 and 0.18 g/plant) and a control (0.0 g/plant). The treatments were distributed in a completely randomized design with four repetitions. The addition of Si in tomato plants increased biomass production, the number of fruits and yield. In addition, in the treatments with the highest dose of Si, the concentration of antioxidants increased, as well as the total antioxidant capacity. It is suggested to include Si in tomato fertilization programs as a sustainable alternative to improve crop growth and productivity.

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“…The tomato (Solanum lycopersicum L.) is characterized by high economic and nutritional values. It is widely cultivated and consumed, and its popularity is increasing [1,2]. The tomato is a well-known crop, and its production can depend on cultivars.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Sabancı

2023

Sustainability

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In the present study, a deep learning-based two-scenario method is proposed to distinguish tomato seed cultivars. First, images of seeds of four different tomato cultivars (Sacher F1, Green Zebra, Pineapple, and Ozarowski) were taken. Each seed was then cropped on the raw image and saved as a new image. The number of images in the dataset was increased using data augmentation techniques. In the first scenario, these seed images were classified with four different CNN (convolutional neural network) models (ResNet18, ResNet50, GoogleNet, and MobileNetv2). The highest classification accuracy of 93.44% was obtained with the MobileNetv2 model. In the second scenario, 1280 deep features obtained from MobileNetv2 fed the inputs of the Bidirectional Long Short-Term Memory (BiLSTM) network. In the classification made using the BiLSTM network, 96.09% accuracy was obtained. The results show that different tomato seed cultivars can be distinguished quickly and accurately by the proposed deep learning-based method. The performed study is a great novelty in distinguishing seed cultivars and the developed innovative approach involving deep learning in tomato seed image analysis, and can be used as a comprehensive procedure for practical tomato seed classification.

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Combination of Biochemical, Molecular, and Synchrotron-Radiation-Based Techniques to Study the Effects of Silicon in Tomato (Solanum Lycopersicum L.)

Cited by 5 publications

References 120 publications

Advances on sparse Dynamic Scanning in Spectromicroscopy through Compressive Sensing

Advances on sparse Dynamic Scanning in Spectromicroscopy through Compressive Sensing

Edaphic silicon nutrition of tomato biostimulates their growth, yield and antioxidant composition under greenhouse conditions

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

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