Disentanglement of the secrets of aluminium in acidophilic tea plant (Camellia sinensis L.) influenced by organic and inorganic amendments

Bora, Krishnamoni; Sarkar, Dibyendu; Konwar, Kaberijyoti; Payeng, Bobita; Sood, Kaushal; Paul, Ranjit Kumar; Datta, Rupali; Das, Sampa; Khare, Puja; Karak, Tanmoy

doi:10.1016/j.foodres.2018.11.049

Cited by 18 publications

(3 citation statements)

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“…The global consumption of tea is increasing over the years which stood at 5.53 million tonnes in 2016 ( Food and Agriculture Organization of the United Nations, 2018 ). The acidic property of tea-growing soil makes the trace elements readily available for the plant to uptake ( Karak and Bhagat, 2010 , Bora et al, 2019 ). Recent literatures documented that the manufacturing processes of different types of tea from the young shoots of tea plant may contribute to the contents of a trace elements build up in made tea ( Brzezicha-Cirocka et al, 2016 , Szymczycha-Madeja et al, 2012 , Zhang et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Impact of processing method on selected trace elements content of green tea: Does CTC green tea infusion possess risk towards human health?

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Impact of processing method on selected trace elements content of green tea: Does CTC green tea infusion possess risk towards human health?

et al. 2021

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“…One of the products studied previously to be effective against obesity are green tea leaves derived from Camellia Sinensis has shown to have It has polyphenols (catechins) as main bioactive molecules. These bioactive molecules increase energy consumption, triggering the oxidation, and sympathetic nervous system of fat metabolism, and for the alternative pathway, results in high production of enzymes responsible for hepatic fat oxidation, appetite prevention, and reduced nutrient absorption [27].…”

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confidence: 99%

Role of Complementary and Alternative Medicine in Treatment and Prevention of Obesity; Molecular Strategies to Recommended Acceptance

Baig¹

2019

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“…Tea is an important economic crop whose demand is increasing worldwide ( Xia et al, 2017 ; Bora et al, 2019 ; Fang et al, 2019 ). However, the tea production is affected by diseases and pest infestations, which poses a severe threat to the yield and quality of tea leaves.…”

Section: Introductionmentioning

confidence: 99%

The Stress Detection and Segmentation Strategy in Tea Plant at Canopy Level

et al. 2022

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As compared with the traditional visual discrimination methods, deep learning and image processing methods have the ability to detect plants efficiently and non-invasively. This is of great significance in the diagnosis and breeding of plant disease resistance phenotypes. Currently, the studies on plant diseases and pest stresses mainly focus on a leaf scale. There are only a few works regarding the stress detection at a complex canopy scale. In this work, three tea plant stresses with similar symptoms that cause a severe threat to the yield and quality of tea gardens, including the tea green leafhopper [Empoasca (Matsumurasca) onukii Matsuda], anthracnose (Gloeosporium theae-sinensis Miyake), and sunburn (disease-like stress), are evaluated. In this work, a stress detection and segmentation method by fusing deep learning and image processing techniques at a canopy scale is proposed. First, a specified Faster RCNN algorithm is proposed for stress detection of tea plants at a canopy scale. After obtaining the stress detection boxes, a new feature, i.e., RGReLU, is proposed for the segmentation of tea plant stress scabs. Finally, the detection model at the canopy scale is transferred to a field scale by using unmanned aerial vehicle (UAV) images. The results show that the proposed method effectively achieves canopy-scale stress adaptive segmentation and outputs the scab type and corresponding damage ratio. The mean average precision (mAP) of the object detection reaches 76.07%, and the overall accuracy of the scab segmentation reaches 88.85%. In addition, the results also show that the proposed method has a strong generalization ability, and the model can be migrated and deployed to UAV scenarios. By fusing deep learning and image processing technology, the fine and quantitative results of canopy-scale stress monitoring can provide support for a wide range of scouting of tea garden.

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Disentanglement of the secrets of aluminium in acidophilic tea plant (Camellia sinensis L.) influenced by organic and inorganic amendments

Cited by 18 publications

References 61 publications

Impact of processing method on selected trace elements content of green tea: Does CTC green tea infusion possess risk towards human health?

Impact of processing method on selected trace elements content of green tea: Does CTC green tea infusion possess risk towards human health?

Role of Complementary and Alternative Medicine in Treatment and Prevention of Obesity; Molecular Strategies to Recommended Acceptance

The Stress Detection and Segmentation Strategy in Tea Plant at Canopy Level

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