Chemical induced mutations for development of resistance in banana cv. Nanjanagudu Rasabale

Kishor, H.; Prabhuling, G.; Ambika, D. S.; Abhijith, Y.C.

doi:10.5958/2454-1761.2017.00023.7

Cited by 7 publications

(3 citation statements)

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“…It is one of the most commonly used classification algorithms with a relatively high degree of accuracy, simplicity of implementation, flexibility and high generalization performance [15]. One of the advantages of the AdaBoost is that it uses the functionalities of each class separately when using all the features may not be needed at this time, it adjusts the errors of weak classifiers, achieving high accuracy with much less tuning of other strong classifiers [16].…”

Section: Deep Learning Architecturesmentioning

confidence: 99%

Towards supervised real-time human activity recognition on embedded equipment

Najeh

Lohr

Leduc³

2022

2022 IEEE International Workshop on Metrology for Living Environment (MetroLivEn)

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In recent years, real-time human activity recognition (HAR) has reached importance due to its applications in various domains such as assistive services for the elderly in smart buildings, monitoring, well-being, comfort and security. Various techniques, researched within the image processing and computer vision communities, have been established to recognize human activities in real-time, but all of them are based on wearable sensors and there is no much attention for ambient sensor based approaches. In the literature, deep learning (DL) is one of effective and cost-efficient supervised learning model and different architectures haves been investigated for real-time HAR. However, it still struggles with the quality of data as well as hardware implementation issues.This paper presents two contributions. Firstly, an intensive analysis of DL architectures and its characteristics along with their limitations in the framework of real time HAR are investigated. Secondly, existing hardware architectures and related challenges in this field are highlighted (adaptation of DL architectures towards microcontrollers, difficulty to provide a smart home with numerous sensors and trends regarding cloud-bases approaches). Then, new research directions and solutions around the real-time data quality assessment, the study of main performance factors for DL on microcontrollers, the concept of minimal sensors set up for the employment of IoT devices and the distributed intelligence are suggested to solve them respectively and to improve this field.

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Section: Deep Learning Architecturesmentioning

confidence: 99%

Towards supervised real-time human activity recognition on embedded equipment

Najeh

Lohr

Leduc³

2022

2022 IEEE International Workshop on Metrology for Living Environment (MetroLivEn)

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“…On the other hand, bananas being climacteric fruits can be harvested before ripening but immature banana fruits do not ripe properly and uniformly which reduces the marketability of fruits. Exogenous application of ethylene and ethylene-releasing compounds stimulates biosynthesis of ethylene, chlorophyll destruction, and yellowing, respiration, and senescence in climacteric fruit (Nair & Singh, 2003), and hence accelerate ripening, improves color and palatability with reduced spoilage of banana Kishor et al, 2017). Ethylene and ethylene-releasing compounds are commonly used in commercial banana ripening in developed countries (Islam et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Effect of different post-harvest practices on the quality of bananas (G9)

Tamang,

Bhujel,

Yadav

et al. 2024

Preprint

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Artificial fruit ripening agents have become increasingly popular in recent years. During the off-season, various ripening agents are used for the ripening of banana fruits. This experiment was carried out under the study entitled “Effect of different post-harvest practices on quality of banana (G9) in Chitwan” for controlling the ripening of the banana, for improvement of quality attributes and post-harvest life of banana. The experiment was conducted at the laboratory of Horticulture, Agriculture and Forestry University, from 14th June to 16th August 2022. These experiments were laid out in a Completely Randomized Design in which the experiment comprised of five treatments dipping in distilled water, hot water treatment at 55℃ then covered with a jute sack, perforated plastic, ethephon 600 ppm, and, ripening chamber with four replications. Different post-harvest parameters were recorded at the interval of three days. In this experiment, the maximum physiological loss in weight, TSS, the highest color score, pH, TSS/TA, and Pulp peel ratio were observed in bananas sprayed with distilled water. In contrast, the minimum values for TSS, pulp peel ratio, Peel color rating, and TSS/TA were observed in Jute sack-covered fruits. Maximum and minimum shelf life were observed in jute sack-covered fruits and bananas sprayed with ethephon @600 ppm respectively. In summary, the present study showed some innovative post-harvest practices that enhance food security, promote responsible consumption, reduce environmental impact, and align with multiple SDGs, ultimately fostering a more sustainable and food-secure future.

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“…Various methods have been implemented to increase NR productivity. Among them, micropropagation (Babu, 2019) and chemical-induced mutations (Kishor et al, 2017) have been attempted.…”

Section: Introductionmentioning

confidence: 99%

Application of Pranic Agriculture to Improve Growth and Yield of Banana (Musa sp. var. Nanjangud Rasa Bale)-A Comparative Field Trial

Prasad,

Vinu,

Jois

2023

ASD

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Background: The projection of prana to soil, seeds and seedlings to enhance plant growth is referred to as Pranic Agriculture (PA). Methods: A field study was carried out to study the influence of PA on Banana var. (Musa sps. var. Nanjangud rasabale) growth for a duration of one year. Banana suckers and agricultural land were divided into two groups namely pranic and control in half acre. The land and suckers were treated with PA and were referred to as the pranic group, while non-pranic were referred to as the control group. Growth parameters (shoot length, shoot diameter, number of leaves, leaf length and leaf diameter) were evaluated on the 2nd, 4th, 6th, 8th, 10th and 12th months and statistically analyzed using repeated measure ANOVA. Fruit morphology and days to flowering were analyzed using a t-test. Result: For pranic group, shoot diameter (F = 9.407, p = 0.001), leaf length (F = 7.082, p = 0.003) and leaf diameter (F = 11.459, p = .000), flowering days (t = 3.39, P=.002), fruit characteristics like fruit bunch weight (t = 8.81, p = .000), total number of fruits in bunch (t = 9.20, p = .000), fruits weight (t = 7.48, p = .000), fruit length (t = 7.94, p = .000) enhanced considerably and the results were all significant when compared to control. Further studies are needed to find out the mechanism for the improved growth in the pranic group. This study will help the farmers to get higher yields and more income by using pranic agriculture.

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Chemical induced mutations for development of resistance in banana cv. Nanjanagudu Rasabale

Cited by 7 publications

References 0 publications

Towards supervised real-time human activity recognition on embedded equipment

Towards supervised real-time human activity recognition on embedded equipment

Effect of different post-harvest practices on the quality of bananas (G9)

Application of Pranic Agriculture to Improve Growth and Yield of Banana (Musa sp. var. Nanjangud Rasa Bale)-A Comparative Field Trial

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