Review article on <i>Luffa acutangula</i> (L) Roxb

Yasmin, Atika; Bharathi, R. Vijaya; Radha, R

doi:10.5958/0974-360x.2019.00429.3

Cited by 2 publications

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“…Early harvesting can prevent full growth, reducing commercial value, while delayed harvesting can lead to a marked decline in fruit quality. Therefore, accurately determining loofah maturity is critical to optimize harvest timing and ensure the vegetable's quality, texture, and edible value [1]. Yet, currently, maturity assessments predominantly rely on subjective time-consuming manual methods, which fall short of modern agricultural production and market demands.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Detection and Maturity Classification Method for Loofah

et al. 2023

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Fruit maturity is a crucial index for determining the optimal harvesting period of open-field loofah. Given the plant’s continuous flowering and fruiting patterns, fruits often reach maturity at different times, making precise maturity detection essential for high-quality and high-yield loofah production. Despite its importance, little research has been conducted in China on open-field young fruits and vegetables and a dearth of standards and techniques for accurate and non-destructive monitoring of loofah fruit maturity exists. This study introduces a real-time detection and maturity classification method for loofah, comprising two components: LuffaInst, a one-stage instance segmentation model, and a machine learning-based maturity classification model. LuffaInst employs a lightweight EdgeNeXt as the backbone and an enhanced pyramid attention-based feature pyramid network (PAFPN). To cater to the unique characteristics of elongated loofah fruits and the challenge of small target detection, we incorporated a novel attention module, the efficient strip attention module (ESA), which utilizes long and narrow convolutional kernels for strip pooling, a strategy more suitable for loofah fruit detection than traditional spatial pooling. Experimental results on the loofah dataset reveal that these improvements equip our LuffaInst with lower parameter weights and higher accuracy than other prevalent instance segmentation models. The mean average precision (mAP) on the loofah image dataset improved by at least 3.2% and the FPS increased by at least 10.13 f/s compared with Mask R-CNN, Mask Scoring R-CNN, YOLACT++, and SOLOv2, thereby satisfying the real-time detection requirement. Additionally, a random forest model, relying on color and texture features, was developed for three maturity classifications of loofah fruit instances (M1: fruit setting stage, M2: fruit enlargement stage, M3: fruit maturation stage). The application of a pruning strategy helped attain the random forest model with the highest accuracy (91.47% for M1, 90.13% for M2, and 92.96% for M3), culminating in an overall accuracy of 91.12%. This study offers promising results for loofah fruit maturity detection, providing technical support for the automated intelligent harvesting of loofah.

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Section: Introductionmentioning

confidence: 99%

A Real-Time Detection and Maturity Classification Method for Loofah

et al. 2023

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Promising Actions of Certain Medicinal Plants of Bangladesh against Diabetes and Oxidative Stress: A Review

Sarkar¹,

Khatun²,

Enam³

et al. 2022

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Diabetes is a metabolic illness defined by hyperglycemia that affects 10% of the world's population. Diabetic complications such as blindness, kidney failure, and heart failure can develop if left untreated and are made worse by oxidative stress. Oxidative stress contributes to the rise of diabetic complications, particularly type-2 diabetes. In the blood vessels of diabetic individuals, it causes endothelial dysfunction. Diabetes is one of the leading causes of death worldwide. Nowadays, it is alarming that the number of diabetic patients is increasing dramatically. There are plenty of anti-diabetic drugs available on the market, but they possess several adverse effects and do not completely cure diabetes. It has now become a financial burden on patients, their families, and society as well. Medicinal plants have gained popularity in developed and developing countries over the last two decades because of their vast natural sources and lack of harmful effects compared to modern allopathic medications. According to the World Health Organization, traditional medicines, which are mostly manufactured from plants, are still used by 80% of the population in developing countries for the management and curing of diseases. This review includes 81 Bangladeshi medicinal plants from 51 different families that can be used to treat diabetes and oxidative stress. Among them, Psidium guajava (L.

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Review article on Luffa acutangula (L) Roxb

Cited by 2 publications

References 0 publications

A Real-Time Detection and Maturity Classification Method for Loofah

A Real-Time Detection and Maturity Classification Method for Loofah

Promising Actions of Certain Medicinal Plants of Bangladesh against Diabetes and Oxidative Stress: A Review

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