Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Basak, Jayanta Kumar; Madhavi, Bolappa Gamage Kaushalya; Paudel, Bhola; Kim, Na Eun; Kim, Hyeon Tae

doi:10.3390/foods11142086

Cited by 38 publications

(26 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The maturity level of the strawberry was categorized based on the amount of redness on the strawberry. In the study, Basak et al [15] categorized the maturity level of strawberries according to different growth stages: whiting, turning red, three-quarter red, bright red, and dark red. In reference to that study, the ripeness level of strawberries was categorized as white-green to green as immature, turning red up to three-quarter red as transition, and above three-quarters red to dark red as mature.…”

Section: Plant Materials and Image Acquisitionmentioning

confidence: 99%

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Tamrakar,

Karki,

Kang

et al. 2024

AgriEngineering

Self Cite

View full text Add to dashboard Cite

A lightweight strawberry detection and localization algorithm plays a crucial role in enabling the harvesting robot to effectively harvest strawberries. The YOLO model has often been used in strawberry fruit detection for its high accuracy, speed, and robustness. However, some challenges exist, such as the requirement for large model sizes, high computation operation, and undesirable detection. Therefore, the lightweight improved YOLOv5s-CGhostnet was proposed to enhance strawberry detection. In this study, YOLOv5s underwent comprehensive model compression with Ghost modules GCBS and GC3, replacing modules CBS and C3 in the backbone and neck. Furthermore, the default GIOU bounding box regressor loss function was replaced by SIOU for improved localization. Similarly, CBAM attention modules were added before SPPF and between the up-sampling and down-sampling feature fusion FPN–PAN network in the neck section. The improved model exhibited higher mAP@0.5 of 91.7% with a significant decrement in model size by 85.09% and a reduction in GFLOPS by 88.5% compared to the baseline model of YOLOv5. The model demonstrated an increment in mean average precision, a decrement in model size, and reduced computation overhead compared to the standard lightweight YOLO models.

show abstract

Section: Plant Materials and Image Acquisitionmentioning

confidence: 99%

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Tamrakar,

Karki,

Kang

et al. 2024

AgriEngineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Basak et al. [ 21 ] developed a non-destructive method using ML algorithms to predict the total soluble solids (TSS) and pH of strawberries. Image processing techniques have also been used in several studies to assess food quality.…”

Section: Relevant Workmentioning

confidence: 99%

Enhancing Shelf Life Prediction of Fresh Pizza with Regression Models and Low Cost Sensors

Wunderlich

Pauli

Neumaier

et al. 2023

Foods

View full text Add to dashboard Cite

The waste of food presents a challenge for achieving a sustainable world. In Germany alone, over 10 million tonnes of food are discarded annually, with a worldwide total exceeding 1.3 billion tonnes. A significant contributor to this issue are consumers throwing away still edible food due to the expiration of its best-before date. Best-before dates currently include large safety margins, but more precise and cost effective prediction techniques are required. To address this challenge, research was conducted on low-cost sensors and machine learning techniques were developed to predict the spoilage of fresh pizza. The findings indicate that combining a gas sensor, such as volatile organic compounds or carbon dioxide, with a random forest or extreme gradient boosting regressor can accurately predict the day of spoilage. This provides a more accurate and cost-efficient alternative to current best-before date determination methods, reducing food waste, saving resources, and improving food safety by reducing the risk of consumers consuming spoiled food.

show abstract

“…The RGB, HSV, and HSL color-space channels were used as input variables to develop multiple linear regression (MLR) and support vector machine regression (SVM-R) models. The results demonstrated that an SVM-R model working on the characteristics in the HSV color space performed better than an MLR model for total soluble solids and pH prediction (accuracy of 84.1% and 79.2% for total soluble solids and 78.8% and 72.6% for pH in the training and testing stages, respectively) [ 174 ].…”

Section: Innovative Non-destructive Techniques For the Quality Monito...mentioning

confidence: 99%

Emerging Postharvest Technologies to Enhance the Shelf-Life of Fruit and Vegetables: An Overview

Palumbo

Attolico

Capozzi

et al. 2022

Foods

View full text Add to dashboard Cite

Quality losses in fresh produce throughout the postharvest phase are often due to the inappropriate use of preservation technologies. In the last few decades, besides the traditional approaches, advanced postharvest physical and chemical treatments (active packaging, dipping, vacuum impregnation, conventional heating, pulsed electric field, high hydrostatic pressure, and cold plasma) and biocontrol techniques have been implemented to preserve the nutritional value and safety of fresh produce. The application of these methodologies after harvesting is useful when addressing quality loss due to the long duration when transporting products to distant markets. Among the emerging technologies and contactless and non-destructive techniques for quality monitoring (image analysis, electronic noses, and near-infrared spectroscopy) present numerous advantages over the traditional, destructive methods. The present review paper has grouped original studies within the topic of advanced postharvest technologies, to preserve quality and reduce losses and waste in fresh produce. Moreover, the effectiveness and advantages of some contactless and non-destructive methodologies for monitoring the quality of fruit and vegetables will also be discussed and compared to the traditional methods.

show abstract

Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Cited by 38 publications

References 84 publications

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Lightweight Improved YOLOv5s-CGhostnet for Detection of Strawberry Maturity Levels and Counting

Enhancing Shelf Life Prediction of Fresh Pizza with Regression Models and Low Cost Sensors

Emerging Postharvest Technologies to Enhance the Shelf-Life of Fruit and Vegetables: An Overview

Contact Info

Product

Resources

About