Applying convolutional neural networks to assess the external quality of strawberries

Choi, Ji‐Young; Seo, Kwangwon; Cho, Jeong-Seok; Moon, Kwang-Deog

doi:10.1016/j.jfca.2021.104071

Cited by 16 publications

(10 citation statements)

References 27 publications

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“…Multiple linear regression (MLR) is often used to model some indicators, but this technique is not always effective in determining quality attributes due to the non-linearity of its variables. In recent years, machine learning, a field in artificial intelligence, has led to innovation in numerous fields involving the core technologies of algorithms and big data [29]. It has been applied to many areas including medicine, manufacturing, healthcare, etc., because it greatly improves productivity, quality, flexibility, safety, and cost [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

Amoriello¹,

Ciccoritti

Ferrante

2022

Agronomy

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Strawberry is a very popular fruit, appreciated for its unique flavor and many beneficial traits such as antioxidants and useful amino acids, which strongly contribute to the overall quality of the product. Indeed, the quality of fresh fruit is a fundamental aspect for consumers, and it is crucial for the success of breeding activities as well as for enhancing the competitiveness and profitability of the fruit industry. Nowadays, the entire supply chain requires simple and fast systems for quality evaluation. In this context, the pomological and chemical traits (i.e., soluble solids, firmness, titratable acidity, dry matter) as well as nutritional ones such as total phenols, total anthocyanins and antioxidant potential were evaluated and compared for seven strawberry cultivars and three harvest times. The prediction of the qualitative traits was carried out using color space coordinates (L*, a* and b*) and two statistical techniques, i.e., the multiple linear regression models (MLR) and artificial neural networks (ANNs). Unsatisfactory prediction performances were obtained for all parameters when MLR was applied. On the contrary, the good prediction of the internal quality attributes, using ANN, was observed, especially for both antioxidant activity and the total monomeric anthocyanin (R2 = 0.906, and R2 = 0.943, respectively). This study highlighted that color coordinates coupled with ANN can be successfully used to evaluate the quality of strawberry.

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Section: Introductionmentioning

confidence: 99%

Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

Amoriello¹,

Ciccoritti

Ferrante

2022

Agronomy

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“…Neural networks have been used for many years in agricultural and food industries, for example, for classifying the quality of beans [ 74 ], citrus fruits [ 75 ], apples [ 76 ], plums, tomatoes [ 77 ], coffee beans [ 74 , 78 ], and strawberries [ 79 ], or detecting plant diseases on plantations [ 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 ] due to the ease of solving troublesome, difficult, or multidimensional linear and nonlinear problems, which, using traditional software techniques, require the use of complicated algorithms and, thus, a lot of time and dedication.…”

Section: Application Of Artificial Intelligence Methodsmentioning

confidence: 99%

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques

Danielak

Przybył

Koszela

2023

Sensors

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This article describes chemical and physical parameters, including their role in the storage, trade, and processing of potatoes, as well as their nutritional properties and health benefits resulting from their consumption. An analysis of the share of losses occurring during the production process is presented. The methods and applications used in recent years to estimate the physical and chemical parameters of potatoes during their storage and processing, which determine the quality of potatoes, are presented. The potential of the technologies used to classify the quality of potatoes, mechanical and ultrasonic, and image processing and analysis using vision systems, as well as their use in applications with artificial intelligence, are discussed.

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“…In general, during storage, softening and waterlogging of the tissue in a small area on the surface of strawberries will initially occur, giving the appearance of a bruise (Choi et al, 2021); the area will then be susceptible to mold invasion and further spoilage (Cristiana et al, 2014). The methods of categorization proposed by Li et al (2022) and Zheng et al (2022) were used with modifications.…”

Section: Appearance Observed and Recordedmentioning

confidence: 99%

A new strategy to design novel modified atmosphere packaging formulation maintains the qualities of postharvest strawberries (Fragaria ananassa) during low‐temperature storage

Cheng,

Huang,

Chan

et al. 2023

Journal of Food Safety

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For fresh foods, the purpose and benefits of modified atmosphere packaging (MAP) generally include the preservation of antioxidants, the extension of shelf life, deferral of microbial spoilage, and retention of product color. Commercial MAP products frequently involve a mixture of nitrogen, oxygen, and carbon dioxide, affecting consumer acceptability, cost‐effectiveness, and hygienic food safety issues. This study investigated a novel active MAP formulation for strawberries in retail packaging which ensures changes in quality‐related physicochemical characteristics [including hardness, moisture, vitamin C, total soluble solids (TSS), titratable acid, and appearance] during low‐temperature storage, whereas samples of different storage times were assessed for shelf life by sensory evaluation. The innovative active MAP formulation of 0.8 g of sodium ascorbate, 2.6 g of sodium carbonate, and 1.6 g of ferrous sulfate achieved atmosphere equilibrium in packaged strawberries in <48 h, compared to 5–6 days in the control group. Strawberries treated with active MAP maintained their moisture, firmness, vitamin C, TSS, and titratable acid contents better than the control group (p < 0.05). The strawberries showed softening and waterlogging of tissue and 50% moldiness by the 14th day, while the control group lost commercial value by the 8th. New applications of similar commodities in food are unavailable based on current knowledge. Therefore, the scope of fresh food applications covered in this study and the refinement of other functions will be helpful directions for future research.

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Applying convolutional neural networks to assess the external quality of strawberries

Cited by 16 publications

References 27 publications

Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

Prediction of Strawberries’ Quality Parameters Using Artificial Neural Networks

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques

A new strategy to design novel modified atmosphere packaging formulation maintains the qualities of postharvest strawberries (Fragaria ananassa) during low‐temperature storage

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