Artificial neural network as an alternative for peach fruit mass prediction by non-destructive method

Rosado, Renato Domiciano Silva; Penso, Gener Augusto; Serafini, Gabriel Antônio Dalapícula; Santos, Carlos Eduardo Magalhães dos; Picoli, Edgard Augusto de Toledo; Cruz, Cosme Damião; Barreto, Cynthia Aparecida Valiati; Nascimento, Moysés; Cecon, Paulo Roberto

doi:10.1016/j.scienta.2022.111014

Cited by 4 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, ANN could be employed as a tool for the identification of peach varieties based on physical characteristics [ 85 ]. Finally, ANN can be utilized as an alternative tool for fruit mass prediction of ber fruits ( Ziziphus mauritiana Lamk) [ 86 ] and peach fruits [ 87 ].…”

Section: Resultsmentioning

confidence: 99%

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Al-Saif

Abdel-Sattar

Aboukarima

et al. 2022

Foods

View full text Add to dashboard Cite

The grape is a very well-liked fruit that is valued for its distinct flavor and several health benefits, including antioxidants, anthocyanins, soluble sugars, minerals, phenolics, flavonoids, organic acids, and vitamins, which significantly improve the product’s overall quality. Today’s supply chain as a whole needs quick and easy methods for evaluating fruit quality. Thus, the objective of this study was to estimate the quality attributes of Flame Seedless grape berries cultivated under various agronomical management and other practices using color space coordinates (berry L*, berry a*, and berry b*) as inputs in an artificial neural network (ANN) model with the best topology of (3-20-11). Satisfactory predictions based on the R2 range, which was 0.9817 to 0.9983, were obtained for physical properties (i.e., berry weight, berry length, and berry diameter as well as berry adherence strength) and chemical properties (i.e., anthocyanin, total soluble solids (TSS), TSS/titratable acidity, total sugars, titratable acidity, reducing sugars, and non-reducing sugars). Meanwhile, we also performed a contribution analysis to analyze the relative importance of CIELab colorimeter parameters of berries L*, a*, and b* to determine the main fruit quality. In terms of relative contribution, berry b* contributed relatively largely to berry weight, berry adherence strength, TSS, TSS/titratable acidity, titratable acidity, total sugars, reducing sugars, and non-reducing sugars and a* contributed relatively largely to anthocyanin, berry length, and berry diameter. The developed ANN prediction model can aid growers in enhancing the quality of Flame Seedless grape berries by selecting suitable agronomical management and other practices to avoid potential quality issues that could affect consumers of them. This research demonstrated how color space coordinates and ANN model may well be utilized to evaluate the Flame seedless grape berries’ quality.

show abstract

Section: Resultsmentioning

confidence: 99%

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Al-Saif

Abdel-Sattar

Aboukarima

et al. 2022

Foods

View full text Add to dashboard Cite

show abstract

Research on intelligent prediction of hydrogen pipeline leakage fire based on Finite Ridgelet neural network

Zhao

Gao

et al. 2022

International Journal of Hydrogen Energy

View full text Add to dashboard Cite

Morphological Variation of Strychnos spinosa Lam. Morphotypes: A Case Study at Bonamanzi Game Reserve, KwaZulu-Natal, South Africa

et al. 2022

View full text Add to dashboard Cite

Strychnos spinosa Lam. of the Loganiaceae family is associated with versatility, poverty eradication, and rural economic development. However, the morphological diversity of S. spinosa is not well documented. This limits efforts toward its improvement and commercial exploitation. This study aimed to characterize the variability, vegetative and reproductive traits, and heritability of S. spinosa morphotypes at Bonamanzi Game Reserve. The majority of the morphotypes had green, rough, round immature fruits with dark green, elongated, open fully developed leaves. Fruits varied between roundish and pyriform shape as well as green and purple colour. Positive correlation was mainly seen between fruit and seed traits. Principal component analysis indicated fruit and seed traits as major discriminating factors for morphotypes, followed by leaf size and seed thickness. A dendrogram primarily grouped morphotypes according to fruit texture. Genotypic variance and genotypic coefficient of variation were higher than environmental variance and environmental coefficient variation in reproductive traits of S. spinosa. A rough pericarp texture and purple tinge on the immature leaves of some morphotypes was recorded for the first time. Differences in morphological features across S. spinosa morphotypes indicate a high level of diversity that could be utilized by breeders to generate new cultivars. This first report on variability and heritability among S. spinosa morphotypes forms the basis of available germplasm essential for future breeding programs.

show abstract

Artificial neural network as an alternative for peach fruit mass prediction by non-destructive method

Cited by 4 publications

References 27 publications

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Research on intelligent prediction of hydrogen pipeline leakage fire based on Finite Ridgelet neural network

Morphological Variation of Strychnos spinosa Lam. Morphotypes: A Case Study at Bonamanzi Game Reserve, KwaZulu-Natal, South Africa

Contact Info

Product

Resources

About