Computer vision based automated billing system for fruit stores

Sugadev, M.; Sucharitha, Kudapa; Sheeba, I.Rexline; Velan, Balamurugan

doi:10.1109/icssit48917.2020.9214101

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…In recent years, human visual perception has been simulated using DL models to separate different fruits or vegetables in order to reduce cost and error (Sugadev et al, 2020). According to (Xue et al, 2020), an accurate fruit classification is considered of interest in the fresh supply chain, factories, and supermarkets.…”

Section: ) Fruit Recognitionmentioning

confidence: 99%

“…Therefore, DL algorithms and computer-vision techniques work together causing efficient fruit recognition. For example, they enhance the accuracy of the banana cultivar detection system (Dittakan et al, 2017) and automate the recognition of multiclass fruits (shape and size) (Mureşan & Oltean, 2018;Sugadev et al, 2020;Xue et al, 2020). To forecast crop yield, the review findings indicated that it provides effective decision-making for the monitoring and estimation of harvests for farmers (Lima Neto et al, 2020;de Souza et al, 2019;Rebortera & Fajardo, 2019a and assistance to policymakers to plan the future more efficiently (Rathod & Mishra, 2018).…”

Section: Proposed Framework and Future Challenges Ai-bsc Performance ...mentioning

confidence: 99%

“…( Dittakan et al, 2017), (Mureşan & Oltean, 2018), (Xue et al, 2020), (Sugadev et al, 2020) Crop yield Crop yield forecasting. Classifying the yield level of the banana crop (tons/hectare) or estimating the harvest period (days).…”

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confidence: 99%

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Recent Developments of Artificial Intelligence for Banana: Application Areas, Learning Algorithms, and Future Challenges

Almeyda

Ipanaqué

2022

Eng. Agríc.

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Bananas are the world's most traded fruits. Several analytical models using artificial intelligence (AI) have been developed to resolve challenges facing the banana supply chain. The number of publications in this field has steadily increased each year. However, a literature review regarding the trends of recent AI developments is not available. Thus, this study reviews the current scenario of scientific research involving AI in the stages of the banana supply chain (pre-harvest, harvest, post-harvest, processing and retail). This review covers literature published between 2015 and 2020 from online databases. Fiftytwo relevant studies were retrieved from 23 countries. Consequently, we propose an AIperformance framework based on real applications implemented for bananas: the application domain, learning algorithms, performance metrics, and reported impacts. This paper discovers 11 AI-application areas for bananas, such as ripeness, leaf diseases, quality grading, crop type, crop yield, and soil control. Moreover, this review summarizes the main functionality of learning algorithms found in the literature (ANN, CNN, SVM, and K-NN). Finally, the future challenges are discussed. This comprehensive review will help researchers understand AI applications in the banana sector and analyze the knowledge gap for future studies.

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Section: ) Fruit Recognitionmentioning

confidence: 99%

Section: Proposed Framework and Future Challenges Ai-bsc Performance ...mentioning

confidence: 99%

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Recent Developments of Artificial Intelligence for Banana: Application Areas, Learning Algorithms, and Future Challenges

Almeyda

Ipanaqué

2022

Eng. Agríc.

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“…In recent years, there have been remarkable advancements in video-based fruit sorting systems driven by the rapid progress in computer vision, machine learning, and deep learning techniques [10,11]. These technologies have enabled the development of more sophisticated and efficient algorithms for fruit classification, leading to improved accuracy and speed in sorting operations [12,13].…”

Section: Introductionmentioning

confidence: 99%

Automated Fruit Sorting in Smart Agriculture System: Analysis of Deep Learning-based Algorithms

Liu,

Niu

2024

IJACSA

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Automated fruit sorting plays a crucial role in smart agriculture, enabling efficient and accurate classification of fruits based on various quality parameters. Traditionally, rulebased and machine-learning methods have been employed for fruit sorting, but in recent years, deep learning-based approaches have gained significant attention. This paper investigates deep learning methods for fruit sorting and justifies their prevalence in the field. Therefore, it is necessary to address these limitations and improve the effectiveness of CNN-based fruit sorting methods. This research paper presents a comprehensive analysis of CNN-based methods, highlighting their strengths and limitations. This analysis aims to contribute to advancing automated fruit sorting in smart agriculture and provide insights for future research and development in deep learning-based fruit sorting techniques.

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PseudoAugment: Enabling Smart Checkout Adoption for New Classes Without Human Annotation

Nesteruk,

Illarionova,

Zherebzov

et al. 2023

IEEE Access

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Computer vision based automated billing system for fruit stores

Cited by 8 publications

References 9 publications

Recent Developments of Artificial Intelligence for Banana: Application Areas, Learning Algorithms, and Future Challenges

Recent Developments of Artificial Intelligence for Banana: Application Areas, Learning Algorithms, and Future Challenges

Automated Fruit Sorting in Smart Agriculture System: Analysis of Deep Learning-based Algorithms

PseudoAugment: Enabling Smart Checkout Adoption for New Classes Without Human Annotation

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