FruitQ: a new dataset of multiple fruit images for freshness evaluation

Abayomi‐Alli, Olusola; Damaševičius, Robertas; Misra, Sanjay; Abayomi‐Alli, Adebayo

doi:10.1007/s11042-023-16058-6

Cited by 8 publications

(2 citation statements)

References 41 publications

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“…Besides the above concerns, we will focus on the following aspects: (1) We will expand the model to include more fruit or other food datasets, such as FruitQ ( Abayomi-Alli et al, 2024 ), and optimize the model based on real-world conditions; (2) We will attempt to explore and experiment with different network architectures as shared CNN and study the impact of these different network architectures on the model; (3) We will explore other multi-task learning applications in the agricultural domain, such as precision harvesting of crops; (4) Develop a GUI frontend for the model to facilitate users in analyzing their datasets.…”

Section: Discussionmentioning

confidence: 99%

Fruit freshness detection based on multi-task convolutional neural network

Zhang,

Yang,

Cheng

et al. 2024

Current Research in Food Science

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Section: Discussionmentioning

confidence: 99%

Fruit freshness detection based on multi-task convolutional neural network

Zhang,

Yang,

Cheng

et al. 2024

Current Research in Food Science

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“…The fusion of multiple models [24,25,26], each specializing in a particular aspect of fruit classification and grading, is yet another method. Combining a CNN-based model for fruit classification with a regression-based model for grading, for instance, can result in enhanced performance levels [27,28]. These fusion-based models seek comprehensive and accurate results by capitalizing on the strengths of individual models [29,30].…”

Section: Review Of Existing Models Used For Multivariate Classificati...mentioning

confidence: 99%

DQLRFMG: Design of an Augmented Fusion of Deep Q Learning with Logistic Regression and Deep Forests for Multivariate Classification and Grading of Fruits

Said,

Joshi

2023

IJRITCC

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Accurate categorization and grading of fruits are essential in numerous fields, including agriculture, food processing, and distribution. This paper addresses the need for an advanced model capable of classifying and grading fruits more effectively than existing methods. Traditional approaches are limited by their lower precision, accuracy, recall, area under the curve (AUC), and delay. In order to overcome these obstacles, the proposed model combines the capabilities of Deep Q Learning (DQL) for classification and Logistic Regression (LR) with Deep Forests for fruit grading process. Three distinct datasets were used to evaluate the model: the Kaggle - Fruits 360 Dataset, the FRont Experimental System for High throughput plant phenotyping Datasets, and ImageNet samples. In multiple respects, comparative analysis demonstrates that the proposed model outperforms existing methods. Specifically, it achieves a remarkable 4.9% improvement in precision, 5.5% improvement in accuracy, 4.5% improvement in recall, 3.9% improvement in AUC, and an 8.5% reduction in delay levels. Utilizing the strengths of both DQL and LR with Deep Forests, the proposed model achieves its superior performance. DQL, a technique for reinforcement learning, provides the ability to learn and make decisions based on the feedback from the environment. By combining DQL and LR, the classification accuracy is improved, allowing for the precise identification of fruit varieties including Mango, Apple. Papaya, etc. In addition, Deep Forests, a novel framework for ensemble learning, is utilized for fruit grading. Deep Forests utilizes decision trees to effectively capture complex patterns in the data, allowing for dependable and robust fruit grading. Experimental findings indicate that the combination of DQL and LR with Deep Forests yields remarkable performance improvements in fruit classification and grading tasks. Improved precision, accuracy, recall, AUC, and delay indicate the model's superiority over existing methods. This research contributes to the field of fruit classification and grading by developing a sophisticated model that can support a variety of applications in the agriculture, food processing, and distribution industries.

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Plant leaf infected spot segmentation using robust encoder-decoder cascaded deep learning model

Femi,

Anandan Mukunthan

2023

Network: Computation in Neural Systems

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FruitQ: a new dataset of multiple fruit images for freshness evaluation

Cited by 8 publications

References 41 publications

Fruit freshness detection based on multi-task convolutional neural network

Fruit freshness detection based on multi-task convolutional neural network

DQLRFMG: Design of an Augmented Fusion of Deep Q Learning with Logistic Regression and Deep Forests for Multivariate Classification and Grading of Fruits

Plant leaf infected spot segmentation using robust encoder-decoder cascaded deep learning model

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