Active Perception Fruit Harvesting Robots — A Systematic Review

Magalhães, Sandro Augusto; Moreira, António Paulo; Santos, Filipe Neves dos; Dias, Jorge

doi:10.1007/s10846-022-01595-3

Cited by 13 publications

(6 citation statements)

References 250 publications

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“…In [33], the focus is on recent non-destructive analysis techniques (2022), exploring methods such as nuclear magnetic imaging, X-ray imaging, acoustic methods, etc., similar to [29]. In [34], the most commonly used methods by fruit-picking robots are analyzed, highlighting that these robots typically perform detection/segmentation tasks using classical machine learning and deep learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Espinoza,

Aguilera,

Rojas

et al. 2024

IEEE Access

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The application of Deep Learning models in fruit analysis has garnered significant attention due to its potential to revolutionize the agricultural sector and enhance crop monitoring. This paper presents a comprehensive review of recent research efforts in fruit analysis using Deep Learning techniques. The study delves into model selection, dataset characteristics, evaluation metrics, challenges, and future directions in this domain. Various model architectures, including classical Convolutional Neural Networks (CNNs) and advanced detection models like R-CNN and YOLO, have been explored for tasks ranging from fruit classification to detection. Evaluation metrics such as precision, recall, F1-score, and mean Average Precision (mAP) have been commonly used to assess model performance. Challenges, including data scarcity, labeling complexities, variations in fruit characteristics, and computational efficiency, have been identified and discussed. The paper also presents an overview of available datasets, encompassing both proprietary and publicly accessible sources. Future research directions involve exploring enhanced data augmentation, multi-modal integration, knowledge transfer across species, robustness in dynamic environments, improved computational efficiency, and practical integration of models into real-world agricultural systems. This review provides valuable insights for researchers and practitioners aiming to leverage Deep Learning for fruit analysis in the pursuit of sustainable agriculture and food production.

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Section: Related Workmentioning

confidence: 99%

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Espinoza,

Aguilera,

Rojas

et al. 2024

IEEE Access

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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].…”

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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“…Stateoftheart progress in research and future challenges is docu mented in a wide range of review papers and book chapters addressing agriculture in general [1], [3], [4], [5] and specific application domains, including phenotyping [6], [7], [8] ara ble farming [9], livestock farming [10], greenhouse horticulture [2], orchard management [11], forestry [12], and food processing [13]. Review papers also address specific technologies in the context of agricultural robotics, such as computer vision [14], [15], active perception [16], unmanned aer ial vehicle technologies [17], [18], cov erage path planning in arable farming [19], and grasping and soft grasping [20], [21]. Some illustrative examples of agrifood robotics are documented in Figure 2.…”

Section: Challengesmentioning

confidence: 99%