Fast olive quality assessment through RGB images and advanced convolutional neural network modeling

Salvucci, Giorgia; Pallottino, Federico; Laurentiis, Leonardo De; Frate, Fabio Del; Manganiello, Rossella; Tocci, Francesco; Vasta, Simone; Figorilli, Simone; Bassotti, Beatrice; Violino, Simona; Ortenzi, Luciano; Antonucci, Francesca

doi:10.1007/s00217-022-03971-7

Cited by 11 publications

(16 citation statements)

References 15 publications

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“…To avoid alteration from external light sources, the camera was covered by a box equipped with controlled Light Emitting Diode (LED) strips to uniformly illuminate the samples. Therefore, the camera was connected to a PC, which ran software made in Java based on multithreading programming structured to manage image acquisition settings, general settings, graphical user interface and serial acquisition of multiple files [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

Olive Fruit Selection through AI Algorithms and RGB Imaging

Figorilli

Violino

Moscovini

et al. 2022

Foods

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(1) Background: Extra virgin olive oil production is strictly influenced by the quality of fruits. The optical selection allows for obtaining high quality oils starting from batches with different qualitative characteristics. This study aims to test a CNN algorithm in order to assess its potential for olive classification into several quality classes for industrial purposes, specifically its potential integration and sorting performance evaluation. (2) Methods: The acquired samples were all subjected to visual analysis by a trained operator for the distinction of the products in five classes related to the state of external veraison and the presence of visible defects. The olive samples were placed at a regular distance and in a fixed position on a conveyor belt that moved at a constant speed of 1 cm/s. The images of the olives were taken every 15 s with a compact industrial RGB camera mounted on the main frame in aluminum to allow overlapping of the images, and to avoid loss of information. (3) Results: The modelling approaches used, all based on AI techniques, showed excellent results for both RGB datasets. (4) Conclusions: The presented approach regarding the qualitative discrimination of olive fruits shows its potential for both sorting machine performance evaluation and for future implementation on machines used for industrial sorting processes.

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Section: Methodsmentioning

confidence: 99%

Olive Fruit Selection through AI Algorithms and RGB Imaging

Figorilli

Violino

Moscovini

et al. 2022

Foods

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“…The category "Other Fruits" corresponds to fruits that are analyzed in a single article, usually fruits of local origin. Agrivita 1 [115] Applied Intelligence 1 [116] Computational Intelligence and Neuroscience 1 [117] Computer Systems Science and Engineering 1 [118] Computers Electrical Engineering 1 [119] Data in Brief 1 [42] Ecological Informatics 1 [120] Electronics 1 [121] Energy Reports 1 [122] European Food Research and Technology 1 [123] Expert Systems With Applications 1 [124] Frontiers in Robotics and AI 1 [125] Horticulturae 1 [126] Journal of Robotics and Mechatronics 1 [135] Jurnal Kejuruteraan 1 [136] Mathematical Problems in Engineering 1 [137] Continued on next page Mechanical Systems and Signal Processing 1 [139] Multimedia Systems 1 [140] Neural Computing Applications 1 [141] Neural Network World 1 [142] Neural Networks 1 [143] Neurocomputing 1 [144] Plant and Cell Physiology 1 [145] Plants 1 [146] Postharvest Biology and Technology 1 [147] Procedia Computer Science 1 [148] Remote Sensing 1 [149] Results in Engineering 1 [150] Scientia Horticulturae 1 [151] Scientific African 1 [152] Scientific Programming 1 [153] Scientific Reports 1 [154] Sn Applied Sciences 1 [50] Sustainability 1 [155] Traitement du Signal 1 [156] Visual Computer 1 [52]…”

Section: F Publication Metadatamentioning

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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“…While two-stage models, including Fast R-CNN [12], Faster R-CNN [13], and Sparse R-CNN [14], show better accuracy and recall due to decoupling localization and classification, one-stage models, such as the Yolo series [15][16][17] and Single Shot Multi-Box Detector [18], exhibit faster inference speed. Some researchers have introduced deep learning to fruit detection and counting; for example, [19] used the Yolo detection model to perform real-time rank classification of RBG images of olives, while [20] used Faster R-CNN and SSD to track and count different fruits separately.…”

Section: Introductionmentioning

confidence: 99%

Fruit Detection and Counting in Apple Orchards Based on Improved Yolov7 and Multi-Object Tracking Methods

Hu,

Fan,

Wang

et al. 2023

Sensors

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With the increasing popularity of online fruit sales, accurately predicting fruit yields has become crucial for optimizing logistics and storage strategies. However, existing manual vision-based systems and sensor methods have proven inadequate for solving the complex problem of fruit yield counting, as they struggle with issues such as crop overlap and variable lighting conditions. Recently CNN-based object detection models have emerged as a promising solution in the field of computer vision, but their effectiveness is limited in agricultural scenarios due to challenges such as occlusion and dissimilarity among the same fruits. To address this issue, we propose a novel variant model that combines the self-attentive mechanism of Vision Transform, a non-CNN network architecture, with Yolov7, a state-of-the-art object detection model. Our model utilizes two attention mechanisms, CBAM and CA, and is trained and tested on a dataset of apple images. In order to enable fruit counting across video frames in complex environments, we incorporate two multi-objective tracking methods based on Kalman filtering and motion trajectory prediction, namely SORT, and Cascade-SORT. Our results show that the Yolov7-CA model achieved a 91.3% mAP and 0.85 F1 score, representing a 4% improvement in mAP and 0.02 improvement in F1 score compared to using Yolov7 alone. Furthermore, three multi-object tracking methods demonstrated a significant improvement in MAE for inter-frame counting across all three test videos, with an 0.642 improvement over using yolov7 alone achieved using our multi-object tracking method. These findings suggest that our proposed model has the potential to improve fruit yield assessment methods and could have implications for decision-making in the fruit industry.

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Fast olive quality assessment through RGB images and advanced convolutional neural network modeling

Cited by 11 publications

References 15 publications

Olive Fruit Selection through AI Algorithms and RGB Imaging

Olive Fruit Selection through AI Algorithms and RGB Imaging

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

Fruit Detection and Counting in Apple Orchards Based on Improved Yolov7 and Multi-Object Tracking Methods

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