Detection of Peruvian Golden Berry Quality using a Convolutional Neural Network

Detection of Peruvian Golden Berry Quality using a Convolutional Neural Network
  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-1
Year of Publication : 2024
Author : Jhon Taquila-Velásquez, Alexis Vela-Guerra, Pedro Portillo-Mendoza, Carlos Sotomayor-Beltran
DOI : 10.14445/22315381/IJETT-V72I1P129

How to Cite?

Jhon Taquila-Velásquez, Alexis Vela-Guerra, Pedro Portillo-Mendoza, Carlos Sotomayor-Beltran, "Detection of Peruvian Golden Berry Quality using a Convolutional Neural Network," International Journal of Engineering Trends and Technology, vol. 72, no. 1, pp. 300-310, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I1P129

Abstract
In Peru, the National Strategic Export Plan seeks to promote exports of many fruits, among them the Peruvian golden berries. To increase the production speed of golden berries to match foreign competition, the design of a convolutional neural network (CNN) algorithm applied to the selection of this type of berry is proposed. The classification of the golden berry quality has been defined according to its condition, a good one and in poor condition. The developed classification software, at its core, is based on a pre-trained CNN called GoogLeNet, which was implemented using Matlab development software; additionally, the Matlab App designer extension was used to create a graphical user interface. The architecture of the CNN allowed us to obtain the characteristic parameters of the fruit, where the criterion for using the database considered 80% images for training, 10% for validation, and 10% for post-training. For this work, 241 images were used, divided into two blocks, as NO_GOOD (in poor condition and immature) and as GOOD (mature and in good condition). The best-trained CNN, with a validation percentage of 100%, was embedded in the user interface. A total of 6000 iterations and 500 epochs were used for the training of the CNN. The development of the user interface system allowed to select golden berries based on their quality at greater speeds than by doing it manually.

Keywords
Golden berry quality, Convolutional neural network (CNN), GoogLeNet, Matlab, User interface system.

References
[1] Market Analysis, Commercial Intelligence Unit – Ministerio de Desarrollo Agrario y Riego (MIGRADI), 2021. [Online]. Available: https://cdn.www.gob.pe/uploads/document/file/1720425/An%C3%A1lisis%20de%20Mercado%20del%20Aguaymanto.pdf
[2] Juan Cevallos Ampuero, “Radial Based Neural Networks Applied to Quality Improvement,” Revista de la Facultad de Ingenieria Industrial, vol. 11, no. 1, pp. 63-72, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Granados Giraldo et al., “Design and Construction of an Export-Type Peach Sorting Machine Using Artificial Intelligence,” Universidad Autónoma de Bucaramanda, Bucaramanga, Colombia, pp. 1-74, 2020.
[Google Scholar] [Publisher Link]
[4] Kyosuke Yamamoto et al., “On Plant Detection of Intact Tomato Fruits using Image Analysis and Machine Learning Methods,” Sensors, vol. 14, no. 7, pp. 12191-12206, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Khaled Marji Alresheedi et al., “Dates Fruit Recognition: From Classical Fusion to Deep Learning,” Computer Systems Science and Engineering, vol. 40, no. 1, pp. 151-166, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Farian Severine Ishengoma, Idris A. Rai, and Rutabayiro Ngoga Said, “Identification of Maize Leaves Infected by Fall Armyworms using UAV-Based Imagery and Convolutional Neural Networks,” Computers and Electronics in Agriculture, vol. 184, pp. 1-8, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Cristopher Bazan Yaranga, Zaid Sanchez, and Ricardo Rodriguez, “Transfer Learning Using Convolutional Neural Networks for Distracted Driver Recognition,” TECNIA, vol. 28, no. 2, pp. 1-5, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Agnieszka Mikołajczyk, and Michał Grochowski, “Data Augmentation for Improving Deep Learning In Image Classification Problem,” 2018 International Interdisciplinary PhD Workshop (IIPhDW), Świnouście, Poland, pp. 117-122, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Sergio Cubero et al., “Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables,” Food Bioprocess Technology, vol. 4, no. 4, pp. 487-504, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Guoxiang Zhang et al., “A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption,” Applied Sciences, vol. 10, no. 4, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Wael Alosaimi, Hashem Alyami, and M. Irfan Uddin, “PeachNet: Peach Diseases Detection for Automatic Harvesting,” Computers Materials and Continua, vol. 67, no. 2, pp. 1665-1677, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Jianhong Yu et al., “Target Positioning and Sorting Strategy of Fruit Sorting Robot Based on Image Processing,” Traitment du Signal, vol. 38, no. 3, pp. 797-805, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] C.K. Sunil, C.D. Jaidhar, and Nagamma Patil, “Cardamom Plant Disease Detection Approach Using EfficientNetV2,” IEEE Access, vol. 10, pp. 789-804, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] M. Ruiz-Altisent et al., “Sensors for Product Characterization and Quality of Specialty Crops-A Review,” Computers and Electronics in Agriculture, vol. 74, no. 2, pp. 176-194, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Hancong Wang et al., “A Novel Wireless Leaf Area Index Sensor Based on a Combined U-Net Deep Learning Model,” IEEE Sensors Journal, vol. 22, no. 16, pp. 16573-16585, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Fidel L´opez Saca et al., “Convolutional Neural Network with Multi-Column Feature Extraction for Image Classification,” Research in Computing Science, vol. 148, no. 7, pp. 391-404, 2019.
[CrossRef] [Google Scholar] [Publisher Link]