Analysis recognition of Ghost Pepper and Cili-Padi using Mask RCNN and YOLO

• Autorzy: Yin L. L. , Zainudin M.N.Shah , Saad W. H. Mohd , Sulaiman N. A. , Idris M. I. , Kamarudin M. R. , Mohamed R. , Razak M. S. J. A.

Identyfikatory

DOI

10.15199/48.2023.08.15

Warianty tytułu

PL

Analiza i rozpoznawanie Ghost Pepper i Cili-Padi przy użyciu Mask-RCNN i YOLO

• Języki publikacji: EN

Abstrakty

EN

Fruit harvesting robots have made headlines in the agricultural industry in recent years. A fruit recognition system would assist farmers or agricultural industry practitioners in lessening workloads while increasing crop yields. Due to the similar characteristics of chili fruits, approximating the chili according to their grades and identifying its maturity will be difficult. Furthermore, because of their different appearances and sizes, distinguishing between the fruits and the leaves becomes difficult. As a result, a real-time object detection algorithm called You Only Look Once (YOLO) and Mask-RCNN is investigates in order to distinguish the fruit from its plant based on its shape and colour. YOLO version 5 (YOLOv5) uses to define and distinguish the chili fruits and its leaves based on two characteristics; shape and colour. The CSPDarknet network serves as the backbone in YOLOv5, where feature extraction and mosaic augmentation has used to combine multiple images into a single image. Total 391 images has divided into two subsets: training and testing, with an 80:20 ratio. YoLov5 is notable for its ability to detect small objects with high precision in a short amount of time while Mask-RCNN has proven its ability to recognize a chili fruits with high precision above 90%. The classification is evaluated using precision, recall, loss function, and inference time.

PL

Roboty do zbioru owoców trafiły w ostatnich latach na pierwsze strony gazet w branży rolniczej. System rozpoznawania owoców pomógłby rolnikom lub praktykom z branży rolniczej w zmniejszeniu obciążenia pracą przy jednoczesnym zwiększeniu plonów. Ze względu na podobne cechy owoców chili przybliżenie chili według ich klas i określenie stopnia dojrzałości będzie trudne. Ponadto, ze względu na ich różny wygląd i rozmiary, odróżnienie owoców od liści staje się trudne. W rezultacie algorytm wykrywania obiektów w czasie rzeczywistym o nazwie You Only Look Once (YOLO) i Mask-RCNN jest badany w celu odróżnienia owocu od rośliny na podstawie jego kształtu i koloru. YOLO wersja 5 (YOLOv5) służy do definiowania i rozróżniania owoców chili i ich liści w oparciu o dwie cechy; kształt i kolor. Sieć CSPDarknet służy jako szkielet w YOLOv5, w którym wyodrębnianie cech i rozszerzanie mozaiki wykorzystano do łączenia wielu obrazów w jeden obraz. Łącznie 391 obrazów zostało podzielonych na dwa podzbiory: treningowe i testowe, ze stosunkiem 80:20. YoLov5 wyróżnia się zdolnością do wykrywania małych obiektów z dużą precyzją w krótkim czasie, podczas gdy Mask-RCNN udowodnił swoją zdolność rozpoznawania owoców chili z wysoką precyzją powyżej 90%. Klasyfikacja jest oceniana za pomocą precyzji, pamięci, funkcji utraty i czasu wnioskowania.

Słowa kluczowe

EN

YOLO; CSPDarknet; CNN; Mask RCNN; chili

PL

YOLO; przetwarzanie obrazu

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 8
• Strony: 92--97
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Yin L. L.

• Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

• https://orcid.org/0000-0001-5621-9632+

autor

Zainudin M.N.Shah

• Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

autor

Saad W. H. Mohd

• Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

autor

Sulaiman N. A.

• Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

autor

Idris M. I.

• Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

autor

Kamarudin M. R.

• Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

autor

Mohamed R.

• Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, UPM Serdang, 43400, Seri Kembangan, Selangor, Malaysia

autor

Razak M. S. J. A.

• 3MSJ Perwira Enterprise [202103095516 (SA0563088-W)], Duyung, 75450 Melaka, Malaysia

Bibliografia

• [1] J. Gené-mola, V. Vilaplana, J. R. Rosell-polo, J. Morros, J. Ruiz-hidalgo, and E. Gregorio, Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities, Computers and Electronics in Agriculture, (2019), vol. 162, pp. 689-698.
• [2] G. Wu, Q. Zhu, M. Huang, Y. Guo, and J. Qin, Automatic recognition of juicy peaches on trees based on 3D contour features and colour data, Biosystems Engineering, (2019), vol. 188, pp. 1-13.
• [3] A. Kuznetsova, T. Maleva, and V. Soloviev, Detecting Apples in Orchards Using YOLOv3 and YOLOv5 in General and Close Up Images, LNCS. (2020), vol. 12557, pp. 233-243.
• [4] W. Wu, H. Liu, L. Li, Y. Long, X. Wang, Z. Wang, J. Li and Y. Chang, Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image, PLoS One, (2021), vol. 16, no. 10.
• [5] F. Zhou, H. Zhao, and Z. Nie, Safety Helmet Detection Based on YOLOv5, Proc. 2021 IEEE Int. Conference on Power Electronics, Computer Applications (2021).
• [6] R. Xu, H. Lin, K. Lu, L. Cao, and Y. Liu, A forest fire detection system based on ensemble learning, Forests, (2021), vol. 12, no. 2.
• [7] M. Ferguson, R. Ak, Y.T.T. Lee, and K.H. Law, Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning, Smart Sustain. Manuf. Syst., (2018), vol. 2, no. 1.
• [8] J. Redmon, and A. Farhadi, YOLO v.3, Tech Report (2018).
• [9] Q. Song, S. Li, Q. Bai, J. Yang, X. Zhang, Z. Li, and Z. Duan, Object detection method for grasping robot based on improved yolov5, Micromachines, (2021), vol. 12, no. 11.
• [10] M.N.S. Zainudin, N. Hussin, W.H.M. Saad, S.M. Radzi, Z.M. Noh, N.A. Sulaiman, and M.S.J.A.Razak, A framework for chili fruits maturity estimation using deep learning convolutional neural network, Przeglad Elektrotechniczny, (2021), vol. 11, no. 2021.
• [11] R. Mohamed, T. Perumal, M.N. Sulaiman, N. Mustapha, and M.N.S. Zainudin, Modeling activity recognition of multi resident using label combination of multi label classification in smart home, International Conference on Applied Science and Technology, (2017).
• [12] N.A. Sulaiman, M.P. Abdullah, H. Abdullah, M.N.S. Zainudin, and A.M. Yusop, Fault detection for air conditioning system using machine learning, IAES International Journal of Artificial Intelligence, (2020), vol. 9, no. 1.
• [13] Y.J. Kee, M.N.S. Zainudin, M.I. Idris, R.H. Ramlee, and M.R. Kamarudin, Activity Recognition on Subject Independent Using Machine Learning, Cybernatics and Information Technologies, (2020), vol. 20, no. 3.
• [14] B. Venkatesh and J. Anuradha, A Review of Feature Selection and its Methods, Cybernatics and Information Technologies, (2019), vol. 19 no. 1.
• [15] M. L. Praburaj, Role of Agriculture in the Economic Development of a Country, Int. J. Commer. (2018) vol. 6, no. 3, pp. 2.
• [16] M. Mraz, P. Findura, O. Urbanovicoba, I. rigo, P. Bajus, T. Drozdz and P. Keilbasa, Development of the web application by the information system for data processing and documentation on selected farm in agricultural production, Przeglad Elektrotechniczny, (2020), vol. 1, no. 218, pp. 218- 221.
• [17] U. Nepal, and H. Eslamiat, Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs. Sensors. (2022), vol .22, no. 2, pp. 464.
• [18] M.N.S. Zainudin, M.S.S.S. Azlan, L.L.Yin, and W.H.Mohd Saad, Analysis on Localization and Prediction of Depth Chili Fruits Images Using YOLOv5, International Journal of Advanced Technology and Engineering Exploration. (2022), vol. 9, no. 97, pp. 1786-1801.
• [19] R.Y. Choi, A.S. Coyner, J. Kalpathy-cramer, M.F. Chiang, and J.P. Campbell, Introduction to machine learning, neural networks, and deep learning, Translational Vision Science & Technology, (2020), vol. 9, no. 2, pp. 1-14.
• [20] S. Kumar, A. Balyan, and M. Chawla, Object detection and recognition in images, International Journal of Engineering Development and Research, (2017), vol. 5, no. 4, pp. 1029-34.
• [21] Y. Tian, G. Yang, Z. Wang, H. Wang, E. Li, and Z. Liang, Apple detection during different growth stages in orchards using the improved YOLO-V3 model, Computers and Electronics in Agriculture, (2019), vol. 157, no. 417-26.
• [22] Y. Tian, G. Yang, Z. Wang, E. Li, and Z. Liang, Detection of apple lesions in orchards based on deep learning methods of cyclegan and yolov3-dense, Journal of Sensors, (2019), vol. 2019, pp. 1-14.
• [23] A. Kuznetsova, T. Maleva, and V. Soloviev, Using YOLOv3 algorithm with pre-and post-processing for apple detection in fruit-harvesting robot, Agronomy, (2020), vol. 10, no. 7, pp.1- 19.
• [24] J. Liu, and X. Wang Tomato diseases and pests detection based on improved YOLO V3 convolutional neural network, Frontiers in Plant Science, (2020), vol. 11, no. 1, pp. 1-12.
• [25] M.O. Lawal, Tomato detection based on modified YOLOv3 framework, Scientific Reports, (2021), vol. 11, no. 1, pp.; 1-11.
• [26] L. Fu, Y. Feng, J. Wu, Z. Liu, F. Gao, Y. Majeed, Fast and accurate detection of kiwifruit in orchard using improved YOLOv3-tiny model, Precision Agriculture, (2021), vol. 22, no. 3, pp. 754-76.
• [27] J. Yao, J. Qi, J. Zhang, H. Shao, J. Yang, X. Li, A real time detection algorithm for Kiwifruit defects based on YOLOv5, Electronics, (2021), vol. 10, no. 14, pp.1-13.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).