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Research Articles

Guava fruit (Psidium guajava) damage and disease detection using deep convolutional neural networks and thermal imaging

Pages 102-116 | Received 12 Oct 2022, Accepted 25 Dec 2022, Published online: 02 Jan 2023

References

  • Guava Export From India | Data, price & analysis of Guava export. https://connect2india.com/global/Guava-export-from-india/1.
  • Ishimwe R, Abutaleb K, Ahmed F. Applications of thermal imaging in agriculture—a review. Adv Remote Sens. 2014;03(03):128–140. doi:10.4236/ars.2014.33011.
  • He Y, Xiao Q, Bai X, et al. Recent progress of nondestructive techniques for fruits damage inspection: a review. Crit Rev Food Sci Nutr. Published online 2021. doi:10.1080/10408398.2021.1885342
  • Mahanti NK, Pandiselvam R, Kothakota A, et al. Emerging non-destructive imaging techniques for fruit damage detection: image processing and analysis. Trends Food Sci Technol. Published online 2021. doi:10.1016/j.tifs.2021.12.021.
  • Bhargava A, Bansal A. Fruits and vegetables quality evaluation using computer vision: A review. J King Saud Univ – Comput Inf Sci. 2021;33(3):243–257. doi:10.1016/j.jksuci.2018.06.002.
  • Nomani A, Ansari Y, Nasirpour MH, et al. PSOWNNs-CNN: a computational radiology for breast cancer diagnosis improvement based on image processing using machine learning methods. Comput Intell Neurosci. 2022:1–17. doi:10.1155/2022/5667264
  • Zangeneh Soroush M, Tahvilian P, Nasirpour MH, et al. EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms. Front Physiol. 2022;13. doi:10.3389/fphys.2022.910368.
  • Ahmadi M, Sharifi A, Jafarian Fard M, et al. Detection of brain lesion location in MRI images using convolutional neural network and robust PCA. Int J Neurosci. Published online 2021. doi:10.1080/00207454.2021.1883602
  • Hassantabar S, Ahmadi M, Sharifi A. Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches. Chaos Solitons Fractals. 2020;140:110170. doi:10.1016/j.chaos.2020.110170.
  • Zhou L, Zhang C, Liu F, et al. Application of deep learning in food: a review. Compr Rev Food Sci Food Saf. 2019;18(6):1793–1811. doi:10.1111/1541-4337.12492.
  • Sheril Angel J, Mary EJ, Dikshna U, et al. Deep learning based disease detection in tomatoes. In: 2021 3rd International Conference on Signal Processing and Communication, ICPSC 2021. Institute of Electrical and Electronics Engineers Inc.; 2021. p. 388–392. doi:10.1109/ICSPC51351.2021.9451731.
  • Andrushia AD, Patricia AT. Artificial bee colony optimization (ABC) for grape leaves disease detection. Evol Syst. 2020;11(1):105–117. doi:10.1007/s12530-019-09289-2.
  • Puspha Annabel LS, Annapoorani T, Deepalakshmi P. Machine learning for plant leaf disease detection and classification – a review. In 2019 International Conference on Communication and Signal Processing (ICCSP); 2019. p. 0538–0542. doi:10.1109/ICCSP.2019.8698004.
  • Sharma A, Lakhwani K, Singh Janeja H. Plant disease identification using deep learning: a systematic review. In Proceedings of 2021 2nd International Conference on Intelligent Engineering and Management, ICIEM 2021. 2021;90(February):222–227. doi:10.1109/ICIEM51511.2021.9445277.
  • Sinha A, Singh Shekhawat R. A novel image classification technique for spot and blight diseases in plant leaves. Imaging Sci J. 2021;68(4):225–239. doi:10.1080/13682199.2020.1865652.
  • Almutiry O, Ayaz M, Sadad T, et al. A novel framework for multi-classification of guava disease. Comput Mater Continua. 2021;69(2):1915–1926. doi:10.32604/cmc.2021.017702.
  • Sai Reddy B, Neeraja S. Plant leaf disease classification and damage detection system using deep learning models. Multimed Tools Appl. 2022;81(17):24021–24040. doi:10.1007/s11042-022-12147-0.
  • Romero Fogué D, Masot Peris R, Ibáñez Civera J, et al. Monitoring freeze-damage in grapefruit by electric bioimpedance spectroscopy and electric equivalent models. Horticulturae. Published online 2022. doi:10.3390/horticulturae8030218.
  • Ahmad I, Yang Y, Yue Y, et al. Deep learning based detector YOLOv5 for identifying insect pests. Appl Sci. 2022;12:10167. doi:10.3390/app121910167.
  • Cao Y, Zhang Y, Lin M, et al. Non-destructive detection of damaged strawberries after impact based on analyzing volatile organic compounds. Sensors. 2022;22(2):427. doi:10.3390/s22020427
  • Sabzi S, Nadimi M, Abbaspour-Gilandeh Y, et al. Non-destructive estimation of physicochemical properties and detection of ripeness level of apples using machine vision. Int J Fruit Sci. 2022;22(1):628–645. doi:10.1080/15538362.2022.2092580.
  • Ramírez Alberto L, Eduardo Cabrera AC, Augusto Prieto OF. A computer vision system for early detection of anthracnose in sugar mango (Mangifera indica) based on UV-A illumination. Inf Process Agric. Published online 2022. doi:10.1016/j.inpa.2022.02.001
  • Guo B, Li B, Huang Y, et al. Bruise detection and classification of strawberries based on thermal images. Food Bioproc Tech. 2022;15(5):1133–1141. doi:10.1007/s11947-022-02804-5.
  • Choudhury T, Mahdi HF, Agarwal A, et al. Quality evaluation in Guavas using deep learning architectures: an experimental review. In: 2022 international congress on human-computer interaction, optimization and robotic applications (HORA). IEEE; 2022. p. 1–6. doi:10.1109/HORA55278.2022.9799824
  • Ajib Susanto IUWM. A good accuracy in apple fruits quality based on back propagation neural network and feature extraction. J Inf Telecommun Eng. 2022;6(1):38–48. doi:10.31289/jite.v6i1.6938.
  • Htike T, Saengrayap R, Aunsri N, et al. Investigation and evaluation of impact bruising in Guava using image processing and response surface methodology. Horticulturae. 2021;7(10):411. doi:10.3390/horticulturae7100411.
  • Mehra V. Guavanet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer [thesis]. Rochester Institute of Technology; 2021. Available from https://scholarworks.rit.edu/theses/10703.
  • Mostafa AM, Kumar SA, Meraj T, et al. Guava disease detection using deep convolutional neural networks: a case study of guava plants. Appl Sci. 2022;12(1):239. doi:10.3390/app12010239.
  • Judith NK, Jasper KI, George OA, et al. Harvesting, postharvest handling, hygiene knowledge and practices of guava fruit farmers: A comparative study of two counties of Kenya. Afr J Food Sci. 2021;15(5):177–189. doi:10.5897/AJFS2021.2079.
  • Bagavathiappan S, Lahiri BB, Saravanan T, et al. Infrared thermography for condition monitoring – a review. Infrared Phys Technol. 2013;60:35–55. doi:10.1016/j.infrared.2013.03.006.
  • Choudhury M, Saikia T, Banik S, et al. Infrared imaging a new non-invasive machine learning technology for animal husbandry. Imaging Sci J. 2020;68(4):240–249. doi:10.1080/13682199.2020.1848084.
  • Makky M, Cherie D. Pre-harvest oil palm FFB nondestructive evaluation technique using thermal-imaging device. IOP Conf Ser Earth Environ Sci. 2021;757(1). doi:10.1088/1755-1315/757/1/012003.
  • Klimkiewicz M, Sokolnicki Ł, Tucki K. Detection of potatoes damages by thermal imaging. Ann Warsaw Univ Life Sci – SGGW, Agricult. 2017;70(March):105–112. doi:10.22630/aafe.2017.70.23.
  • Gurupatham SK, Ilksoy E, Jacob N, et al. Fruit ripeness estimation for avocado using thermal imaging. In: ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE); 2018; 8B-2018. p. 1–5. doi:10.1115/imece2018-86290.
  • Plasquy E, Garcia JM, Florido MC, et al. Estimation of the cooling rate of six olive cultivars using thermal imaging. Agric (Switz). 2021;11(2):164–113. doi:10.3390/agriculture11020164.
  • Zolfagharnassab S, Mohamed Shariff AR, Ehsani R. Emissivity determination of oil palm fresh fruit ripeness using a thermal imaging technique. In: Acta horticulturae. Vol. 1152. International Society for Horticultural Science; 2017. p. 189–193. doi:10.17660/ActaHortic.2017.1152.26.
  • Hespeler SC, Nemati H, Dehghan-Niri E. Non-destructive thermal imaging for object detection via advanced deep learning for robotic inspection and harvesting of chili peppers. Artif Intell Agric. 2021;5:102–117. doi:10.1016/j.aiia.2021.05.003.
  • Bakshi P, Wali VK, Sharma A, et al. Maturity indices of Guava. In: Determination of quality and harvest maturity for commercially grown fruit crops in Jammu Sub-tropics; 2015;(January 2014). p. 1–18.
  • Zeng X, Miao Y, Ubaid S, et al. Detection and classification of bruises of pears based on thermal images. Postharvest Biol Technol. 2020;161:111090. doi:10.1016/j.postharvbio.2019.111090.
  • Dong Y, Huang Y, Xu B, et al. (2022 undefined). Bruise detection and classification in jujube using thermal imaging and DenseNet. Wiley Online Library. Accessed July 23, 2022. https://onlinelibrary.wiley.com/doi/abs/10.1111jfpe.13981.

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