Journal of Innovative Image Processing
IRO Journals
Volume 1 — Issue 2 — December 2019

RGBD ANALYSIS FOR FINDING THE DIFFERENT STAGES OF MATURITY OF FRUITS IN FARMING

https://doi.org/10.36548/jiip.2019.2.006
Abraham Chandy
Professor, Karunya University, Coimbatore, India
PDF
PDF

How to Cite

Chandy, Abraham. 2019. “RGBD ANALYSIS FOR FINDING THE DIFFERENT STAGES OF MATURITY OF FRUITS IN FARMING”. Journal of Innovative Image Processing 1 (2): 111-21. https://doi.org/10.36548/jiip.2019.2.006.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— Fruit Farming
— Maturity Stage
— Image Processing
— Computer Vision
— Machine Learning Approaches
— RGBD Analysis
Published: 31-12-2019

Abstract

The agriculture being the major source of revenue for the people in the rural areas in a country like India many measures have been taken to improve the revenue of the farmers. The assessing the ripeness of the fruits is also one of the primary responsibilities of the farmer to fix the proper time to glean the fruits and taking them to the market. Computer vision and the image processing together remains as a key methodology in reducing the human labor and classifying the different stages of maturity is has become very prominent in examining the stages of the fruit growth. As the color seems to be the important feature to be observed in determining the different stages of the fruits maturity and color identification of the tropic fruits is difficult due to the various illuminations and the partial clogging up of the fruits. In order to identify maturity stage of the fruits the paper proposes a RGBD analysis to examine the stages of the fruits and identify the various stages of maturity.

References

  1. Raj, Jennifer S., and J. Vijitha Ananthi. "AUTOMATION USING IOT IN GREENHOUSE ENVIRONMENT." Journal of Information Technology 1, no. 01 (2019): 38-47.
  2. Jimenez, A. R., R. Ceres, and Jose L. Pons. "A survey of computer vision methods for locating fruit on trees." Transactions of the ASAE 43, no. 6 (2000): 1911.
  3. Vijayakumar, T. "COMPARATIVE STUDY OF CAPSULE NEURAL NETWORK IN VARIOUS APPLICATIONS." Journal of Artificial Intelligence 1, no. 01 (2019): 19-27.
  4. Rahimunnisa, K. "HYBRIDIZED GENETIC-SIMULATED ANNEALING ALGORITHM FOR PERFORMANCE OPTIMIZATION IN WIRELESS ADHOC NETWORK." Journal of Soft Computing Paradigm (JSCP) 1, no. 01 (2019): 1-13.
  5. Jacob, I. Jeena. "CAPSULE NETWORK BASED BIOMETRIC RECOGNITION SYSTEM." Journal of Artificial Intelligence 1, no. 02 (2019): 83-94.
  6. Koresh, Mr H. James Deva. "COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT." Journal of Innovative Image Processing (JIIP) 1, no. 01 (2019): 11-19.
  7. Bashar, Abul. "SURVEY ON EVOLVING DEEP LEARNING NEURAL NETWORK ARCHITECTURES." Journal of Artificial Intelligence 1, no. 02 (2019): 73-82.
  8. Anguraj, Dinesh Kumar, and S. Smys. "Trust-based intrusion detection and clustering approach for wireless body area networks." Wireless Personal Communications 104, no. 1 (2019): 1-20.
  9. Sathesh, A. "PERFORMANCE ANALYSIS OF GRANULAR COMPUTING MODEL IN SOFT COMPUTING PARADIGM FOR MONITORING OF FETAL ECHOCARDIOGRAPHY." Journal of Soft Computing Paradigm (JSCP) 1, no. 01 (2019): 14-23.
  10. Raj, Jennifer S., and J. Vijitha Ananthi. "RECURRENT NEURAL NETWORKS AND NONLINEAR PREDICTION IN SUPPORT VECTOR MACHINES." Journal of Soft Computing Paradigm (JSCP) 1, no. 01 (2019): 33-40.
  11. Joseph, S. Iwin Thanakumar. "SURVEY OF DATA MINING ALGORITHM’S FOR INTELLIGENT COMPUTING SYSTEM." Journal of trends in Computer Science and Smart technology (TCSST) 1, no. 01 (2019): 14-24.
  12. Raj, Jennifer S. "A COMPREHENSIVE SURVEY ON THE COMPUTATIONAL INTELLIGENCE TECHNIQUES AND ITS APPLICATIONS." Journal of ISMAC 1, no. 03 (2019): 147-159.
  13. Li, Bairong, Yan Long, and Huaibo Song. "Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting." International Journal of Agricultural and Biological Engineering 11, no. 1 (2018): 192-198.
  14. Tan, Kezhu, Won Suk Lee, Hao Gan, and Shuwen Wang. "Recognising blueberry fruit of different maturity using histogram oriented gradients and colour features in outdoor scenes." Biosystems engineering 176 (2018): 59-72.
  15. UluiŞik, Selman, Fikret Yildiz, and Ahmet Turan Özdemİr. "Image processing based machine vision system for tomato volume estimation." In 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), pp. 1-4. IEEE, 2018.
  16. Taofik, A., N. Ismail, Y. A. Gerhana, K. Komarujaman, and Muhammad Ali Ramdhani. "Design of Smart System to Detect Ripeness of Tomato and Chili with New Approach in Data Acquisition." In IOP Conference Series: Materials Science and Engineering, vol. 288, no. 1, p. 012018. IOP Publishing, 2018.
  17. Pereira, Luiz Fernando Santos, Sylvio Barbon Jr, Nektarios A. Valous, and Douglas Fernandes Barbin. "Predicting the ripening of papaya fruit with digital imaging and random forests." Computers and electronics in agriculture 145 (2018): 76-82.
  18. Wan, Peng, Arash Toudeshki, Hequn Tan, and Reza Ehsani. "A methodology for fresh tomato maturity detection using computer vision." Computers and Electronics in Agriculture 146 (2018): 43-50.
  19. Zhang, Yan, Jian Lian, Mingqu Fan, and Yuanjie Zheng. "Deep indicator for fine-grained classification of banana’s ripening stages." EURASIP Journal on Image and Video Processing 2018, no. 1 (2018): 1-10.
  20. Kumar, Arun, Vijay S. Rajpurohit, and Bhairu J. Jirage. "Pomegranate fruit quality assessment using machine intelligence and wavelet features." Journal of Horticultural Research 26, no. 1 (2018): 53-60.
  21. Mim, Farjana Sultana, Syed Md Galib, Md Fakhrul Hasan, and Sanjida Akther Jerin. "Automatic detection of mango ripening stages–An application of information technology to botany." Scientia Horticulturae 237 (2018): 156-163.
  22. Tu, Shuqin, Yueju Xue, Chan Zheng, Yu Qi, Hua Wan, and Liang Mao. "Detection of passion fruits and maturity classification using Red-Green-Blue Depth images." Biosystems engineering 175 (2018): 156-167.