with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Document Type : Research Article-en


1 Department of Biosystems Engineering, Takestan Branch, Islamic Azad University, Takestan, Iran

2 Department of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran

3 MS.c. graduated, Department of Biosystems Engineering, Takestan Branch, Islamic Azad University, Takestan, Iran

4 Department of Plant Breeding, Takestan Branch, Islamic Azad University, Takestan, Iran


The main purpose of this study was to provide a method for accurately identifying the position of cucumber fruit in digital images of the greenhouse cucumber plant. After balancing the brightness histogram of the desired image, it multiplies the image with a window containing the image of a cucumber fruit, which causes larger coefficients to be obtained in areas with suspected cucumber. By extracting these local maximums, clusters of initial points are obtained as possible windows of cucumber existence. Then, in order to accurately detect the location of the cucumbers, these points and areas around them are referred to a neural network that has been trained using a number of images including cucumber images, non-cucumber images and their optimal responses. The proposed method was implemented in the Simulink toolbox of MATLAB software. The proposed method was then simulated using this network structure and tested on 120 images obtained from a greenhouse by a digital camera. The areas obtained from this network led to the accurate detection of the location of the cucumbers in the image. The proposed method was then simulated and tested on 120 images. The proposed method had a low error and was able to detect high levels of cucumber fruit in the images. This detection took an average of 5.12 seconds for each image. The accuracy of the network in correctly identifying the position of the cucumber fruit in the images was 95.3%. This method had low error and was able to detect a high rate at a good time of cucumber fruits in discover images.


Main Subjects

©2022 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

  1. Bao, G., Cai, S., Qi, L., Xun, Y., Zhang, L., & Yang, Q. (2016). Multi-template matching algorithm for cucumber recognition in natural environment. Computers and Electronics in Agriculture, 127, 754-762. https://doi.org/10.1016/j.compag.2016.08.001
  2. Fernandes, S., & Bala, J. (2015). Study on MACE Gabor Filters, Gabor Wavelets, DCT-Neural Network, Hybrid Spatial Feature Interdependence Matrix, Fusion Techniques for Face Recognition. Recent Patents on Engineering, 9(1), 29-36. https://doi.org/10.2174/2210686303666131118220632
  3. Hayashi, S., Ganno, K., Ishii, Y., & Tanaka, I. (2002). Robotic Harvesting System for Eggplants. Japan Agricultural Research Quarterly: JARQ, 36(3), 163-168. https://doi.org/10.6090/jarq.36.163
  4. Li, D., Zhao, H., Zhao, X., Gao, Q., & Xu, L. (2017). Cucumber detection based on texture and color in greenhouse. International Journal of Pattern Recognition and Artificial Intelligence, 31(08), 1754016. https://doi.org/10.1142/S0218001417540167
  5. Li, P., Lee, S., & Hsu, H. Y. (2011). Review on fruit harvesting method for potential use of automatic fruit harvesting systems. Procedia Engineering, 23, 351-366. https://doi.org/10.1016/j.proeng.2011.11.2514
  6. Li, Z., Miao, F., Yang, Z., & Wang, H. (2019). An anthropometric study for the anthropomorphic design of tomato-harvesting robots. Computers and Electronics in Agriculture, 163, 104881. https://doi.org/10.1016/j.compag.2019.104881
  7. Lin, G., Tang, Y., Zou, X., Xiong, J., & Fang, Y. (2019). Color, depth, and shape-based 3D fruit detection. Precision Agriculture. https://doi.org/10.1007/s11119-019-09654-w
  8. Liu, X., Zhao, D., Jia, W., Ji, W., Ruan, C., & Sun, Y. (2019). Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation. IEEE Access, 7, 139635-139642. https://doi:10.1109/access.2019.2942144
  9. Ma, J., Du, K., Zheng, F., Zhang, L., Gong, Z., & Sun, Z. (2018). A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Computers and Electronics in Agriculture, 154, 18-24. https://doi.org/10.1016/j.compag.2018.08.048
  10. Mohamadzamani, D., Sajadian, S., & Javidan, S. M. (2020). DDetection of Callosobruchus maculatus with image processing and artificial neural network. Applied Entomology and Phytopathology, 88(1), 103-112. https://doi.org/10.22092/jaep.2020.341684.1324
  11. Vakilian, K. A., & Massah, J. (2016). An apple grading system according to European fruit quality standards using Gabor filter and artificial neural networks. Scientific Study & Research. Chemistry & Chemical Engineering, Biotechnology, Food Industry, 17(1), 75.
  12. Yuan, T., Li, W., Feng, Q., & Zhang. J. (2010). Spectral Imaging for Greenhouse Cucumber Fruit Detection Based on Binocular Stereovision. 2010 Pittsburgh, Pennsylvania. https://doi.org/10.13031/2013.29858
  13. T., Chen-guang, X., Yong-xin, R., Qing-chun, F., Yu-zhi, T., & Wei, L. (2008). Detecting the Information of Cucumber in Greenhouse for Picking Based on NIR Image. College of Engineering China Agricultural University Beijing 100083.
  14. Zhao, Y., Gong, L., Huang, Y., & Liu, C. (2016). A review of key techniques of vision-based control for harvesting robot. Computers and Electronics in Agriculture, 127, 311-323. https://doi.org/10.1016/j.compag.2016.06.022