Detection of Cucumber Fruit on Plant Image Using Artificial Neural Network

Document Type : Research Article-en

Authors

• D. Mohammad Zamani ¹
• S. M. Javidan ²
• M. Zand ³
• M. Rasouli ⁴

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

https://doi.org/10.22067/jam.2022.73827.1077

Abstract

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.

Keywords

• Artificial Neural Network
• Detection
• Greenhouse cucumber
• Image processing

20.1001.1.22286829.1402.13.1.3.0

Main Subjects

• Image Processing

©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.

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