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Disease spot image segmentation algorithm with memory-based fruit fly optimization algorithm

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Abstract

Considering that apples are susceptible to physiological diseases, an effective image segmentation method is a key point for the fruit diseases detection. It is necessary to develop a novel and effective segmentation algorithm for apple skin disease images. Aiming at the shortcomings of noise interference and over-segmentation of the maximum class variance (OTSU) method in image segmentation, an image segmentation algorithm with memory-based Fruit Fly Optimization algorithm and adaptive weighting factor were proposed and applied in the apple skin disease images. The iterative memory step size was applied to the updating process of the individual position of the flies after one-time optimization to achieve fast global convergence. Combining the improved algorithm with the classical Otsu method, the segmentation algorithm with memory-based fruit fly optimization algorithm was formed. The algorithm encodes and processes the apple skin disease images, selects the inter-class variance of the image as its fitness value, and then, the improved Otsu method was used to segment skin disease of apple images. The convergence of speed and accuracy of memory-based fruit fly optimization algorithm are significantly superior to the other six algorithms. Three types of apple skin disease images including black spot disease under strong illumination, black spot disease under medium illumination and bitterness disease under weak illumination conditions were selected for segmentation experiments. Compared with other region segmentation methods and edge segmentation methods, the results indicate that the Otsu’s segmentation algorithm with memory-based fruit fly optimization algorithm has much better effect in the segmentation experiments of apple skin disease images. The benchmark apple images test results show that the proposed method achieves better segmentation effect than other two segmentation methods. The results show that the proposed algorithm achieves better segmentation effect and higher performance stability.

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Data availability

Benchmark Function Comparison Experiment

In this simulation experiments, Twenty-four benchmark functions selected from literature [2, 42, 44] were adopted to perform minimum value optimization performance comparison tests on the above seven algorithms.

[28] Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis. Grey Wolf Optimizer [J]. Advances in Engineering Software, 2014, 69(3):46–61.

[29] Mousavirad S J, Ebrahimpour-Komleh H. Human mental search: a new population-based metaheuristic optimization algorithm [J]. Applied Intelligence, 2017.

[66] N. H. Awad, M. Z. Ali, J. J. Liang, B. Y. Qu, P. N. Suganthan. Problem Definitions and Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Bound Constrained Real-Parameter Numerical Optimization [R]. Technical Report, Nanyang Technological University, Singapore, November 2016.

Application experiment of apple skin disease image segmentation

Three types of apple epidermal disease images were randomly selected from the following three conditions for experiment, including black spot disease under strong illumination, black spot disease under medium illumination and bitterness disease under weak illumination.

The three apple images come from the international website, which URL is https://image.baidu.com/search/index?tn=baiduimage&ct=201326592&lm=-1&cl=2&ie=gb18030&word=%C6%BB%B9%FB%B2%A1%B0%DF%CD%BC%C6%AC&fr=ala&ala=1&alatpl=normal&pos=0&dyTabStr=MCwzLDIsNCw2LDUsMSw4LDcsOQ%3D%3D

Application experiment of benchmark image segmentation

Three apple images were randomly selected from fruit classification dataset based on Baidu easydl platform as benchmark images.

The three apple images come from the international website, which URL is https://pan.baidu.com/s/11SstydxQNXNNq9tk9kuyfg

In addition, all data generated or analysed during this study are included in this published article. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgement

This work was supported by the Gansu Provincial University Teacher Innovation Fund Project [grant number 2023A-051]; and, supported by Gansu Science and Technology Plan [grant number 20JR5RA032]; and, the Young Supervisor Fund of Gansu Agricultural University [grant number GAU-QDFC-2020-08].

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  1. College of Information Science and Technology, Gansu Agricultural University, Lanzhou, 730070, China

    Liqun Liu

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Liqun Liu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Liu, L. Disease spot image segmentation algorithm with memory-based fruit fly optimization algorithm. Multimed Tools Appl 82, 47135–47163 (2023). https://doi.org/10.1007/s11042-023-15630-4

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