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Intelligent Mango Canopies Yield Estimation Using Machine Vision

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Abstract

The use of technology in agriculture has grown imperative. To provide the expanding population's needs for food, agricultural productivity should rise. Computer vision technology has been used to solve the difficulties associated with manual yield estimation. This article present efficient mango fruit yield estimation system with color based pixel classification method with support to that a benchmark mango tree dataset is presented. Dataset is collected temporally under varying illumination conditions, distance and time for 5 months from its blossoming phase to the ripen phase of the fruit. The repository accounts for 21,000 images of mango trees. The proposed work initially preprocess the RGB image by converting into grayscale, HSV and YCbCr color models, each layers of color model are separately extracted and each layer is enhanced by applying techniques like Gaussian blur, histogram equalization to study the features and superiority of the mango images and best color layer which exhibits most dominant features are selected for next level processing. Further, proposed a two stage algorithm using color features to classify the pixels of mango fruit region. Finally, after fruit pixel classification the method is followed by mango fruit detection using Hough transform circle fitting technique. The proposed method could count up to 80% of mango fruits present in the image. This work offers specialized help for the visual recognizable proof and yield estimation of mango fruits and also for other fruits available in the environment.

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Data availability

The data that support the findings of this study are available from [M V Neethi] but restrictions apply to the availability of these data, which were captured personally for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [M V Neethi].

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Malnad College of Engineering, Hassan, Karnataka, India

    M. V. Neethi & A. Geetha Kiran

  2. Center of Incubation, Innovation, Research and Consultancy (CIIRC), Jyothy Institute of Technology, Bengaluru, Karnataka, India

    Harshvardhan Tiwari

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  1. M. V. Neethi
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  2. A. Geetha Kiran
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  3. Harshvardhan Tiwari
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Correspondence to M. V. Neethi.

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For the writers, there are no apparent conflicts of interest. Each co-author has examined and approved the manuscript's content, and none of the authors have any competing financial interests to mention.

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This article is part of the topical collection “Advances in Computational Intelligence for Artificial Intelligence, Machine Learning, Internet of Things and Data Analytics” guest edited by S. Meenakshi Sundaram, Young Lee and Gururaj K S.

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Neethi, M.V., Kiran, A.G. & Tiwari, H. Intelligent Mango Canopies Yield Estimation Using Machine Vision. SN COMPUT. SCI. 4, 171 (2023). https://doi.org/10.1007/s42979-022-01602-2

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