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Mango leaf disease recognition using neural network and support vector machine

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Abstract

The mango tree is affected by different diseases and it is very difficult to detect disease in naked eye. This paper presents a neural network ensemble (NNE) for mango leaf disease recognition (MLDR) that help to identify diseases easily and correctly instead of traditional system. This study intends to detect the diseases of mango leaf with machine learning monitoring different symptoms of leaves. Here, trained data are produced by classification technique collecting images of leaves that were various disease affected. A machine learning system is designed to identify the symptom of mangoes’ leaf diseases automatically uploading and matching new images of affected leaf with trained data. The proposed system could successfully detect and classify the examined disease with average accuracy of 80%. This proposed solution would clinch the mango plants. The system will help to detect disease without the presence of agriculturist that would save time to identify disease with machine instead of manual system. It would also ease to treat the affected mango leaf disease properly, increase the production of mango, and meet the demand of global market.

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Authors and Affiliations

  1. Department of CSE, Gono Bishwabidyalay, Dhaka, Bangladesh

    Md. Rasel Mia, Sujit Roy & Md. Atikur Rahman

  2. Department of CSE, Jatiya Kabi Kazi Nazrul Islam University, Dhaka, Bangladesh

    Sujit Roy & Subrata Kumar Das

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  1. Md. Rasel Mia
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  2. Sujit Roy
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  4. Md. Atikur Rahman
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Correspondence to Subrata Kumar Das.

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Mia, M.R., Roy, S., Das, S.K. et al. Mango leaf disease recognition using neural network and support vector machine. Iran J Comput Sci 3, 185–193 (2020). https://doi.org/10.1007/s42044-020-00057-z

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