Potato leaf disease detection using image processing

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Research Paper 05/06/2024
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Potato leaf disease detection using image processing

Rokon Uz Zaman, KU Ahmmad, Debasis Sarkar, MAA Mumin, N Salahin
Int. J. Agron. & Agric. Res. 24(6), 15-19, June 2024.
Copyright Statement: Copyright 2024; The Author(s).
License: CC BY-NC 4.0

Abstract

Agriculture is one of the most important pillars of Bangladesh’s economy. However, due to some factors such as plant diseases, pests, climate change, the yield of the farming industry decreases, and the productivity decreases as well. The detection of plant diseases is crucial to avert the losses in the productivity and in the yield. It is not obvious to monitor the plant diseases manually as the act of disease detection is very critical. It needs a huge effort, along with knowledge of plant diseases and extensive processing times. Therefore, image processing technology is used to detect the plant disease, this is done by capturing the input image that undergoes the process and is compared with the dataset. This dataset is composed of diverse diseases of potato leaves in the image format.  This study aims to build a web application to predict the diseases of potato plants that will help farmers to identify the diseases so that they can use appropriate fungicide to get more yields. The purpose of this study is to assist and provide efficient support to the potato farmers. In this study, we propose a system that will use the techniques of image process to both analyze and detect the plant diseases using machine learning Conventional Neural Networks (CNN) with Tensorflow framework 2. The results of the implementation show that the designed system could give a successful result by detecting and classifying the potato leaf diseases and healthy plant.

BBS. 2021. Report of the Share of economic sectors in the GDP in Bangladesh.

DAM. 2020. Report of the Department of Agricultural Marketing, Khamarbari, Dhaka,    Bangladesh.

FAOSTAT. 2020. New food balance sheet for Bangladesh. Food and Agriculture Organization of the United Nations. http://www.fao.org/faostat/en/#data/FBS.

GEOPOTATO. 2016-2019. Report of the GEOPOTATO. https://www.wur.nl/en/project/geopotato-control-fungal-disease-in-potato-in-bangladesh.htm

Iqbal MA,  Talukder KH.  2020. Detection of Potato Disease Using Image Segmentation and Machine Learning. International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), 43–47. DOI: 10.1109/WiSPNET48689.2020.9198563

Knaak C, Von Eßen J, Kröger M, Schulze F, Abels P,  Gillner A. 2021. A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low Power Embedded Computing Boards. Sensors 21(12), 4205, DOI: 10.3390/s21124205.

Shrivastava G, Patidar H. 2022. Rice Plant Disease Identification Decision Support Model Using Machine Learning. Ictact Journal on Soft Computing 12(3), 2619-2627. DOI: 10.21917/ijsc.2022.0375

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