Universal Journal of Agricultural Research Vol. 11(1), pp. 98 - 109
DOI: 10.13189/ujar.2023.110109
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Agronomy with IoT Devices: The Smart Solution for Detection of Diseases of Betel Leaves

Indranil Sarkar ¹, Sunil Karforma ², Priyanka Bhattacharya ³, Rajesh Bose ¹, Sandip Roy ^1,*
1 Department of Computational Science, Brainware University, West Bengal, India
² Department of Computer Science, Burdwan University, West Bengal, India
³ Department of Computer Science & Engineering, Academy of Technology, West Bengal, India

ABSTRACT

The growth of the entire population in the entire world and scarcity of food crops are the most challenging concept nowadays and for solving these challenges, new mechanization like artificial intelligence, Internet of Things and the mobile internet are used for solving this above-mentioned real- life problem. From 2019 onwards, a current perspective regarding Intelligent Agronomy has been focused on. For this smart system, Internet of Thing (IoT) is the column pillar because sensor devices are being connected to perform various basic tasks. Different sensors are used in the smart irrigation system which is based on smart controllers and detectors for vigilance of water line, watering efficacy and environment. Automatic leaf disease detection is also a very important concept for monitoring the growth of food crops and other plant leaves with medicinal value. This system also detects the symptoms of the disease in the plant leaves automatically. In the proposed decision- making system the authors have collected a set of betel leaf images and have utilized image content characterization and Support Vector Machine (SVM) classifier. Different stages like initialization, bifurcation, eradication of characteristics and orderly arrangement have been involved in image processing mechanisms for analyzing decisions. At the time of processing, an input image is being rescaled as per requirement. Authors of this research paper have eliminated color and surface characteristics from input image sets for classification and training. The proposed system will be able to analyze the test images automatically for making decisions about the leaf whether it is abnormal or good.

KEYWORDS
Internet of Things (IoT), Smart Agronomy, Support Vector Machine (SVM) Classifier, Python 3.9, Betel Leaves

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Indranil Sarkar , Sunil Karforma , Priyanka Bhattacharya , Rajesh Bose , Sandip Roy , "Agronomy with IoT Devices: The Smart Solution for Detection of Diseases of Betel Leaves," Universal Journal of Agricultural Research, Vol. 11, No. 1, pp. 98 - 109, 2023. DOI: 10.13189/ujar.2023.110109.

(b). APA Format:
Indranil Sarkar , Sunil Karforma , Priyanka Bhattacharya , Rajesh Bose , Sandip Roy (2023). Agronomy with IoT Devices: The Smart Solution for Detection of Diseases of Betel Leaves. Universal Journal of Agricultural Research, 11(1), 98 - 109. DOI: 10.13189/ujar.2023.110109.