2021
DOI: 10.47992/ijaeml.2581.7000.0112
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A Literature Review of the Detection and Categorization of various Arecanut Diseases using Image Processing and Machine Learning Approaches

Abstract: Background/Purpose: Every scholarly research project starts with a survey of the literature, which acts as a springboard for new ideas. The purpose of this literature review is to become familiar with the study domain and to assess the work's credibility. It also improves with the subject's integration and summary. This article briefly discusses the detection of disease and classification to achieve the objectives of the study. Objective: The main objective of this literature survey is to explore the different… Show more

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Cited by 15 publications
(8 citation statements)
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“…The moisture is checked when it increases to 800 and thetemperature. The plant can survive for another few days without water if the temperature falls below the threshold value [25][26] [27]. If it exceeds the cutoff point, irrigation water must be applied to the plant [28][29].…”
Section: Decision Making and Analysismentioning
confidence: 99%
“…The moisture is checked when it increases to 800 and thetemperature. The plant can survive for another few days without water if the temperature falls below the threshold value [25][26] [27]. If it exceeds the cutoff point, irrigation water must be applied to the plant [28][29].…”
Section: Decision Making and Analysismentioning
confidence: 99%
“…Within this fertile ground, deep learning emerges as a potent tool, revolutionizing tasks once solely reliant on human expertise (Santos et al, 2020). This survey delves into the exciting realm of deep learning applications in arecanut image analysis, a pivotal domain holding immense potential for the arecanut industry (Puneeth B. R et al, 2021).…”
Section: Introductionmentioning
confidence: 99%
“…Puneeth and Nethravathi [1] focused on diseases in arecanut through the classification of healthy and unhealthy arecanuts using image processing techniques. Dhanuja and Kumar [2] proposed on building a fully automated image classification system for the disease detection on multiple arecanut by using algorithms at all detection stages.…”
Section: Introductionmentioning
confidence: 99%