Areca Nut Disease Detection using Image Processing Technology

Dhanuja, K C; P, Mohan Kumar H

doi:10.17577/ijertv9is080352

Cited by 10 publications

(4 citation statements)

References 1 publication

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“…Tariqul et al [11] (2020) had introduced a system that utilized image processing and CNN to increase crop production and alleviate plant disease and insect attraction. The model is trained with the dataset to achieve an accuracy rate of 94.29%.…”

Section: Literature Surveymentioning

confidence: 99%

An Accurate Plant Disease Detection Technique Using Machine Learning

Raman

2024

EAI Endorsed Trans IoT

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INTRODUCTION: Plant diseases pose a significant threat to agriculture, causing substantial crop and financial losses. Modern technologies enable precise monitoring of plant health and early disease identification. Employing image processing, particularly Convolutional Neural Network (CNN) techniques, allows accurate prediction of plant diseases. The aim is to provide an automated, reliable disease detection system, aiding professionals and farmers in timely action to prevent infections and reduce crop losses. Integrating cutting-edge technologies in agriculture holds vast potential to enhance profitability and production. OBJECTIVES: The primary focus lies in developing an automated system proficient in analysing plant images to detect disease symptoms and classify plants as healthy or disease affected. The system aims to simplify plant disease diagnostics for farmers, providing essential information about leaf name, integrity, and life span. METHODS: The method aims to empower farmers by enabling easy identification of plant diseases, providing essential details like disease name, accuracy level, and life span. The CNN model accurately gauges the system's accuracy level. It further streamlines the process by offering a unified solution through a user-friendly web application, eliminating the need for separate interventions for affected leaves. the system saves farmers time by delivering crucial information directly. RESULTS: The Proposed web application proves to be a comprehensive solution, eliminating the need for farmers to search for separate interventions for affected leaves. The machine learning model exhibits a noteworthy accuracy of 96.67%, emphasizing its proficiency in making correct predictions for the given task. CONCLUSION: In conclusion, the paper successfully employed a CNN algorithm for precise plant disease prediction. With the proposed model deployment, farmers can easily access information about plant diseases, their life span, and preventive measures through the web application. By detecting illnesses early, farmers can promptly take remedial actions to mitigate sicknesses and minimize crop losses. The integrated approach holds promise for advancing agricultural practices and ensuring sustainable crop management.

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Section: Literature Surveymentioning

confidence: 99%

An Accurate Plant Disease Detection Technique Using Machine Learning

Raman

2024

EAI Endorsed Trans IoT

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“…In [12], arecanut disease detection using image processing technology. The key focus is on the classification of arecanut based on texture features, with the use of various image processing techniques such as Wavelet, Gabor, Local binary (LBP), Gray Level Difference Matrix (GLDM), and Gray Level CoOccurrence Matrix (GLCM) for feature extraction.…”

Section: Literature Surveymentioning

confidence: 99%

Review on Detection and Prediction of Diseases in Arecanut Trees

R N

2024

IJSREM

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In this study, we propose a method for detecting and predicting diseases in arecanut plants using image processing. The proposed method consists of three main steps: image acquisition, image segmentation, and disease detection and prediction. The performance of the proposed method is evaluated using a dataset of arecanut leaf images with various diseases. The results show that the proposed method can accurately detect and predict the presence of diseases in the arecanut plants with high precision and recall rates. Keywords: Arecanut, Machine learning, Convolutional neural networks.

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“…Model/method Accuracy Dhanuja and Kumar [2] Nearest neighbors (NN) 90.9% Anilkumar et al [4] Convolutional neural network (CNN) 88.46% Akshay and Hegde [5] Decision tree 90% Balipa et al [28] Support vector machines (SVM) 75% Zamani et al [29] Random forest 92% Nanthakumar et al [30] CNN-long short-term memory (LSTM) 92.55% Proposed work…”

Section: Referencementioning

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. Siddesha and Niranjan [3] presented detection of affected region from an infected arecanut image using K-means clustering and Otsu method.…”

Section: Introductionmentioning

confidence: 99%

Identification and categorization of diseases in arecanut: a machine learning approach

Hegde

Sadanand

Hegde

et al. 2023

IJEECS

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Arecanut is one of the prominent commercial crops that are grown worldwide for traditional medicines, furniture, cosmetics, food, veterinary preparations, and textile industries. It experiences a variety of diseases during its existence, from the bottom to the tip. The conventional method for detection of diseases is through visual inspection and it is also necessary to have properly designed laboratories to check these harvests. It is a time consuming and tedious task to inspect these crops across wide acres of plantations. The proposed system has been developed that uses convolutional neural network (CNN) to identify and categorize diseases in arecanuts, trunks and leaves also suggesting effective preventative measures. Proprietary dataset consists of 1,100 photos of healthy and diseased arecas. The ratio between the train and test data is 80:20. Binary cross entropy is employed as the loss function for model construction, with accuracy serving as the metrics and Adam serving as the optimizing function. In identification and categorization of arecanut diseases, the suggested approach was shown to be efficient with 93.05% accuracy.

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Areca Nut Disease Detection using Image Processing Technology

Cited by 10 publications

References 1 publication

An Accurate Plant Disease Detection Technique Using Machine Learning

An Accurate Plant Disease Detection Technique Using Machine Learning

Review on Detection and Prediction of Diseases in Arecanut Trees

Identification and categorization of diseases in arecanut: a machine learning approach

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