International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 05 | May 2024
p-ISSN: 2395-0072
www.irjet.net
A Review of the Literature on The Implementation of Image Processing and Machine Learning Techniques for The Detection and Classification of Different Areca nut Diseases Tejaswi R1, Rajesh M Mysoremath2, Pradhan D Prabhu3, Honey Jain4, Prof. Mahitha G5 1,2,3,4Student Department of Computer Science and Engineering PES University Bangalore, India 5Professor, Department of Computer Science and Engineering PES University Bangalore, India
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Abstract - The Areca nut tree is a type of straight-trunk tree that can grow up to 30 meters tall and 25 to 40 centimeters in
diameter. The normal life expectancy is up to 60 years, with some species living up to 100 years. It is believed that Sulawesi (Celebes), Indonesia, Malaysia, and New Guinea are the natural habitats of this plant. Its range encompasses areas of East Africa, the Pacific, and the tropics in Asia. The areca nut goes by various names, including catechu, betel palm nut, and Areca nut. This plant can adapt to creeks, wetlands, and the borders of swamp forests. Almost every part of this plant is commercially valuable for humans. In terms of plant morphology, the pinnate leaves of areca nuts range in length from one to 1.5 meters. The midrib leaf's base is crown-shaped and gray in color. Branched flower stalks emerge from beneath the crown stalk and extend to a maximum length of one meter. The fruit has red or orange seeds that are 4-5 cm wide and 5-6 cm long, and it might be round or somewhat flat in shape. Pinang belongs to the Order Arecales, Family Ericaceae, Division Spermatophyta, Monocotyledon class, and Genus Areca in taxonomy. Key Words: Arecanut diseases, Multi-gradient images, ResNet model, Convolutional Neural Networks (CNNs), Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), Grey-Level Co-occurrence Matrix (GLCM), Fruit rot disease, Grey-Level Difference Matrix (GLDM), Support Vector Machine Regression (SVMR), Random Forest Classifier (RFC), Multilayer perceptron regression, Random Forest Regression (RFR), Support Vector Regression (SVR), Gated Recurrent Unit (GRU).
1.INTRODUCTION Arecanut is a crop that is widely cultivated in India. Karnataka, Kerala, and Assam are the major states that produce Arecanut in India. Karnataka produces the largest quantity of Arecanut in India, with a total cultivation area of 218,010 hectares and a production of 457.560 tones. Arecanut is mainly grown in the southern and coastal districts of India under assured irrigation. The crop thrives well in areas with a temperature range of 20-34°C and an annual rainfall of 2000-5000 mm. The crop is grown as a garden crop and is usually intercropped with coconut, cocoa, pepper, and other crops. The crop is used in various forms, such as raw, boiled, or roasted, and is consumed as a mouth freshener. It is also used in Ayurvedic medicine for its medicinal properties. Since it has several importance in India and it is also a major commercial crop there are some challenges to cultivate this crop. In that challenge occurrence of Disease is a major challenge due to changes in temperature and climatic conditions.
2. LITRATURE SURVEY B. Mallikarjuna et al. [5], the authors employ a novel approach using multi-gradient images to identify diseases in Areca nut fruit. These multi-gradient images undergo augmentation and are converted into arrays before being fed into a ResNet (Residual Network) model for both training and testing. The ResNet model, renowned for its efficiency and popularity in image processing, boasts up to 152 layers, enabling effective feature extraction and representation. Notably, the ResNet architecture addresses the vanishing gradient problem, enhancing its suitability for complex tasks like disease identification. The proposed model achieves an accuracy of 80.02% for normal images and 82% for multi-gradient-direction images, demonstrating its efficacy in Areca nut disease detection. Meghana D R et al. [6], the authors propose a method for identifying diseases in Areca nut plants using Convolutional Neural Networks (CNNs). CNNs are deep learning systems specifically designed for image recognition tasks. The authors created a dataset consisting of 620 images depicting healthy and diseased Areca nuts, which were divided into training and testing sets. Their model was developed using accuracy as the evaluation metric, Adam as the optimizer function, and categorical crossentropy as the loss function. During the training process conducted over 50 epochs, the model aimed to achieve high validation © 2024, IRJET
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