Abstract: Areca nut, sometimes called betel nut, is a tropical crop. Areca nuts are produced and consumed in India, which is the world's second-largest producer and consumer. It suffers from a range of ailments during its life cycle. Farmers use their senses of sight to detect disease. Multiple image processing techniques for categorization of Areca nuts with various properties such as colour, texture are examined in this paper. Computer detection systems have been widely employed in the real world for retrieval tests because they can provide rapid, efficient, accurate, and clear testing. Until now, areca nut separation has been done by hand. Areca nut separation employing a complex colour sorting mechanism is made up of a variety of exterior properties of the nut, such as colour, texture, form, and size. Various approaches will be used to extract information from the captured image. The areca nut's colour can also be used to classify it. To efficiently plan the areca nut, all of these traits are essential. To extract composition characteristics, the co-occurrence matrix of wavelet coefficients at the second level of details, as well as contour let coefficients, is used. Separation is accomplished by mixing three texture elements namely strength, contrast, and homogeneity. This approach has been shown to work effectively in the contour let domain, allowing for a reduction in the vector dimension feature. When given training data, it categorises the data into healthy and unhealthy areca nuts based on colour and quality. Areca nut is separated using CNN and SVM classifiers.
Keywords: Convolution Neural Network (CNN), Support Vector Machine(SVM), Machine Learning (ML), Artificial Intelligence (AI), K-Nearest Neigbor (KNN), Artificial Neural Network(ANN), Gray Level Co-occurrence Matrix (GLCM).
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DOI:
10.17148/IJARCCE.2022.11571
