AI BASED ARECANUT CLASSIFICATION SYSTEM

Arecanut, also known as betel nut or supari, is a widely cultivated crop with significant economic importance in many tropical regions. The quality assessment and classification of arecanut are crucial tasks in the agricultural sector, impacting both production efficiency and market value. Traditional methods of classification often rely on subjective human judgment and are prone to inconsistency and inefficiency. In this paper, we propose an AI-based Arecanut Classification System (AACS) that leverages advanced machine learning techniques to automate the classification process. The system employs state-of-the-art image processing and deep learning algorithms to analyze images of arecanut samples and classify them into predefined quality grades. The AACS consists of several key components, including image preprocessing, feature extraction, and classification. Image preprocessing techniques are applied to enhance the quality and uniformity of input images, followed by feature extraction to capture relevant characteristics of arecanut samples. Deep learning models, such as convolutional neural networks (CNNs), are then employed for classification, learning discriminative patterns from the extracted features to accurately assign quality grades to the samples. To evaluate the performance of the proposed system, extensive experiments are conducted using a dataset of labeled arecanut images representing different quality grades. The results demonstrate the effectiveness of the AACS in accurately classifying arecanut samples, achieving high classification accuracy compared to traditional methods. The AACS offers several advantages over traditional classification approaches, including automation, consistency, and scalability. By automating the classification process, the system reduces reliance on human expertise and minimizes errors, thereby improving efficiency and productivity in the agricultural sector. Additionally, the scalability of the system allows for seamless integration into existing production workflows, facilitating widespread adoption in agricultural communities.