Sensor Deployed Agricultural Land for E-Commerce Platform withClustering of Nodes in the Network

A sensor-deployed agricultural land for an e-commerce platform integrates IoT sensors and wireless networks to monitor crop health, soil conditions, and environmental factors in real-time. These sensors collect data on moisture levels, temperature, humidity, and nutrient content, which is transmitted to a centralized platform. Through clustering of sensor nodes within the network, efficient data aggregation and communication are achieved, reducing energy consumption and improving network performance. This paper presents a comprehensive analysis of agricultural product e-commerce leveraging advanced data analysis techniques. By employing methods such as word frequency analysis, sentiment analysis, and clustering algorithms, we explore the multifaceted landscape of online agricultural markets. This study investigates the prevalence of key themes and trends in agricultural e-commerce, ranging from product categories to digital marketing strategies. Through sentiment analysis, we uncover nuanced patterns in consumer sentiment, providing insights into effective market positioning strategies. Our findings reveal that "agricultural products" emerge as the most prevalent theme, accounting for 8.5% of the dataset. Additionally, sentiment analysis indicates a positive sentiment towards e-commerce platforms, with a precision of 0.85 and an F1-score of 0.83. Clustering analysis delineates five distinct clusters, each representing different facets of agricultural e-commerce, with Cluster 1 focusing on agricultural products and Cluster 2 on digital marketing strategies.