An Intelligent Business-to-Consumer Ecommerce Framework for Direct Agricultural Product Distribution

Abstract

The rising demand for fresh, locally sourced agricultural products, along with the need for efficient distribution approaches, has prompted interest in direct-to-consumer ecommerce models within the agriculture industry. Existing agricultural distribution systems frequently incorporate intermediaries, resulting in inefficiencies, inflated costs and delays in the delivery of perishable goods. The development of advanced technologies, including artificial intelligence (AI), has facilitated the development of more efficient, transparent, and consumer-oriented agricultural distribution systems. This paper outlines the framework of an intelligent business-to-consumer ecommerce platform for the direct distribution of agricultural products using machine learning to improve shipments, transparency and user experience. The proposed framework analyzes key factors such as price, freshness, and shipping conditions to deliver customized product recommendations and improve shipment assignment. The intelligent product recommendation and shipment assignment ensures user preference as well as the freshness of perishable goods while reducing delays and transportation expenses. Also, the proposed framework comprises of overall conceptual framework including, data collection and preprocessing steps, feature extraction, the use of recurrent neural network and singular value decomposition to train data points and evaluation metrics such as RMSA, MAE, ranking quality and cold start testing to validate intelligent model efficiency. Also, it facilitates interaction between consumers and producers, promoting a transparent, efficient, and economical distribution process. The intelligent model continuously adjusts to customer preferences and market dynamics, improving efficiency in operation and user satisfaction.