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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63931
Volume:
10 |
5 |
May 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Information Technology
Mahendra Engineering College
Department of Information Technology
Mahendra Engineering College
Department of Information Technology
Mahendra Engineering College
Department of Information Technology
Mahendra Engineering College
Department of Information Technology
Mahendra Engineering College
The digital technology has grown rapidly and impacted how businesses operate. This trend gives rise to many opportunities for different types of businesses, including agriculture. There are many benefits of using online shopping platforms; these include enhanced convenience and accessibility to products as well as improved operational efficiency. However, many sectors within agriculture are struggling to implement e-commerce technology for their businesses. One of the main reasons is that traditional marketing channels rely heavily on third-party intermediaries (e.g., wholesalers, distributors, and retailers) and this reduces the farmer's profit margin and provides no direct access to the final end consumer. Farmers therefore do not get good price, while consumers pay higher prices because intermediaries between buyers and sellers have a negative impact on the price paid by the consumer, while providing very little visibility into where the products came from or what quality they are. To address these issues, AGROPRO is introduced to be an online digital marketplace for agricultural products and will allow farmers and consumers to interact directly and provide transparency and efficiency in agriculture. The proposed system integrates multiple artificial intelligence techniques to enhance functionality and security. A Random Forest (RF) based User Authentication Module will allow the secure verification of a user's identity during registration. A Deep Neural Network (DNN) will allow for intelligent categorization and inventory management of agricultural products uploaded to the AGROPRO system, allowing sellers to more efficiently organize and improve their reach within the marketplace based on availability of inventory. A Recurrent Neural Network (RNN) will allow for the analysis of user-generated data to understand user behaviour and predict order patterns to effectively process shopping carts and orders, providing users with empowered capabilities to browse, search, compare prices, and manage their purchases of agricultural products using an intuitive user interface. An auto-encounter based fraud detection model will be employed in the payment module to ensure that users will have secure, reliable, and safe monetary transactions that maintain the confidentiality of user payment details. The experimental results demonstrate that the AGROPRO system achieves high accuracy and precision in the areas of user authentication and security, thus exceeding previous performance methods of user authentication and security.
KEYWORDS
To summarize, the AGROPRO system addresses the shortcomings of conventional ways of marketing agricultural goods by giving farmers an open and direct digital marketplace to sell their program to consumers. The additional benefit AGROPRO provides to its farmers through machine learning, such as Random Forest for authentication, Deep Neural Networks for product management, and Recurrent Neural Networks for consumer behaviour modelling has improved the overall intelligence of the AGROPRO system and has improved operational efficiencies as well. Additionally, the inclusion of an autoencoder-based fraud detection mechanism will allow for much more secure and trusted transactions than any existing method by detecting transaction fraud before it even occurs. Preliminary experiment results show that AGROPRO performs significantly better than all other similar technologies in measuring authentication success rates 98.3%, measuring accuracy 97.8%, measuring precision 97.2% and measuring security 96(S).
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Publication Date
5/29/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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