Intelligent Farming Assistant for Optimized Crop Management and Farmer Consumer Connectivity

The Intelligent Farming Assistant promises to be the ultimate farming operation tool that leverages the power of both complex computational technology and actual. The platform collects data and using machine learning solutions such as Random Forests, Gradient Boosting, as well as Neural Networks offers farmers recommendations regarding optimal planting times and types, resource use, or protection against pests and diseases. It also provides the latest weather information and reports on soil health that enable the farmers to make the right decision which improves production. What is more, stands out from other systems in its capacity to base its recommendations on the current data to provide a farmer with more real-time analysis of his or her circumstances. Moreover, unlike most other platforms designed to assist farmers in managing their crops, the platform also enables them to sell them directly to consumers, meaning fewer intermediaries and a wider client base. The easy-to-navigate design also means that farmers get the information they need in the format of their choice regardless of the device they are using. In this work, the Intelligent Farming Assistant is developed to enhance the level of efficiency, sustainability, and profitability of farming through leadership in the formulation of algorithms. A tool for all that is for the farmers and consumers and the entire agricultural economy and chalks the growth and sustainability of all.

Authors

Publication Details

Published
2025-05-07
DOI
https://doi.org/10.1109/rmkmate64874.2025.11042487
Primary Topic
Food Supply Chain Traceability
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Intelligent Farming Assistant for Optimized Crop Management and Farmer Consumer Connectivity

R. Rengalakshmi, Umadevi Subramanian, S. Churchill Karamsanth, S. Anuij et al.
1 links
Food Supply Chain Traceability
article

Intelligent Farming Assistant for Optimized Crop Management and Farmer Consumer Connectivity

R. Rengalakshmi, Umadevi Subramanian, S. Churchill Karamsanth, S. Anuij, N. Devanathansampath
article en

Abstract

The Intelligent Farming Assistant promises to be the ultimate farming operation tool that leverages the power of both complex computational technology and actual. The platform collects data and using machine learning solutions such as Random Forests, Gradient Boosting, as well as Neural Networks offers farmers recommendations regarding optimal planting times and types, resource use, or protection against pests and diseases. It also provides the latest weather information and reports on soil health that enable the farmers to make the right decision which improves production. What is more, stands out from other systems in its capacity to base its recommendations on the current data to provide a farmer with more real-time analysis of his or her circumstances. Moreover, unlike most other platforms designed to assist farmers in managing their crops, the platform also enables them to sell them directly to consumers, meaning fewer intermediaries and a wider client base. The easy-to-navigate design also means that farmers get the information they need in the format of their choice regardless of the device they are using. In this work, the Intelligent Farming Assistant is developed to enhance the level of efficiency, sustainability, and profitability of farming through leadership in the formulation of algorithms. A tool for all that is for the farmers and consumers and the entire agricultural economy and chalks the growth and sustainability of all.

Zero hunger
Openalex Percentile: Top 82%
Food Supply Chain Traceability
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.