Smart AG – Smart Agriculture using Gen AI

This project leverages generative AI to revolutionize agricultural practices by integrating advanced data analysis, predictive modeling, and real-time monitoring systems. By analyzing soil characteristics such as pH, moisture content, nutrient levels, and texture, the AI system provides farmers with precise crop recommendations tailored to specific soil conditions, ensuring optimal growth and yield. The system also considers environmental factors like temperature, humidity, and sunlight exposure to further refine crop suggestions. This enables farmers to plan their activities, such as planting, irrigation, and harvesting, more effectively, mitigating the impact of adverse weather conditions. Additionally, the system employs image analysis to detect potential crop diseases early by comparing images of crops with a comprehensive database of known diseases. This early diagnosis facilitates timely interventions, reducing crop losses and ensuring healthy produce. By offering accurate recommendations and predictions, the system helps farmers achieve higher yields and better-quality produce, ensuring a stable food supply and addressing issues of food security. The promotion of sustainable agricultural practices reduces the environmental impact of farming and ensures the long-term viability of agricultural activities. Additionally, the improved productivity and reduced losses lead to higher profits for farmers, contributing to economic growth in the agricultural sector.

Authors

Publication Details

Published
2025-05-06
DOI
https://doi.org/10.38124/ijisrt/25apr1578
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Smart AG – Smart Agriculture using Gen AI

S. Vasuki, P. Balamurugan, Hariselva Vignesh S, B Devadharshini et al.
Smart Agriculture and AI
article

Smart AG – Smart Agriculture using Gen AI

S. Vasuki, P. Balamurugan, Hariselva Vignesh S, B Devadharshini, M Dharsini
article en

Abstract

This project leverages generative AI to revolutionize agricultural practices by integrating advanced data analysis, predictive modeling, and real-time monitoring systems. By analyzing soil characteristics such as pH, moisture content, nutrient levels, and texture, the AI system provides farmers with precise crop recommendations tailored to specific soil conditions, ensuring optimal growth and yield. The system also considers environmental factors like temperature, humidity, and sunlight exposure to further refine crop suggestions. This enables farmers to plan their activities, such as planting, irrigation, and harvesting, more effectively, mitigating the impact of adverse weather conditions. Additionally, the system employs image analysis to detect potential crop diseases early by comparing images of crops with a comprehensive database of known diseases. This early diagnosis facilitates timely interventions, reducing crop losses and ensuring healthy produce. By offering accurate recommendations and predictions, the system helps farmers achieve higher yields and better-quality produce, ensuring a stable food supply and addressing issues of food security. The promotion of sustainable agricultural practices reduces the environmental impact of farming and ensures the long-term viability of agricultural activities. Additionally, the improved productivity and reduced losses lead to higher profits for farmers, contributing to economic growth in the agricultural sector.

Openalex Percentile: Top 93%
Smart Agriculture and AI
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.