Transplant Track: AgriGenius: The Ultimate Smart Farming App

AgriGenius is a high-tech smart farm application that is able to revolutionize the traditional methods of agriculture by combining modern technologies with the use of Artificial Intelligence (AI) and Machine Learning (ML), Internet of Things (IoT) and real-time data analytics. The ultimate goal of the system is to improve agricultural productivity and sustainability, as well as decision-making efficiency by offering farmers with precise and data-driven information. The suggested system gathers real-time information of the IoT-enabled sensors positioned in the fields that are used in agriculture, and the sensors will record data on soil moisture, temperature, humidity, pH levels and nutrient content. Besides a ground-level sensing process, a comprehensive picture of crops state includes satellite imagery, historical data on agriculture, and the system developed. Machine learning algorithms allow the processing and analysis of these various sources of data to find patterns, predict results and generate actionable recommendations. AgriGenius provides various intelligent modules, such as crop health control, image processing-based disease control, irrigation control (according to the soil and weather conditions), and pest infestation forecasting. Predictive capacity of the system is further improved by adopting weather forecasting APIs, which enable farmers to be proactive with their agricultural activities. The system is also highly accurate by using convolutional neural networks (CNNs) and predictive analytics to detect diseases of crops and prescribe suitable treatment. The app will have a friendly mobile interface that will allow farmers, regardless of their technical knowledge, to access it. The system facilitates precision farming methods that would allow the effective utilization of resources like water, fertilizers, and pesticides hence lowering the cost of operation and resulting in less environmental cost. The scientific findings prove the newly introduced AgriGenius system to be more efficient and effective than the traditional farming techniques and already existing smart agriculture systems based on their accuracy, efficiency, and reliability. Having this system helps to make the agricultural industry more sustainable through enhancement of crop productivity, lowering the rate of resource wastage, as well as enlightening sound decision-making.

Authors

Publication Details

Journal
Iconic Research and Engineering Journals
Published
2026-04-21
DOI
https://doi.org/10.64388/irev9i10-1716604
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

Transplant Track: AgriGenius: The Ultimate Smart Farming App

D. Rambabu, Pulluri Ruthvik, Pallerla Aman Kumar, Velagandula Sri Ram Sanju
Iconic Research and Engineering Journals
Smart Agriculture and AI
article

Transplant Track: AgriGenius: The Ultimate Smart Farming App

D. Rambabu, Pulluri Ruthvik, Pallerla Aman Kumar, Velagandula Sri Ram Sanju
article en

Abstract

AgriGenius is a high-tech smart farm application that is able to revolutionize the traditional methods of agriculture by combining modern technologies with the use of Artificial Intelligence (AI) and Machine Learning (ML), Internet of Things (IoT) and real-time data analytics. The ultimate goal of the system is to improve agricultural productivity and sustainability, as well as decision-making efficiency by offering farmers with precise and data-driven information. The suggested system gathers real-time information of the IoT-enabled sensors positioned in the fields that are used in agriculture, and the sensors will record data on soil moisture, temperature, humidity, pH levels and nutrient content. Besides a ground-level sensing process, a comprehensive picture of crops state includes satellite imagery, historical data on agriculture, and the system developed. Machine learning algorithms allow the processing and analysis of these various sources of data to find patterns, predict results and generate actionable recommendations. AgriGenius provides various intelligent modules, such as crop health control, image processing-based disease control, irrigation control (according to the soil and weather conditions), and pest infestation forecasting. Predictive capacity of the system is further improved by adopting weather forecasting APIs, which enable farmers to be proactive with their agricultural activities. The system is also highly accurate by using convolutional neural networks (CNNs) and predictive analytics to detect diseases of crops and prescribe suitable treatment. The app will have a friendly mobile interface that will allow farmers, regardless of their technical knowledge, to access it. The system facilitates precision farming methods that would allow the effective utilization of resources like water, fertilizers, and pesticides hence lowering the cost of operation and resulting in less environmental cost. The scientific findings prove the newly introduced AgriGenius system to be more efficient and effective than the traditional farming techniques and already existing smart agriculture systems based on their accuracy, efficiency, and reliability. Having this system helps to make the agricultural industry more sustainable through enhancement of crop productivity, lowering the rate of resource wastage, as well as enlightening sound decision-making.

Iconic Research and Engineering JournalsVol. 9(10)
Zero hunger
Openalex Percentile: Top 7%
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.