• Version
• Download 7
• File Size 720.70 KB
• File Count 1
• Create Date 23 March 2026
• Last Updated 23 March 2026

An AI-Driven Multi-Sensor Soil Analytics System with Crop Recommendation

Dhaval J. Devikar
dhavaldevikar09@gmail.com
Ms. Minakshi B. Manalwar
siddhamminakshi@gmail.com
Dr. M.V. Lande
milind.jdiet@gmail.com
Abhijit A. Khandre
abhijeetkhandre2007@gmail.com
Suhani B. Palikondawar
suhanipalikondawar@gmail.com
Mrs. Sarita I. Bansod
sarubansod@gmail.com
Yash S. Shriramjawar
yashshriramojwar@gmail.com
Dhanashri G. Atram
dhanashriatram01@gmail.com
Department Of Computer Engineering Government Polytechnic Gadchiroli, Maharashtra, India

Abstract— Agricultural productivity largely depends on soil quality and environmental factors that influence crop growth, which often leads to inefficient irrigation, unsuitable crop selection, andreduced yield. To address these challenges, this paper  proposes an artificial intelligence based multi sensor soil analytics system for intelligent agricultural decision support. The proposed system continuously collects important soil and environmental parameters. such as soil moisture, temperature, humidity, pH, and nutrient levelsusing an integrated sensing  framework. The collected sensor data is processed by the microcontroller and displayed through amonitoring interface for analysis. A machine learning model is employed to analyze the multi sensor data and evaluate soil conditions, predict irrigationrequirements, and recommend suitable crops.Experimental results indicate that the proposed  system improves precision farming by enabling data driven decisions, optimizing resource utilization, andsupporting sustainable agricultural practices. For  example, chickpea was identified as a suitable crop for the tested soil conditions.Keywords— Artificial intelligence, Internet of  Things, Soil health monitoring, Crop prediction, Precision agriculture, Machine learning