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Smart Soil Detection and Crop Recommendation System using AI/ML
Subject area: Science,Engineering and Technology · Area of research: Agriculture
Abstract
Agriculture is the backbone of the global economy, yet farmers often face challenges in determining optimal crops due to variable soil conditions and limited access to expert knowledge. This paper presents a Smart Soil Detection and Crop Recommendation System using Artificial Intelligence (AI) and Machine Learning (ML). The system utilizes IoT-based soil sensors to collect real-time data on parameters such as pH, temperature, moisture, nitrogen (N), phosphorus (P), and potassium (K) content. The collected data is analysed using machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVMs), to classify soil types and recommend suitable crops for maximizing yield. An intelligent dashboard provides farmers with actionable insights for soil health monitoring and crop selection. Experimental results indicate that the proposed system enhances the accuracy of crop recommendations compared to traditional methods, promotes sustainable farming practices, and optimizes resource utilization. The integration of AI/ML with IoT-based soil monitoring demonstrates a promising approach toward precision agriculture.
Keywords
Smart Agriculture, Soil Detection, Crop Recommendation, Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI), Precision Farming, Soil Parameter Analysis.
How to cite this paper
@article{1711783,
author = {Doke Vishal Dattatraya, Khawale Prasad Dilip, Sakat Priya Manik, Prof. Priti R Vanmali},
title = {Smart Soil Detection and Crop Recommendation System using AI/ML},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {171-177},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1711783.pdf},
abstract = {Agriculture is the backbone of the global economy, yet farmers often face challenges in determining optimal crops due to variable soil conditions and limited access to expert knowledge. This paper presents a Smart Soil Detection and Crop Recommendation System using Artificial Intelligence (AI) and Machine Learning (ML). The system utilizes IoT-based soil sensors to collect real-time data on parameters such as pH, temperature, moisture, nitrogen (N), phosphorus (P), and potassium (K) content. The collected data is analysed using machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVMs), to classify soil types and recommend suitable crops for maximizing yield. An intelligent dashboard provides farmers with actionable insights for soil health monitoring and crop selection. Experimental results indicate that the proposed system enhances the accuracy of crop recommendations compared to traditional methods, promotes sustainable farming practices, and optimizes resource utilization. The integration of AI/ML with IoT-based soil monitoring demonstrates a promising approach toward precision agriculture.},
keywords = {Smart Agriculture, Soil Detection, Crop Recommendation, Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI), Precision Farming, Soil Parameter Analysis.},
month = {November},
}