International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 12 | Dec 2024
p-ISSN: 2395-0072
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IoT Based Soil Parameter Analysis for Crop Recommendation Snehal R. Watharkar1, Shreyash S. Mali2, Sneha S. Salunkhe3, Shreya S. Khochage4, Omkar U. Kanase5 1Assistant Professor, Department of Electronics and Telecommunication Engineering, Kasegaon Education
Society’s Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale, MS-415414, India. 2345UG Student, Department of Electronics and Telecommunication Engineering, Kasegaon Education Society’s
Rajarambapu Institute of Technology, affiliated to Shivaji University, Sakharale, MS-415414, India. ---------------------------------------------------------------------***--------------------------------------------------------------------2. LITERATURE SURVEY Abstract - In the evolving agricultural landscape, precision farming is revolutionizing crop cultivation with the aid of technology. This research focuses on the development of a soil parameter analysis device that uses Bluetooth technology to measure and transmit real-time data on essential soil properties such as temperature, moisture, pH, and nutrient content. The device offers personalized crop recommendations based on soil conditions, helping farmers optimize their resource use and improve crop yields. By integrating IoT principles with Bluetooth, the system provides an accessible and efficient solution for modern, sustainable farming.
Poor soil fertility is a major challenge in Indian agriculture. This study uses chemical soil measurements to classify soil parameters such as organic carbon (OC), phosphorus pentoxide (P2O5P_2O_5P2O5), manganese (Mn), and iron (Fe), alongside soil pH, type, and nutrient content, to recommend fertilizers and suitable crops. Twenty machine learning classifiers, including Random Forest (RF), AdaBoost, and SVM, were evaluated. RF achieved the highest performance in six of ten classification problems, surpassing 90% accuracy in most cases. The results highlight the potential of ML models to save time and cost while offering reliable recommendations.[1]
Key Words: Soil Analysis, IoT, Bluetooth, Precision Agriculture, Real-time Data, Sensors, Sustainable Farming.
Agriculture faces numerous challenges such as soil degradation, water scarcity, and the effects of climate change. Precision farming, supported by Internet of Things (IoT) technologies, offers an innovative approach to address these challenges. This paper presents the development of a Bluetooth-based device for real-time soil parameter analysis. This device measures critical parameters such as soil moisture, soil temperature, pH, and N, P, K that provides crop recommendations based on the data collected by respective sensors.
Sensitivity and uncertainty analyses identify critical soil parameters for crop simulation models, enabling users to prioritize calibration efforts. This study focused on 15 soil input parameters of the Web Info Crop Wheat model under different stress conditions, including water deficit, high temperature, and their combination. Key findings showed that nutrient parameters like nitrate and organic carbon were most sensitive under potential and high-temperature stress, while soil moisture parameters such as clay percentage and field capacity were critical under water deficit conditions. Identifying sensitive soil parameters significantly improved model efficiency across various agro-climatic regions in India.[2]
In conventional soil testing method, number of soil samples are collected from the same field at different places. Then the collected soil samples are mixed then filtered and converted to a single fine soil sample. 10 grams of soil sample is tested on various equipment. These tests like electrical conductivity, pH, and flame photometry are carried out using various solutions. As this conventional method of soil testing takes more time to give precise results, IoT based soil parameter analysis device is developed. This device allows farmers to maximize their available resources and reduce the risk of crop failure. By utilizing Bluetooth for data transmission, the system ensures easy access to soil health information through a mobile application, empowering farmers to make informed decisions in crop management.
This study presents an IoT-enabled soil nutrient classification and crop recommendation model (IoTSNA-CR) designed to assist farmers in precision agriculture. The model integrates sensors, cloud computing, and machine learning for real-time monitoring of soil parameters such as temperature, moisture, pH, and NPK values. Data collected via sensors are stored in Firebase cloud storage and accessed through an Android application for analysis using a multi-class support vector machine optimized with the fruit fly optimization method (MSVM-DAG-FFO). The proposed algorithm achieved high accuracy (0.973) compared to other methods like SVM and decision trees. The system provides cost-effective, userfriendly solutions for monitoring soil health, optimizing fertilizer use, and enhancing productivity.[3]
1. INTRODUCTION
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