| Autor: |
Iqbal, S., Nizamani, S., Qasim, I., Siraj, S., Soomro, M. A., Ayaz, A. |
| Předmět: |
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| Zdroj: |
Technical Journal of University of Engineering & Technology Taxila; 2024, Vol. 29 Issue 1, p13-19, 7p |
| Abstrakt: |
Agricultural crop monitoring and data analysis has been challenging part in agronomics. The IoT based systems give a better solutions to monitor crop and collect data for further analysis. This study bases on soil and weather data collection and prepare a dataset. The dataset is constructed on the basis of four important parameters of weather and soil including temperature, soil pH level, humidity, soil moisture and smoke. The two major crops monitored named Wheat and Cotton for dataset construction. The data set attributes are selected by applying principal component analysis. The highest ranked attribute is selected for data analysis. Furthermore, machine learning models applied for the better results analysis comparatively. ML models parameters are Correlation coefficient, Mean Absolute error, Root Mean Squared Error, relative absolute error and Root relative squared error. The results are obtained by Applying WEKA Data Mining Tool. The results showed that Random Forest Classifier performed better results as compared to KNN and Decision Tree classifiers. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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