Abstract
The agriculture is very essential and important part of human lives and serves as the basis of economical growth. The yield of the crops is dependent on numerous factors and the most important factor which effects the crop production in the soil. Soil has several parameters such as Nitrogen content(N), phosphorous content, potassium content(K), pH, temperature and humidity which are to be analysed in order to provide a view on which crop is suitable for cultivation. In the current research, a relative survey is made on the soil characteristic parameters in order to predict the future crop which can be grown is performed using predictive analysis algorithms. In addition to that the NPK content for each crop is analysed and if there is any deficiency in these contents, suggestions are given in order to improve the deficient content. The embedded system which has sensors and controller is used in the data acquisition process. The analysis is done using the data which are obtained from the sensors like Nitrogen, phosphorous, potassium, pH and temperature which are attached to the field and the sensor data is processed using the LPC2148 microcontroller. The data set are trained using KNN, Gaussian Naïve Bayes, Random Forest, Decision tree, Logistic regression and SVM. Experimental results indicate that Gaussian Naïve Bayes and Random Forest are found to be more efficient with reference to metrices like accuracy, precision, F1 score and recall. And hence the prediction is made using the GNB and Random Forest algorithm and the percentage of accuracy for the algorithms are 97.3% and 98% respectively 88%.
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Sree, R.D., Raja, A.A., Jayanthy, S. (2023). An Embedded System for Smart Farming Using Machine Learning Approaches. In: Mercier-Laurent, E., Fernando, X., Chandrabose, A. (eds) Computer, Communication, and Signal Processing. AI, Knowledge Engineering and IoT for Smart Systems. ICCCSP 2023. IFIP Advances in Information and Communication Technology, vol 670. Springer, Cham. https://doi.org/10.1007/978-3-031-39811-7_25
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DOI: https://doi.org/10.1007/978-3-031-39811-7_25
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