Adaptive Lemuria: A progressive future crop prediction algorithm using data mining
| Autor: | Tamil Selvi M, Jaison B |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science 020209 energy Crop yield Decision tree Word error rate Particle swarm optimization 020206 networking & telecommunications 02 engineering and technology computer.software_genre Naive Bayes classifier Deep belief network 0202 electrical engineering electronic engineering information engineering Data mining Electrical and Electronic Engineering Cluster analysis Algorithm computer Feature learning |
| Zdroj: | Sustainable Computing: Informatics and Systems. 31:100577 |
| ISSN: | 2210-5379 |
| DOI: | 10.1016/j.suscom.2021.100577 |
| Popis: | Agriculture is one of the foremost and the minimum salaried employment in India. Data mining be able to fetch an explosion in the agriculture field by altering the profits scenario through growing the optimum crop with crop yield prediction, which is a difficult task because of the climatic factors, soil fertility, nutrients and so on. Precise crop forecast requires fundamental understanding of the functional association between crop and input parameters and to predict the crop yield in advance we developed an Adaptive Lemuria algorithm. Our proposed model comprises of Deep Belief Network for feature learning and pre-training, Decision tree & K-Means clustering (HDTKM) with Particle Swarm Optimization (PSO) for training to attaining global solution and Naive bayes clustering with PSO for testing to get optimum result. The forecast made by our proposed algorithms will aid the ranchers to choose which crop to cultivate to get the extreme yield. The experimentation was conducted to verify the performance of our proposed framework in python with Anaconda Spyder and outcome attains 98.35 % of accuracy with an error rate of 0.0314, which is relatively higher than the existing methodologies. |
| Databáze: | OpenAIRE |
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