Adaptive Lemuria: A progressive future crop prediction algorithm using data mining

• Published in: Sustainable computing informatics and systems Vol. 31; p. 100577
• Main Authors: M, Tamil Selvi, B, Jaison
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2021
• Subjects: Adaptive Lemuria algorithm; Agriculture; Crop prediction; Data mining; Deep learning; Deep learning; Crop prediction; Agriculture; Data mining; Adaptive Lemuria algorithm
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2021.100577

•Agriculture is an important field for the growth and development of the economy of any nation, but the accurate crop prediction is always an issue to the farmers and other farming based organizations because of the varying climatic factors.•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: DBN for feature learning and pre-training, Decision tree & K-Means clustering (HDTKM) with PSO for training to attaining global solution and Naive bayes clustering with PSO for testing to get optimum result.•The experimentation attains 98.35% of accuracy with an error rate of 0.0314, which is relatively higher than the existing methodologies. 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.