Vol.1, No.2, 2019, pp.45-58, doi:10.32604/jai.2019.04444
OPEN ACCESS
ARTICLE
A Hybrid Approach of TLBO and EBPNN for Crop Yield Prediction Using Spatial Feature Vectors
  • Preeti Tiwari1, *, Piyush Shukla1
1 UIT, RGPV, Bhopal, 462033, India.
2 UIT, RGPV, Bhopal, 462033, India.
* Corresponding Author: Preeti Tiwari. Email: preetitw77@gmail.com.
Abstract
The prediction of crop yield is one of the important factor and also challenging, to predict the future crop yield based on various criteria’s. Many advanced technologies are incorporated in the agricultural processes, which enhances the crop yield production efficiency. The process of predicting the crop yield can be done by taking agriculture data, which helps to analyze and make important decisions before and during cultivation. This paper focuses on the prediction of crop yield, where two models of machine learning are developed for this work. One is Modified Convolutional Neural Network (MCNN), and the other model is TLBO (Teacher Learning Based Optimization)-a Genetic algorithm which reduces the input size of data. In this work, some spatial information used for analysis is the Normalized Difference Vegetation Index, Standard Precipitation Index and Vegetation Condition Index. TLBO finds some best feature value set in the data that represents the specific yield of the crop. So, these selected feature valued set is passed in the Error Back Propagation Neural Network for learning. Here, the training was done in such a way that all set of features were utilized in pair with their yield value as output. For increasing the reliability of the work whole experiment was done on a real dataset from Madhya Pradesh region of country India. The result shows that the proposed model has overcome various evaluation parameters on different scales as compared to previous approaches adopted by researchers.
Keywords
Crop yield prediction, data mining, machinelearning, vegetation index, TLBO.
Cite This Article
. , "A hybrid approach of tlbo and ebpnn for crop yield prediction using spatial feature vectors," Journal on Artificial Intelligence, vol. 1, no.2, pp. 45–58, 2019.
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