A Predictive Model To Predict Seed Classes Using Machine Learning

Abstract

In Ethiopian history, agriculture has been the backbone of the economy. This agricultural activity remain undeveloped due to different factors. Most of the activities are done with a lack of modern technology. Currently, seed classification is done based on knowledge of human being. The current seed classification analysis is inefficient and has no validation mechanism. In this research, we have made an effort to present a predictive model to predict seed classes using machine learning algorithms which results in high crop production. For the development, this research machine learning algorithm is used to learn from data which can be used to make predictions, to make real-world simulations, for pattern recognitions and classifications of the input data. An artificial neural network is used for modeling complex relationships between inputs and outputs or to find patterns in data. The objective of this thesis is to understand the machine learning algorithm using neural networks and constructing model which predicts seed classes based on machine learning technique. The developed model is experimented using seed dataset and then seed classes are predicted using the developed model. Finally by using the developed model, determinant factors for classify seeds are identified and ranked.