Vegetation index based crop yield prediction model using convolution neural network - a case study of Kenya
Date
2020-06
Authors
Chepngetich, Judith
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Predicting the crop yield is a vital food security strategy that can help a country take suitable
measures and come up with policies that will help in crop production management. Such
predictions will also support the farmers and industries involved in crop production for strategizing
the logistics of their business or farming activities. Having sufficient production plans can improve
food sufficiency and avoid situations of food emergencies. Climate change has had a huge impact
on food production with variations in crop yields, creating uncertainty. Most of the studies on crop
yield prediction have been done based solely on weather data, which is sometimes inaccurate due
to scarcity of weather information especially in developing countries, where there is poor record
keeping and insufficient resources to collect data. The use of vegetation indices derived from
remote sensing data overcomes these challenges by providing data that is easily accessible and
gives a comprehensive and multidimensional analysis.
This study proposes a model that uses of vegetation index to predict crop yield using machine
learning. Data from past crop yields in Kenya and vegetation greenness indices were the inputs
applied to the algorithms. Various machine learning algorithms were applied and thereafter
evaluated, so as to select the algorithm that gives better accuracy. To determine the accuracy level
for the prediction model, the RMSE is calculated to compare actual and predicted values. The
RMSE values obtained using convolution neural network for the three crops maize, rice and wheat
were lower compared to those obtained using ridge regression, so it was selected as the optimal
algorithm for the crop yield prediction model.
Description
A Thesis Submitted to the Faculty of Information in partial fulfillment of the requirements for the award of Master of Science in Information Technology
Keywords
Convolution Neural Network, Crop Yield Prediction, Vegetation Index