Abstract:
Agriculture is one of the issues that no-way runs out to be examined. Since farming is one of the
main sources of livelihood of the pastoral population. Thus, the construction of agrarian data grounded internet of effects is veritably important to do. In Ethiopia collecting information like
fertility of the soil, rainfall, growth of crops, temperature, downfall and information regarding
colony of seeds, etc. can be collected with the help of IOT. It helps the growers to gain
information regarding all farming conditions. With the help of internet technology, farming
processes can be covered through detectors, smart cameras, mobile operations and bias like mini
chips. Through IOT the automated internet technology, farmers can utilize farming resource
efficiently.
In Jimma zone farmers rely on their experience to sow seeds on the soil. Without knowing what
types of crops can be grown in the soil accurately, they cultivate the land and grow crops based
on usual way of farming. Farmers do not try to crop new species in their land because if it fails to
grow, they will lose revenue from farming. So, if there is a means that will provide the
information to let them grow potential crops that will yield optimum production will be helpful
to them. In addition the soil they cultivate may have enough minerals to grow surplus crops that
are not grown in the region. Having the mineral content information of the soil in advance
enhances optimum crop production.
Previously many researches have been done in crop prediction in another country. But up to my
knowledge there is no existing crop prediction research that is done on Jimma zone in Jiren
kebele. The data that is collected and the model that is built aimed for this kebele only. This
research project is prediction of the types of crops that will grow in Jimma zone Jiren kebele.
Based on the soil mineral and environmental condition data of the kebele a machine learning
model was built to predict the types of crops that can be grown in the area. Different sensors
such as moisture sensor, temperature sensor, pH sensor and NPK (nutrient sensor) were
employed in the soil to get mineral and environmental condition data. And then decision tree
algorithm was used to build a machine learning model that will predict the types of crops that
will be grown in the soil. The average accuracy of the model in decision tree after 22 times run is
0.97.
