Estimation Chickpea Species and Productivity per Decare with Synthetic Data Generation Methods

Production increase in agriculture depends on some parameters such as improving arable land, activating spraying and irrigation activities. In addition to these, it is known that seed types have an effect productivity. Therefore, proper selection of important. With the developing technology, big data consisting scientific studies can be recorded digitally used estimation or decision-making process. this study, chickpea species diversity was made with classification process using machine learning methods by taking advantage characteristics plant. addition, productivity per decare estimated regression Accuracy preferred a success criterion for classification, rmse regression. The dataset first raw, then experiments were synthetic data. To generate data, minority oversampling technique method also n-shifting mean proposed study used. When rates results obtained compared, highest rate 90.6% only raw Likewise, created 100%. For regression, score 0.17 0.16 high performance showed algorithms field.