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

Abstract

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