Usage of Artificial Intelligence in Agriculture

Abstract

This study investigates the application of various artificial intelligence (AI) techniques in agriculture. The materials used consist of plant leaf images, field images, agricultural production data and climate data obtained from agricultural research institutes and open access databases. The AI ​​techniques applied in this study include fuzzy logic for modeling uncertain data, artificial neural networks for tasks such as disease classification and energy consumption estimation, genetic algorithms for optimization problems, expert systems for diagnosing agricultural problems and ant algorithms for path optimization. The methodology includes several main steps. First, data needs to be collected and preprocessed for AI model training. In this step, the data collection and preparation process is of great importance. In the second step, it is aimed to apply various AI methods to specific agricultural problems. In the third step, the training of the models on training datasets and the evaluation of their performance on test datasets with metrics such as accuracy and F1 score are carried out. Finally, the results are analyzed and interpreted. In this step, the focus is on the analysis of model performance, discussion of successful findings and highlighting the advantages and disadvantages of AI techniques in agricultural applications. The study demonstrates an approach to comprehensively use AI to increase productivity in agriculture, optimize resource use, and improve decision-making processes in agricultural practices.