The role of machine learning algorithms in optimizing agricultural production: a review of international experience and adaptation to Ethiopian conditions

The relevance of this study is driven by the need to enhance the efficiency of agricultural production in response to the growing demand for food security, particularly in economically underdeveloped countries such as Ethiopia. The main objective of the research is to explore the potential application of machine learning algorithms to optimize agricultural processes and adapt international practices to the specific conditions of Ethiopia. The methodological approach includes an analysis of contemporary scientific literature on the use of machine learning in agriculture and the systematization of successful practices involving algorithms such as CNN, LSTM, RNN, and Q-Learning. The study investigates the characteristics of Ethiopia's agricultural sector, including existing barriers to the adoption of advanced technologies. The results highlight that machine learning algorithms hold significant potential for increasing crop yields, improving soil and crop monitoring, and forecasting climate risks. Specifically, utilizing data from drones and sensors enables the creation of precise models for managing agricultural processes. However, key challenges such as insufficient funding, a lack of specialized data processing infrastructure, and limited access to technology have been identified. The study concludes by emphasizing the importance of attracting governmental and international investments, developing tailored databases, and creating models that account for local conditions. The findings provide practical value for developing strategies to digitize agriculture and prevent food crises in countries facing similar challenges.

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