Application of Machine Learning in Smart Agriculture to Predict Crop Yields Based on Environmental Factors
Keywords:
Smart Agriculture, Machine Learning, Crop Prediction, Environmental Factors, Artificial Intelligence.Abstract
The development of digital technology in the agricultural sector has led to the emergence of the concept of Smart Agriculture as a solution to increase agricultural productivity and efficiency through the use of data and artificial intelligence. The main problem in modern agriculture is the uncertainty of environmental conditions such as temperature, humidity, rainfall, light intensity, and soil conditions that can affect crop production yields. This study aims to apply Machine Learning methods to predict crop yields based on environmental factors to assist the decision-making process in the agricultural sector. The research method used is a quantitative approach with an experimental method, namely through the stages of collecting environmental data and crop yield data, data preprocessing, building a Machine Learning model, and evaluating model performance using prediction error measurement parameters. The results show that the Machine Learning model is able to process environmental factor data to find patterns of relationships with crop productivity and produce a prediction system that can be used to support agricultural decisions. The preprocessing process and feature selection are important factors in improving model quality because environmental data has complex and dynamic characteristics. The conclusion of this study shows that the application of Machine Learning in Smart Agriculture can be an effective approach to improving the accuracy of crop yield predictions and supporting data-driven agricultural management. Further research can be developed by integrating real-time IoT sensor data and testing various Machine Learning algorithms to obtain models with more optimal prediction performance.
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