Abstract: Crops are always in demand in the country, not only for the lives of the people, but also for eco nomic growth, so growing crops is of utmost importance. Using standard technology also increases efficiency and lessens the workload of the farmers. Therefore, in order to increase productivity, it is important to know about soil moisture and types of crops. Each variety of crop and the associated soil requires a particular amount of water, so the project need to make the most of what is available. In order to achieve this, it must utilize modern technology and tools. This paper focuses on an automated irrigation system, i.e., irrigating fields only when they need to be watered, by utilizing machine learning algorithms. Real-time readings of soil moisture, fertility, and pH are sensed through sensors and are available on the system.
Keywords: Agricultural yield prediction, Crop yield forecasting, Machine learning in agriculture, Regression models in agriculture
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DOI:
10.17148/IJARCCE.2025.14941
[1] Rashmi, Bindu T, Gouthami J, H M Anitha, J Ashwini, "Predicting Agricultural Yields Based On Machine Learning Using Regression And Deep Learning," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.14941
