Machine learning and IoT-based smart farming for enhancing the crop yield

Abstract The lack of favourable atmospheric conditions leads to the loss of many crops each year. In India alone, over 11 billion dollars are lost. By combining IoT and machine learning technologies, this team has created a system that integrates agriculture’s three primary operations: crop selectio...

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Bibliographic Details
Published in:Journal of physics. Conference series 2023-03, Vol.2466 (1), p.12028
Main Authors: Sundaresan, S., Daniel Johnson, S., Mani Bharathy, V., Mohan Pavan Kumar, P., Surendar, M.
Format: Article
Language:English
Subjects:
Crop yield
Fertilizers
Irrigation systems
Machine learning
Moisture effects
Nutrients
Physics
Recommender systems
Soil moisture
Weather forecasting
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Summary:Abstract The lack of favourable atmospheric conditions leads to the loss of many crops each year. In India alone, over 11 billion dollars are lost. By combining IoT and machine learning technologies, this team has created a system that integrates agriculture’s three primary operations: crop selection, autonomous watering, and fertiliser suggestion. The following crops—Apple, Rice, Maize, Grape, Banana, Orange, Cotton, and Coffee—were considered in the study. Three systems are covered in the paper: The crop recommendation system employs machine learning to examine factors including nitrogen (N), phosphorous (P), potassium (K), pH, and weather before recommending a crop. The crop type and the current levels of soil nutrients are the two main determinants on which the fertiliser recommendation method bases its recommendation. When employing an automatic irrigation system, the crop is irrigated automatically while taking current soil moisture levels and weather forecasts into consideration. This paper attempted to implement the mentioned systems. The paper discusses the successes of the crop recommendation system, the automatic watering system, and the fertiliser recommendation system. In this paper, we report the results of simulations of the mentioned systems.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2466/1/012028