A Novel Approach for Monitoring Agricultural Production Process using Wireless Sensor Networks and Machine Learning
| dc.contributor.author | ADITYA ARABALE ; CHARANRAJ K R ; NISHANTH S B | |
| dc.date.accessioned | 2024-10-29T06:37:23Z | |
| dc.date.available | 2024-10-29T06:37:23Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | The majority of nations rely heavily on agriculture. In India, agriculture directly supports more than half of the country's population. The yield of a given crop is influenced by a number of variables, including the climate, wind speed, soil quality, humidity, etc. The growth of a crop is impacted by these components' ongoing variability. The agricultural industry has benefited from technological advancement. In the agriculture industry, wireless sensor networks and crop yield prediction have had a significant impact. This study introduces a revolutionary Precision Farming method and demonstrates how sensor data can be effectively utilized. The Crop Yield Prediction model receives real-time input from the sensor-generated data. This strategy aids us in getting more accurate results. The project suggests a web application that, every split second, sends data from wireless sensors used in precision farming as an input to a crop production forecast model. Additionally, these sensed parameters from different users can be used as a training dataset. This method not only makes the most of the sensor data but also predicts crop yield with accuracy and promptness. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/15991 | |
| dc.language.iso | en | |
| dc.publisher | NHCE | |
| dc.title | A Novel Approach for Monitoring Agricultural Production Process using Wireless Sensor Networks and Machine Learning | |
| dc.type | Learning Object |