IoT-based Crop Monitoring and Decision Support System for Precision Farming

Abstract: Crop prediction using Machine Learning (ML) and Internet of Things (IoT) based solutions is promising approach to guarantee food security and sustainable agriculture. Algorithms based on Machine Learning have shown promising results in predicting crop yields based on various environmental elements such as weather, soil conditions, and irrigation patterns. The project presents an alternative approach for crop prediction using ML. This approach involves integration of various sensors and IoT devices to collect data on various ecological factors that are known to affect crop yields, such as temperature, humidity, rainfall, and soil nutrient levels. To evaluate our approach, we collected data on crop yields and environmental factors for several years from multiple farms in different regions. The data is pre-processed and used to train the ML model, and its accuracy in predicting crop yields for a specified set of environmental conditions is tested. The outcomes reveal that this approach outperforms traditional methods of crop prediction, such as statistical regression models. The ML model was able to precisely predict crop yields with an average error rate of less than 4%. This demonstrates the potential of ML algorithms in improving crop yields and ensuring food security. In conclusion, this approach of crop prediction using ML is a promising method for improving agriculture and food security. By leveraging the power of ML algorithms and collecting data on various environmental factors, it can accurately predict crop yields and optimize agricultural practices. This may significantly affect food production worldwide and contribute to feeding the world's expanding population.

Keywords: Crop Prediction, ML Algorithm, Environmental Factors, IoT based Solutions, Food Security, sustainable agriculture.