Optimizing crop selection for sustainable agriculture: a compound ensemble approach integrating machine learning and IoT-based sensors.

Crop selection is the meticulous process of choosing the most suitable crops for a specific geographical region, taking into consideration a range of factors. This research work delves into the transformative role of advanced technology by focusing on the application of IoT sensors and machine learning for crop selection in agriculture. The problem at hand is the demand for precise crop recommendations, with the existing system relying on static data collection and a single ML model for predictions. The proposed solution is a crop recommendation system that integrates rainfall forecasts, drought predictions, and soil-related parameters to enhance recommendations. An innovative ensemble approach, utilizing 12 weak classifiers including logistic regression and artificial neural networks is implemented for better prediction accuracy. This research involves the creation of a compound ensemble model and the use of a distance-based similarity index for improved recommendations. The methodology includes an IoT sensor for data collection and ML model integration. The ensemble approach and similarity index enhance adaptability to regional variations, benefiting the agricultural sector. An accuracy of 99.8% is observed for the models, indicating precision and robustness. Eventually, this paper exploits advanced technology to transform crop selection, fostering agricultural productivity and global food security. This research serves as a technical proof-of-concept for regional precision agriculture, offering a scalable framework to mitigate the impact of climate variability on crop productivity in semi-arid zones.