Statistical Modelling for Precision Agriculture: A Case Study in Optimal Environmental Schedules for Agaricus Bisporus Production via Variable Domain Functional Regression
This research developed a statistical method to help mushroom farmers optimize their growing conditions using sensor data. The study analyzed how factors like temperature, humidity, and oxygen levels affect mushroom yields throughout the growing process. The findings provide practical guidance for farmers to adjust their growing environments to maximize production. Impacts on everyday life: – More efficient food production through optimized growing conditions – Potential reduction in agricultural resource waste and costs – Improved mushroom availability and quality for consumers – Application of similar methods could improve yields of other indoor-grown crops – Demonstrates how data analysis can improve traditional farming practices