Sensors and machine learning are enhancing potato storage management by enabling predictive control to prevent quality loss, reducing energy expenses and downgrades. Key focus areas include monitoring environmental factors, improving actionable alerts, and integrating historical farming data. Successful implementations show significant energy savings and reduced shrinkage, though legacy storage limitations and sensor calibration issues pose challenges. Overall, predictive techniques promise more effective storage practices for maintaining potato quality.
