A Review on Potato Crop Yield and Nitrogen Management Utilizing Remote/Proximal Sensing Technologies and Machine Learning Models in Canada

Potatoes are a vital part of our diet worldwide, but their cultivation poses challenges due to environmental impacts from traditional farming methods. Precision agriculture offers a promising solution by managing variability in crop growth and reducing environmental disturbance. Remote sensing technologies have become crucial for monitoring crops and field dynamics more efficiently. In potato farming, the use of remote and proximal sensing technologies facilitates the prediction of yields and nutrient levels. This capability allows farmers to pinpoint the spatiotemporal variations in yields and nutrients, allowing them to make precise management decisions for each specific area of their fields. It is also valuable for monitoring nitrogen levels in plants during the growing season. This helps farmers apply the right amount of fertilizer at the right time, optimizing yields without affecting the environmental quality. While still evolving, remote sensing transforms potato farming by providing detailed, non-invasive insights that can enhance productivity and sustainability. Research into different technologies and machine learning models for potato farming in Canada has been largely confined to a few provinces. This review aimed to identify this knowledge gap, underscoring the need for broader exploration of innovative agricultural methods across diverse regions of the country.