Development of Monitoring System Using Unmanned Aerial Vehicle and Artificial Intelligence for Potato Yield Prediction (Part 1)

Abstract

We used UAV as a remote sensing device in potato cultivation, and assessed the relationship between image information and yield. The experiment was performed in a potato field with six fertilization conditions, aerial images were acquired, and growth and yield were investigated. Yield prediction models were constructed by multiple regression analysis and AI. The aerial image showed a difference in canopy. Plant height differed each of the treatment as the growth progressed. For the prediction of potato yield, multiple regression analysis based on NDVI and plant height data during the growing and flowering seasons had high accuracy. Yield prediction by AI, the accuracy is low due to imbalance of datasets and overfitting, so it is necessary to improve accuracy.