Selection of Small Camera for Leaf Area Measurement Based on Distance Information and Validation of Strawberry Yield Prediction Model Using Distance Information

Abstract

　High-yield technology leveraging yield prediction has been studied for strawberries, a crop with significant export potential. Yield prediction requires time-series data for the leaf area index (LAI). While distance-based measurements using images offer a simpler and more accurate method for estimating LAI, few studies have examined camera selection and accuracy. Furthermore, strawberries can exhibit significant seasonal fluctuations in yield, necessitating a yield prediction model that accounts for dry matter distribution to fruits based on the daily number of fruits attached to the strawberry plant. This study therefore evaluated camera selection criteria for leaf area measurement using distance information and validated a new yield prediction model for strawberries. The results showed that key factors affecting camera selection include camera performance, compactness, ease of set up, affordability, and distance between the camera and the strawberries. In addition, the proposed yield prediction model successfully accounted for seasonal variations in strawberry yield, demonstrating its application to the development of efficient strategies for strawberry production.