Prediction of strawberry growth and fruit yield based on environmental and growth data in a greenhouse for soil cultivation with applied autonomous facilities

The ability to predict how well crops will grow and how much fruit they will yield is important for farmers, consumers, and researchers. Advances in environmental and plant measurement equipment provide the opportunity for more data to be collected from plant growing operations, which could result in more accurate predictions. The objective of this study was to predict the strawberry growth and fruit yield using environmental and growth data collected with this equipment. The correlation coefficients of the average daily air temperature and soil temperature data for strawberry growth predictions were higher than the relative humidity, soil moisture content, electronic conductivity, CO₂ concentration, photosynthetic active radiation, and vapor pressure deficit data. The correlation coefficients of photosynthetic active radiation, vapor pressure deficit, and relative humidity for strawberry yield prediction were higher than the other environmental data and all growth data such as plant height, crown diameter, and leaf length and width. The regression model using environmental data showed high correlation coefficients with the actual yield data (R² = 0.99). These results indicate that strawberry growth and fruit yield could be predicted using environmental data.