Combining Machine Learning with Images from RGB and Multispectral Cameras to Estimate Fresh Weight and Height of Spinach for Processing Use

Abstract

Spinach for processing use should be systematically harvested according to the fresh weight and height standards required by the processing plant; thus, monitoring spinach growth in fields is crucial. However, ground measurements require substantial labor. In recent years, unmanned aerial vehicles (UAVs) have been established as effective labor-saving tools for monitoring crop growth. This study aimed to clarify: 1) which model and image type are appropriate for mono-regression in the estimation of fresh weight and height of processing spinach, 2) whether a combination of images from RGB and multispectral cameras and the application of machine learning can improve the estimation accuracy in multivariate regression, and 3) which model shows the highest estimation accuracy over extrapolation data. In the mono-regression, the highest fresh weight estimation accuracy was achieved with the soil adjusted vegetation index (SAVI) using a multispectral camera at 50 m altitude (test root mean squared error (RMSE) = 0.795). The highest height estimation accuracy was achieved with the modified green red vegetation index (MGRVI) using an RGB camera at 50 m altitude (test RMSE = 5.01). Estimation accuracy was highly dependent on image type, suggesting that the effectiveness of appropriate selection for accurate estimation. In multivariate regression, machine learning models showed higher accuracy for fresh weight estimation compared to multiple linear regression. Although most support vector regression models exhibited higher accuracy than that of the multiple linear regression model, random forest regression models exhibited lower accuracy for height estimation. These findings suggest that the selection of an appropriate model type is critical. Although combining images from RGB and multispectral cameras improved accuracy in machine learning models, the effects were inconsistent because of other factors including target traits, model types, and UAV altitude. After comparing all models with the highest accuracy in each category, the fresh weight estimation model developed using support vector regression with images from an RGB camera at 30 m altitude, and the height estimation model based on support vector regression with images from both cameras at 30 m altitude showed the highest accuracies (test RMSE = 0.720 and 4.19, respectively). These machine learning-based models can thus facilitate precise monitoring of the fresh weight and height of spinach for processing use.