Authors

Abstract

In this study, a stacked machine learning algorithm was constructed with tomato organ biomass as the research object, taking the geometric morphology data of tomato organs as the variables, utilizing eight classical machine learning algorithms as the base-model, and applying the linear regression algorithm as the stacked meta-model. This algorithm was then utilized to establish a prediction model for tomato biomass at the organ scale, and the biomass models of tomato plant leaves and fruits at the organ scale were constructed. The model has R2=0.86, MAE=0.49, and RMSE=0.81 in predicting leaves, and R2=0.94, MAE=0.33, and RMSE=0.57 in predicting fruits. The model has practical applications in predicting tomato yield and supply, providing market information, and supporting agricultural investment decisions. It also helps to optimize agricultural production and management, guide industrial development and planning, and improve the efficiency and competitiveness of the agricultural sector.

Abstract in Chinese

本研究以番茄器官生物量为研究对象，将番茄器官的几何形态数据作为变量，利用八种经典机器学习算法作为基础模型，并应用线性回归算法作为堆叠元模型，构建了一种堆叠式机器学习算法。然后利用此算法建立器官尺度上的番茄生物量预测模型，并构建了器官尺度上番茄植株叶片和果实的生物量模型。该模型在预测叶片方面的R2=0.86，MAE=0.49，RMSE=0.81；在预测果实方面的R2=0.94，MAE=0.33，RMSE=0.57。该模型在预测番茄产量和供应、提供市场信息、支持农业投资决策等方面具有实际应用价值，还有助于优化农业生产和管理，指导产业发展和规划，提高农业部门的效率和竞争力。