Plant-specific crop evapotranspiration estimation system for greenhouse tomatoes using convolutional neural network and rail-based monitoring device

Abstract

Accurately estimating individual plant evapotranspiration is essential for precise management and sustainable resource use in greenhouse cultivation. Integrating evapotranspiration models with crop-monitoring devices capable of acquiring images and solar radiation data may enable plant-level estimation of crop evapotranspiration. In this study, a plant-specific crop evapotranspiration estimation system was developed for hydroponic tomato cultivation in greenhouses during the harvest season. The evapotranspiration was estimated using a simplified Penman-Monteith model based on the leaf area index (LAI), solar radiation, air temperature, and relative humidity. The model was subsequently generalized through z-score normalization. To acquire side-view RGB images of individual tomato plants and measure the solar radiation distribution, a rail-based crop-monitoring device was employed. A ResNet-based convolutional neural network model was developed to estimate the LAI from the acquired images. The images were augmented via permutations with repetition to enhance the model's accuracy. An image-merging method and a You Only Look Once version 8 Nano-based object detection model were used for rapid and automated image acquisition. The system calculated the crop evapotranspiration for each plant, and its performance was evaluated in a tomato cultivation greenhouse. Validation tests revealed strong correlations between the estimated and measured LAI (R2 = 0.89, RMSE = 0.06) and between the predicted and actual evapotranspiration values (R2 = 0.88, RMSE = 26.43 g h- 1 plant- 1). Distribution maps for the LAI and evapotranspiration were generated using the developed system. The system can accurately assess plantspecific evapotranspiration, thereby supporting precision crop management and helping improve productivity in greenhouse cultivation.