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 plant-specific evapotranspiration, thereby supporting precision crop management and helping improve productivity in greenhouse cultivation.

Plant phenotyping relevance

個体別のLAI画像推定と蒸発散量推定を中核とする監視システムを開発し、実測値との相関で検証しているため、植物表現型取得・推定手法として対象に含める。

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