Predicting water status, growth and yield of tomato under different irrigation regimes using the RGB image indices and artificial neural network model.

Abstract

Water stress is a global challenge that severely impacts crop production by hindering essential physiological processes. To address this issue, proximal sensing has emerged as a promising technique for the early identification of stress in vegetables, enabling timely management interventions and optimizing yield. This study aimed to use RGB image indices and an artificial neural network (ANN) model to quantify the responses of various plant traits, such as fresh biomass (FB) weight, dry biomass (DB) weight, canopy water content (CWC), relative chlorophyll content (SPAD), soil moisture content (SMC), and tomato yield across different irrigation levels. Field experiments were conducted during the 2022 and 2023 growing seasons, capturing digital RGB images and measuring plant traits at the flowering and fruit-ripening stages. The results revealed that a reduced irrigation level led to a decrease in various plant traits. The study also revealed significant differences in RGB image indices between different irrigation levels, with strong positive relationships identified for the majority of RGB image indices incorporating green components (G) and R2 reaching 0.99 for various plant traits. However, the red-blue simple ratio (RB) index, which does not consider the G, did not significantly correlate with any of the plant traits. The ANN models achieved high prediction accuracy, with high R2 values reaching 0.99 for various plant traits and yields. These findings underscore the practicality and reliability of employing RGB imaging indices in conjunction with ANN models for effectively managing tomato crop growth and production, particularly under limited water conditions.

Plant phenotyping relevance

RGB画像指標とANNによる植物形質・収量の定量推定が研究の中心であり、予測精度も評価しているため、画像ベース形質推定の方法適用・検証に該当する。

Code and data availability

The paper reports RGB image indices and ANN models for tomato phenotyping, but the data availability statement says all data are presented within the article; no public dataset, image repository, code deposit, or trained model checkpoint is provided. The analysis used OpenCV/Python and scikit-learn, but no authors' URL

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