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 processes such as nutrient uptake, photosynthesis, and respiration. To address this issue, proximal sensing has emerged as a promising technique for detecting stress in plants. By utilizing remote sensing and non-destructive methods, early and spatial identification of stress in vegetable crops becomes possible, enabling timely management interventions and optimizing yield in precision farming. 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, growth stages, and growing seasons. 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 decreased FB, DB, CWC, SMC, and tomato yield. The study also revealed significant differences in RGB image indices between different irrigation levels, with lower values observed under severe stress treatment. The majority of RGB image indices incorporating the green component demonstrated strong positive relationships, with R² ranging between 0.52 and 0.94 for FB, 0.49 and 0.92 for DB, 0.44 and 0.85 for CWC, 0.29 and 0.82 for SPAD, 0.27 and 0.74 for SMC, and 0.42 and 0.89 for tomato yield. Notably, we did not observe a significant correlation between any of the RGB image indices and SPAD during the combined data of both stages. However, the red-blue simple ratio (RB) index, which does not consider the green component (G), did not significantly correlate with any of the plant traits. The ANN models utilizing RGB image indices achieved high prediction accuracy, as indicated by R² values ranging from 0.84 to 0.99 for FB, 0.88 to 0.98 for DB, 0.81 to 0.97 for CWC, 0.67 to 0.98 for SPAD, 0.55 to 0.81 for SMC, and 0.83 to 0.96 for tomato yield. 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 conditions of limited water availability for irrigation.