Image-based phenotyping to estimate anthocyanin concentrations in lettuce

Automated Imaging to Evaluate the Exogenous Gibberellin (Ga3) Impact on Seedlings from Salt-Stressed Lettuce Seeds

Salinity stress is a common challenge in plant growth, impacting seed quality, germination, and general plant health. Sodium chloride (NaCl) ions disrupt membranes, causing ion leakage and reducing seed viability. Gibberellic acid (GA3) treatments have been found to promote germination and mitigate salinity stress on germination and plant growth. 'Bauer' and 'Muir' lettuce (Lactuca sativa) seeds were soaked in distilled water (control), 100 mM NaCl, 100 mM NaCl + 50 mg/L GA3, and 100 mM NaCl + 150 mg/L GA3 in Petri dishes and kept in a dark growth chamber at 25 °C for 24 h. After germination, seedlings were monitored using embedded cameras, capturing red, green, and blue (RGB) images from seeding to final harvest. Despite consistent germination rates, 'Bauer' seeds treated with NaCl showed reduced germination. Surprisingly, the 'Muir' cultivar's final dry weight differed across treatments, with the NaCl and high GA3 concentration combination yielding the poorest results (p < 0.05). This study highlights the efficacy of GA3 applications in improving germination rates. However, at elevated concentrations, it induced excessive hypocotyl elongation and pale seedlings, posing challenges for two-dimensional imaging. Nonetheless, a sigmoidal regression model using projected canopy size accurately predicted dry weight across growth stages and cultivars, emphasizing its reliability despite treatment variations (R2 = 0.96, RMSE = 0.11, p < 0.001).

Estimating the frost damage index in lettuce using UAV-based RGB and multispectral images

Introduction

The cold stress is one of the most important factors for affecting production throughout year, so effectively evaluating frost damage is great significant to the determination of the frost tolerance in lettuce.

Methods

We proposed a high-throughput method to estimate lettuce FDI based on remote sensing. Red-Green-Blue (RGB) and multispectral images of open-field lettuce suffered from frost damage were captured by Unmanned Aerial Vehicle platform. Pearson correlation analysis was employed to select FDI-sensitive features from RGB and multispectral images. Then the models were established for different FDI-sensitive features based on sensor types and different groups according to lettuce colors using multiple linear regression, support vector machine and neural network algorithms, respectively.

Results and discussion

Digital number of blue and red channels, spectral reflectance at blue, red and near-infrared bands as well as six vegetation indexes (VIs) were found to be significantly related to the FDI of all lettuce groups. The high sensitivity of four modified VIs to frost damage of all lettuce groups was confirmed. The average accuracy of models were improved by 3% to 14% through a combination of multisource features. Color of lettuce had a certain impact on the monitoring of frost damage by FDI prediction models, because the accuracy of models based on green lettuce group were generally higher. The MULTISURCE-GREEN-NN model with R2 of 0.715 and RMSE of 0.014 had the best performance, providing a high-throughput and efficient technical tool for frost damage investigation which will assist the identification of cold-resistant green lettuce germplasm and related breeding.