Artificial Shading Can Adversely Affect Heat-tolerant Lettuce Growth and Taste, with Concomitant Changes in Gene Expression

Shading has been used to produce high-quality lettuce (Lactuca sativa) in locations where production conditions are not optimal for this cool-season crop. To learn what additional benefits shading provides if heat-tolerant cultivars are used and to understand the effects of shading on growth, sensory quality, chemical content, and transcriptome profile on heat-tolerant lettuce, we grew two romaine lettuce cultivars with and without shading using 50% black shadecloth in 2018 and 2019. Shading reduced plant leaf temperatures, lettuce head fresh weights, glucose and total sugars content, and sweetness, but not bitterness, whereas it increased lettuce chlorophyll b content compared with unshaded controls. Transcriptome analyses identified genes predominantly involved in chlorophyll biosynthesis, photosynthesis, and carbohydrate metabolism as upregulated in unshaded controls compared with shaded treatments. For the tested cultivars, which were bred to withstand high growing temperatures, it may be preferable to grow them under unshaded conditions to avoid increased infrastructure costs and obtain lettuce deemed sweeter than if shaded.

Quality and Yield of Lettuce in an Open-Air Rooftop Hydroponic System

In this study, the yield and growth performance of lettuce in an open-air rooftop hydroponic system were investigated. Lettuce was grown in a closed recirculating nutrient film technique (NFT) unit using a standard nutrient solution (NS). Yield, fresh weight, and nutrient content in the leaf tissue of the harvested lettuce were measured. The results were compared with the results obtained in indoor hydroponic lettuce growth with artificial lightning. Despite strong winds during the growth period, 25% of the total lettuce heads weighed twice the marketable weight; however, 25% of the total lettuce heads were below the marketable weight. A more efficient nutrient uptake was indicated by the lettuces in the rooftop system compared with the uptake in the indoor system. Foliar analysis revealed a higher content of all nutrients in the leaves of rooftop hydroponic lettuce compared with indoor hydroponic lettuce. This study suggests that hydroponic rooftop-grown lettuce can be competitive with their indoor counterparts if the rooftop hydroponic system is protected from extreme weather conditions.

Kaolin application to increase lettuce (Lactuca sativa) growth under suboptimal watering condition

Lettuce is a crop sensitive to water availability as it needs more water in cultivation. Kaolin has been reported to reduce the impact of drought stress in plants. However, the use of kaolin to increase lettuce growth under drought condition has not been reported yet. In this study, the effect of kaolin to increase the growth of lettuce under suboptimal watering condition was investigated. Kaolin foliar application (3%, 5%, and 6%) was tested on a group consisting of 5 lettuce plants grown under reduced watering condition in a greenhouse. Lettuce height, number of leaves, root length, leaf length and width, fresh and dry weight of lettuce, and leaf chlorophyll content were observed. Kaolin applications significantly increased the growth of lettuce in most parameters compared to the negative control (reduced watering condition, without kaolin). Kaolin 3% application presented the best treatment to increase growth parameters. All kaolin applications did not affect chlorophyll content significantly under lower watering condition. In conclusion, kaolin was able to reduce the impact of reduced watering condition and increase lettuce growth but showed no effect on the leaf chlorophyll content. Thus, further evaluation of kaolin application in lettuce under drought condition is needed.

Manipulation of Intraday Durations of Blue- and Red-Light Irradiation to Improve Cos Lettuce Growth

The morphology of plants growing under combined blue- and red-light irradiation is affected by the presence or absence of time slots of blue- and red-light mono-irradiation. The purposes of this study were to investigate the morphology and growth of cos lettuce grown under light irradiation combining several durations of blue and red light simultaneously and independent mono-irradiations of blue and red light during the day, and to clarify the effects of the durations of blue-light mono-irradiation and blue-light irradiation. Young cos lettuce seedlings were grown under 24-h blue-light irradiation with a photosynthetic photon flux density (PPFD) of 110μmol m−2 s−1 (B+0R) or under 24-h blue-light irradiation with a PPFD of 100μmol m−2 s−1 supplemented with 8 (B+8R), 16 (B+16R), and 24-h (B+24R) red-light irradiation with PPFDs of 30, 15, and 10μmol m−2 s−1, respectively (Experiment 1). The daily light integral was 9.50mol m−2 in all treatments. In Experiment 1, leaf elongation was promoted as the duration of red-light irradiation decreased and the duration of blue-light mono-irradiation increased. The maximum shoot dry weight was observed under the B+8R treatment. Growth was likely promoted by the expansion of the light-receptive area caused by moderate leaf elongation without tilting. In Experiment 2, young cos lettuce seedlings were grown as for Experiment 1, but blue- and red-light irradiation intensities were reversed (R+0B, R+8B, R+16B, and R+24B). Leaf elongation was promoted by the absence of blue-light irradiation (R+0B). The leaf surface was increasingly flattened, and the shoot dry weight was enhanced, as the duration of blue-light irradiation increased. Thus, cos lettuce leaf morphology may be manipulated by adjusting each duration of blue-light mono-irradiation, red-light mono-irradiation, and blue- and red-light simultaneous irradiation, which can, in turn, promote cos lettuce growth.

Lettuce Growth Pattern Analysis Using U-Net Pre-Trained with Arabidopsis

To overcome the challenges related to food security, digital farming has been proposed, wherein the status of a plant using various sensors could be determined in real time. The high-throughput phenotyping platform (HTPP) and analysis with deep learning (DL) are increasingly being used but require a lot of resources. For botanists who have no prior knowledge of DL, the image analysis method is relatively easy to use. Hence, we aimed to explore a pre-trained Arabidopsis DL model to extract the projected area (PA) for lettuce growth pattern analysis. The accuracies of the extract PA of the lettuce cultivar “Nul-chung” with a pre-trained model was measured using the Jaccard Index, and the median value was 0.88 and 0.87 in two environments. Moreover, the growth pattern of green lettuce showed reproducible results in the same environment (p < 0.05). The pre-trained model successfully extracted the time-series PA of lettuce under two lighting conditions (p < 0.05), showing the potential application of a pre-trained DL model of target species in the study of traits in non-target species under various environmental conditions. Botanists and farmers would benefit from fewer challenges when applying up-to-date DL in crop analysis when few resources are available for image analysis of a target crop.